Data Science

Process management is an enormous field that is divided into various sections. It is all about dealing with the crucial aspects of creating, managing, and implementing multiple architectures by minimizing all the obstacles in the process. Among the essential constituents of process management; comes process mining, which can be seen as a blend of various technologies that help complete a project successfully, saving time and energy.

The primary purpose of process mining is to inspect the way processes work, how they originate, the hurdles that appear, and the techniques to minimize the barriers and upsets for a process' improvisation. Keep reading this blog as we will shed light on process mining, how it works, its benefits, and will compare it with RPA:

What is Process Mining?

Process mining can be defined as a process to examine and to keep an eye on the processes’ progress. Earlier process mining was done by conducting various workshops and consulting individuals to draw a picture of the processes.Since everything has modernized with time, so have the process mining techniques as they have evolved from the traditional practices to more advanced and automated ways. These days, process mining is conducted by analyzing the already available data and displaying a process based on the information.Process mining can be implemented on any process if the required data is available or stored in a system. It has made the visualization of your processes more effortless than ever before. You can use process mining to conduct an in-depth analysis, compare different strategies, monitor tasks, set benchmarks, and work on the data for improving processes.

Process Mining Benefits

Process Mining brings a series of benefits with its implementation since it is a solid upgrade from the weary traditional methods for analyzing data and project management. Let's take a look at the salient advantages of process mining in this section:

1)   Process Improvements & Error Detection

All the activities that are conducted for the initiation, processing and finalization of processes are shown by the process flow. A process flow includes all the anomalies, divergences, and missed steps to help you conclude better results. A user can track the processes and check if anything goes against your target model, check for improvements, and make the needed amendments right on time. Not only that, but a process flow also informs you about the better methods, and you may implement them for improved results.

2)   Timely Improvements

Process mining makes it quick and a lot simpler to get the results, so it also has the nature to accept the real-time changes in the market.It also makes the process of setting goals easier, which helps in developing an all-encompassing, assertive, and long-term optimization strategy that's also flexible and welcomes new changes without any problems.

3)   Clarity

Since many processes are running in parallel, it is impossible to monitor each project following a traditional approach. Process mining provides more clarity in process management, as it shows the progress of all processes, whether running alone or in parallel to other processes.Earlier, the visibility was quite tricky since there was a lot of paperwork involved, and with bigger projects, it was nearly impossible to track every process. Gone are the days when you had to guess if a process was failing or successfully running; with process mining, you get a clear picture of the progress of all processes.

4)   Quick Results

Since process mining follows the latest approaches for optimization, it dramatically increases the pace of results. Rather than spending hours on paperwork and analysis, mining does your job in a matter of seconds.

5)   Easy Monitoring

Process mining displays all your processes in great detail so that you can bring about changes at any phase to improve your processes. It allows you to either enhance the whole process or just work on the snippets of a process. All this helps you in developing a better strategy. On top of that, process mining also allows you to check how your optimizations are affecting your processes and change the strategy at any point for better results.

Process Mining and Robotic Process Automation (RPA)

Process mining has been used effectively to analyze the current state of business process performance, identify areas of improvement, and assess the results of process improvements. With process mining, you get a clear, data-driven picture of how well a process performs. The ability to see issues and solutions clearly will intrigue people working with process management. It will strengthen a company's commitment to making decisions based on data. Some businesses have already recognized process mining as a significant step in implementing RPA with better results. Many upcoming solutions will use a fusion of process mining, robotic process automation, and machine learning for best results.

How Do Process Mining and RPA Compare Against Each Other?

RPA handles all the tasks that are performed on a repeated basis, as it automates all those repetitive tasks to be done by robots in a faster and more efficient way. The RPA bots are handled via an application, and they imitate all the human actions that include regular tasks like adding, editing, removing, sorting the data, and much more. Unlike RPA, which is a solution or a tool, process mining is more like a methodology, intending to turn data into useful information and take appropriate actions. In order to digitize and automate business processes, businesses use process mining to analyze event log data for trends, correlations, and precise details about how a process develops. The new insights obtained from process mining can be used to eliminate corrupt data, efficiently allocate resources, and respond to any changes rapidly. RPA automates business processes while process mining solutions help in the CRMs and ERP systems. Despite the fact that RPA and process mining are polar opposites, they work brilliantly together.

Benefits of Using Process Mining and RPA Together

Process mining and RPA are both powerful technologies but are lethal when they come together. They help your business in the following ways:

• Process mining and RPA complement each other as the former ads system event logs to gain insight into business processes, and the latter automates these processes.
• When used together, process mining improves the efficacy of bot operations and their deployment, which results in better results.
• Process mining increases the success rate of RPA projects.

Process Mining + RPA = Hyper-automation

Hyper-automation refers to the practice of automating everything that can be automated in a business. Think of it as a combination of RPA and process mining. Using AI, ML, and other technologies, organizations adopting hyper-automation aspire to streamline operations across their business so that they can function without human involvement. Businesses implementing hyper-automation will find that process mining does much more than just identify areas for automation. The system also establishes links between different IT systems and reveals previously hidden workloads. People mostly get confused figuring out the difference between automation and hyper-automation, so let’s clear how they differ once and for all. Automation refers to the accomplishment of a routine task without the involvement of a human being. It's more common on a micro level, with solutions tailored to specific problems. Hyper-automation pertains to using various automation tools for large-scale automation projects. The tools used in process mining also produce data ready for machine consumption, allowing for the automated process's robotic automation. Hyper-automation can benefit an organization in myriad ways, including:

•      Helping your workforce with teaching the right skillset.
•      Improving your business via intelligence using Artificial Language and Machine Learning.
•      Providing information on automating your ROI so that your business can continue to grow.
•      Optimizing any business process using the latest technologies.

Process Mining and RPA Costs

Sure, process mining and RPA are not cheap. You might get scared a bit when looking at the costs of RPA and process mining. But here's the thing. You need to calculate the value they are providing against their price. Calculate how much labor costs you will be saving with their implementation. If we take into account the amounts that these tools help us save, then their amounts will look like nothing. Keep in mind that these tools aren't built for struggling small businesses or individuals; but rather for enterprises. Using RPA bots as a quick fix instead of tighter data integrations and improved ETL processes is quite common these days. RPA bots often hide technical debt by sitting on top of fragmented software landscapes. Businesses can benefit from more intelligent automation. However, many organizations are better off unraveling their technical debt to enable simple data integrations and automation within their existing software rather than embarking on RPA expeditions.

Final Thoughts

In this technological era of development, anyone abstaining from the latest technological advancements will find themselves getting stuck in the web of problems.All successful businesses are embracing process mining and robotic process automation to help them grow faster than ever. The combination of both RPA and process mining is lethal, so if you can afford it, then go for it.

Did you know that most of the data warehouse projects fail due to wrong planning and platform selection? That said, many businesses skip the step of selecting the right cloud data warehouse and proceed directly with the other tasks. Speaking of cloud data warehouse platform providers, both Snowflake and Google BigQuery are among the most sought-after options and offer top-notch features to facilitate organizations.

Our blog compares both warehouse solution providers in detail as we dig into the details of these data warehouse giants to help you make the right selection.

Understanding Snowflake and BigQuery

The thought of setting up a data warehouse earlier implied emptying your pockets on overly expensive hardware solutions to run in your data centers. However, the advent of cloud data warehouse solutions has halted these scary means and has provided inexpensive and finer solutions like Snowflake and BigQuery. Before we jump into the comparison, let us first give a brief overview of Snowflake and BigQuery for people new to these names.

If you are already acquainted with these data warehousing solution providers, you may skip this part and directly move towards the comparison part.

What is Snowflake?

Snowflake is a fully managed cloud data warehouse that is offered as a SaaS and DaaS to users worldwide.What separates Snowflake from its competitors is its architecture, which lets the users scale and pay for the computations and storage separately.You can deploy Snowflake to any of the following cloud providers:

•      Microsoft Azure
•      Amazon Web Services (AWS)
•      Google Cloud Storage (GCS)

Businesses and organizations that don't want to get into the nitty-gritty of handling their in-house servers and hiring multiple people for the system's installation, configuration, and management can get a solution like Snowflake.With Snowflake, you don't have to deal with any back-end work, as you can deploy Snowflake instances on any of their preferred cloud providers.

What is BigQuery?

Google BigQuery, like Snowflake, is also a fully managed cloud data warehouse solution that is popular for its speed and responsiveness. As the name suggests, BigQuery is presented by Google and uses its Dremel technology, and is presented as a read-only data solution. BigQuery's tree-like architecture is the secret behind its ultra-fast scanning and querying. BigQuery is highly scalable due to the fast deployment cycle, and to put the cherry on top, it is serverless and offers on-demand pricing. Its architecture works on analyzing the used resources. It assures the usage of all available allocated resources so that the organizations can deploy them without needing to scale out. BigQuery is also a big-data solution thanks to its ability to collect high volumes of data and analyze and organize it fastly. Businesses and organizations seeking robust analytical and intelligent solutions can opt for BigQuery, as its algorithm, architecture, and flexible pricing makes it quite handy.

Snowflake vs. BigQuery: Comparison

Now that we have learned about Snowflake and BigQuery, we can jump into their comparison. We will compare both data warehouse solutions in three different departments, i.e., features, performance, and pricing, and lastly will conclude a winner that excels better in these departments.

Snowflake vs. BigQuery: Features

We all fancy solutions that are not just reliable and affordable but are also packed with the best and latest features. We will compare BigQuery and Snowflake in terms of their features' offering in this section and declare a winner in the features department at the end of this section.

Machine Learning

Machine learning sheds light on the algorithms and the data usage to copy the methods by which a process is learned and improvised with time, thanks to its complex technology. While the technological world is welcoming artificial intelligence with open arms, it is impossible to forget the importance of machine learning in growing data science solutions. BigQuery pays its homage to machine learning as it lets the users train and deploy the machine learning models using the existing models and improvising them. You can make most of this feature as you no longer are required to export your data or use a tool to carry data exportation tasks. Contrarily Snowflake solely depends on the external tools for machine learning. Even though using these external tools, you can carry out the tasks in a proficient manner; this solution is certainly not as coherent and handy as the one that BigQuery provides. Furthermore, if you combine BigQuery with Looker, you can get the best machine learning results.

‍Winner: BigQuery

Security

Security is one factor that, if compromised, can annihilate any business or organization regardless of its size. Any business or firm dealing with confidential data should only opt for the cloud data warehouse solution that provides the most robust security. Thankfully, both our competitors BigQuery, and Snowflake are strong contenders in the security domain. Snowflake and BigQuery both use Advanced Encryption Standard on the data and support customer-managed keys. That said, both are dependent on the roles to offer access to their resources. Snowflake provides the SOC 1 Type II, SOC 2 Type II, PCI DSS, and HIPAA compliance, and offers strong security features to safeguard your precious data from intruders. Other security features include access control, multi-factor authentication, etc.

Don't want specific IP addresses to access your data? Snowflake lets you choose a list of IP addresses that you can whitelist, and any user with a different IP address from the list won't be able to enter the system. You can also blacklist IP addresses and use its automatic data encryption feature to guard your data further. On the other hand, BigQuery also focuses on security and follows modern methods to ensure the best security protocols. As BigQuery is a cloud solution offered by Google, it encrypts all your data automatically regardless of it being at rest or in transit. What more would one want?Like Snowflake, BigQuery also meets the PCI DSS and HIPAA compliance standards. Moreover, BigQuery allows the admins to manage the user's access to the cloud resources.

‍Winner: Snowflake

Ease of Use

Usability is another factor that everyone must take into consideration while selecting a data warehouse solution. Luckily, Snowflake and BigQuery are pretty user-friendly and built to provide a handy experience. The best thing about BigQuery in terms of user-friendliness is its serverless architecture which does not require the user to get into the technical complexities, as there is no setup required. The user just has to move their data into Google cloud storage, and that's pretty much all that is needed from the user's end. Even though Snowflake isn't serverless, it does not require you to set up the storage and compute, as it separates them both and uses the Snowflake Data Cloud to handle them. That said, you will need to have a cloud provider to back you up, unlike BigQuery that Google Cloud manages. The comparison of BigQuery and Snowflake is quite challenging in this domain, as both go head-to-head on user-friendliness, with BigQuery having a slight edge over Snowflake.

‍Winner: BigQuery

Maintenance

Most organizations are reluctant to pay high prices while spending on cloud warehouse solution providers and to save a few bucks, opt for inexpensive solutions. Even though they save themselves in the beginning by paying low costs, that strikes back as the cheap solutions often fail or require hefty amounts for their maintenance. The cheap solutions' maintenance is hard on the pockets, but they are also unreliable and insecure. Always go for a well-reputed warehouse solution provider and that does not require heavy maintenance over time. Unlike other solutions, Snowflake and BigQuery do not require massive administration costs and are pretty easily maintained. BigQuery facilitates its users by transferring the unused data to long-term storage automatically, saving high costs. If any element within BigQuery has not been used for over three months, it will automatically move it to long-term storage. Since both Snowflake and BigQuery are automated systems, they don't require much supervision. Both don't need human intervention in query optimization and instance adjustment. They also allow the admins to manage the user roles and permissions to ensure secure access. As data scales up with the passing time and the queries get more complex, both Snowflex and BigQuery automatically scale them to meet the requirements.

‍Winner: Tie

Scalability

Since Snowflake separates the compute and storage resources, users can independently scale them as per their requirements. It also considers automated performance tuning and workload monitoring to enhance the query times when the platform is running. On the other hand, BigQuery tackles scalability differently. As it is serverless, it automatically facilitates extra compute resources or as per the on-time requirements to deal with big data. This ability makes it easier for BigQuery to process millions of gigabytes of data in a couple of minutes. Winner: BigQueryCombining our results in the domain of the features, we see BigQuery as the clear winner. Let’s see what we get in the performance and pricing domains.

Snowflake vs. BigQuery: Performance

The auto-scaling ability of both Snowflake and BigQuery allows them to sustain incredible amounts of load and deliver excellent performance. Both deliver almost similar performances for many tasks and require very little maintenance.If your business or organization deals with massive volumes of data and has high idle times, then BigQuery is a better option.On the flip slide, if your usage is relatively steady dealing with the data and queries, then Snowflake would be a more economical option, as it will let you resolve more queries into your compute times.Last year, Fivetran worked on a benchmark report that compared both our contenders, Snowflake and BigQuery. They ran 99 TPC-DS queries of different complexities and ran each query only once to abstain from caching the previous results.Fivetran generated a 1TB TPC data set having 24 tables in a snowflake schema, and they also decided to avoid fine-tuning the data warehouses and delivered the following results.

•      Snowflake gave an average query time of 8.21 seconds.
•      BigQuery gave an average query time of 11.18 seconds.

The results concluded that Snowflake is faster than BigQuery in terms of performance.Winner: Snowflake

Snowflake vs. BigQuery: Pricing

The last and probably the most important factor of our Snowflake and BigQuery comparison is their pricing plans and affordability. As mentioned in the upper sections, they both provide separate storage and compute, but we didn't discuss the computing costs.Interestingly, both Snowflake and BigQuery have different ways to calculate computing costs. While Snowflake calculates the prices based on time usage, BigQuery focuses on the data amount spent in scanning the queries.Let's discover more about their pricing plans:

Snowflake Pricing

Snowflake offers you a monthly amount of $23 per terabyte if you opt for upfront payment; else, you can also choose their $40 per terabyte (monthly average) if you choose their on-demand plan.Snowflake has separate pricing plans for the compute. It has divided its service into seven different tiers for data warehouses. You can avail of it for as low as an amount of $0.00056 per second.Visit Snowflake's official website to check out its pricing plans in detail.

BigQuery Pricing

With BigQuery, you have the following two payment options with storage:

•      A flat rate of $20 per terabyte (monthly) for uncompressed and active storage.
•      Pay $10 per terabyte (monthly) for long-term storage.

Note: Google offers the first 10 GBs of monthly storage for free. If we look at BigQuery's compute pricing plans, it charges you the on-demand queries for $5 per terabyte. It also gives you the option to buy 500 slots at $10,000 (monthly flat rate) or $8500 (annual flat rate). Note: Google offers the first 1TB of monthly storage for free. Visit BigQuery’s official website to check out its pricing plans in detail. Users seeking on-demand and pre-purchasing pricing plans as per their data needs and spending on a per-second basis should opt for Snowflake. While users looking for a charge per usage basis should go for BigQuery. BigQuery's web console also provides an estimated number of scanned data before the run to help you get an idea of the total cost. Winner: BigQuery

Final Decision: Snowflake vs BigQuery?

We compared both Snowflake vs BigQuery on various factors. While we have concluded a winner from our findings and personal opinions, we leave the final decision to you to pick up the better option.As per our comparison, BigQuery won in the features and pricing department, while Snowflake won in the performance department. While both are neck-to-neck competitors in all domains, our results conclude BigQuery as the better data warehouse solution.

If you are a data science enthusiast, then your curiosity about the life cycle of data science projects is quite understandable. Knowing such important processes is essential in developing a better understanding of the overall subject. Data Science has come a long way since it was first introduced and is constantly evolving with time. Data Science works on data as the main subject, and all the studies and researches are conducted to derive more from the available data.

To feed all the inquisitive data scientists with the information they need, we have covered the life cycle of data science projects in great detail in this blog. Keep reading to find out about the steps involved in the life cycle.

What is a Data Science Life Cycle?

You may think of a project's data science life cycle as recurring stages that are required to be completed, and its deliverance to the client is dependent upon the successful completion of each step. Even though the life cycle contains similar steps, each company or organization follows a different approach. Data science projects require collaboration and are unsuccessful without a proper team effort. Different deployment and development teams come together on one platform to work on the given data and study it to derive various solutions and their analysis.

The data science life cycle encompasses all stages of data, from the moment it is obtained for research to when it is distributed and reused. The data lifecycle begins when a researcher or analyst comes forward with an idea or a concept. Once the concept for the study is accepted, then begins the process of collecting the relevant data. Data is stored after it is collected by the research team and is made available to other researchers to be used in the future. Once data has reached the distribution point, it is stored where other researchers can access it.

Why Do We Need Data Science?

Not too long ago, we didn't have enormous quantities of data, and it was readily available in a well-structured form to be easily stored in documents and sheets. However, as the data size increased with time, keeping big data and maintaining it became quite an obstacle and required extra effort. Companies dealing with gigantic data sizes can not rely on Excel sheets or a few folders for their storage; they want an improvised solution.

The need for maintaining and analyzing the vast data amounts gave birth to the idea of Data Science, which solves this problem using its complex algorithm, and robust technology. Data science is necessary to process, analyze, and interpret data safely. It helps the organizations better plan, set realistic goals, get a proper understanding of their current data, and focus on growth. The prominence of data science in the past few years has caused a spike in demand for data scientists throughout the world.

Five Stages of the Data Science Life Cycle

Data Science has come a long way since it emerged almost three decades back. Problems like these require a proper set of steps to tackle the issues correctly. Over the years, data scientists have developed a life cycle for data science projects and adhere to the process while working on data science problems. We all love shortcuts without realizing the damage they can provide. Some organizations prefer to jump towards the methods to solve the problem directly, without going through the proper steps. Sometimes these shortcuts solve your problem, but they almost always prove detrimental in the long run. Following the data science, life cycle steps ensure that the problem is being tackled to its core and provide a much better and more detailed analysis. The data science life cycle is divided into five steps, and we have listed the steps below along with their brief overview.

1. Business Understanding

Before you start working on your client's model, learn about the obstacles they're facing to apprehend their needs. Most people skip the pivotal step of understanding the actual problem and directly jump to the next phase and often end up in a failure or not fulfilling their client's demands. Understanding your client's issues is essential to building an efficient business model. Conduct thorough research to learn more about your client's business and ask them their expectations. Don't be reluctant to spend your time on the understanding phase, take help from the relevant people, conduct multiple meetings, and do whatever is required until you have understood the existing problems and issues. Business analysts are normally given the duty to collect customer information and send it to the data scientists team for analysis. Identifying and analyzing the objectives with the utmost accuracy is crucial, as even a tiny mistake can result in a project's failure.

2. Data Collection

Data science is non-existent without data, so collecting data is one of the most crucial life cycle stages for data science projects. When you have clearly understood your client's requirements and have analyzed the existing system and its problems, it's time to map down how to collect the required data. Consult your client, conduct team meetings, and do proper research to develop your data requirements and the methods to obtain them. Seasoned data scientists have their own ways to source, collect, and extract data to meet clients' expectations. Usually, the data analyst team is assigned to obtain the data, and they either source data via web scraping or with third-party APIs.

3. Data Preparation

Data is primarily obtained in a raw form, and the proper alignment of the scattered form is required to perceive it as information. It has to go through a cleaning process and be arranged in a proper format to be understood and used in an analytical step. The process of refining data is called data cleaning and is the core of data preparation. Once the data is presented in a structured form and is free from useless information, it helps you devise a strategy much better. Multiple sources are used for extraction during the data collection process, but they have to be compiled together in an understandable form for proper analysis. When data is typically acquired from various places, it sometimes is incomplete or has many gaps to make any sense for analysis. Data scientists have designed multiple methods to extract the missing piece and help structure the data. They also take the help of the exploratory data analysis (EDA), which identifies the important process of conducting initial research on data to find patterns, detect anomalies, and test hypotheses using statistical results and graphical representations.

4. Data Modelling

Data modeling is perhaps the core of the data science life cycle. In this step, the data scientist has to choose the appropriate model depending upon the problem. Using structured data as input, a model then outputs the desired result. Once the model family has been decided, the data scientist has to choose the right algorithm depending upon the model family that would give the best results and implements them effectively. Data scientists use the modeling stage to find data patterns and derive insights. The modeling stage marks the start of the entire data science system's analysis and allows you to measure the accuracy and relevance of your data.

5. Model Deployment

The final step of the life cycle of a data science project is the deployment phase. The step focuses on developing a delivery procedure to deliver the model to the users or a machine. The complexity of the deployment step depends upon the nature of the project. At times, it would require you to display your model output, and sometimes it would need you to scale your model to the cloud to thousands of users. Normally this step is taken care of by the application developers, SQA team, data engineers, machine engineers, and cloud engineers.

FAQs

Q. What is the life cycle of a data science project?

Ans: The life cycle of a data science project comprises the five stages that lead to the project's completion. The five stages are listed as follows:

1. Business Understanding
2. Data Collection
3. Data Preparation
4. Data Modelling
5. Model Deployment

Q. What is the first step in the data science life cycle?

Ans: The first step in the data science life cycle is business understanding. Data scientists should start with understanding their client's requirements first before jumping on to the next steps.

Q. What are the final stages of data science methodology?

Ans: The final stages of data science methodology include structuring the data, choosing the appropriate model, and then deploying the model.

Final Thoughts

Data science is the field that revolves over statistical methods, innovative technologies, and scientific thinking. We have tried to cover the data science life cycle in this blog and have tried to explain every step concisely and clearly. Still, if you are unclear about anything, don't hesitate to comment, and we will answer your queries ASAP!

Data is the new commodity in today’s tech-driven world. With the increasing dependencies of the world on data, it proves to be the fundamental asset for small and mid-sized businesses to the big enterprises. Dependence upon data increased as enterprises started tracking records of their data for analytics and decision-making objectives.

The international big data market is predicted to grow to 103 billion U.S. dollars by 2027 with a share of 45 percent, and the software segment will occupy a notable big data market volume by 2027.

However, to keep a managed record of these overwhelming volumes of data, a proper data warehousing solution must be adapted. A data warehouse helps users in the accessibility, integrations, and more critically on the security aspect. This blog post focuses on the discussion of state-of-the-art data warehousing solutions and their detailed comparison, i.e., ‍

Snowflake vs. Redshift. To understand the differences between Snowflake and Redshift, we will go through some key aspects of both platforms.

What is Redshift?

Redshift can be considered a highly managed, cloud-based data warehouse service seamlessly integrated with various business intelligence (BI) tools. The only thing left is Extract, Transform, Load - ETL process to load data into the warehouse and start making informed business decisions.Amazon makes it easier for you to initiate with a few hundred gigabytes of data and scale up or down the capacity as per your requirements. It enables businesses to enjoy the perks of their data to get fruitful business insights about themselves or their customers.

If you want to launch your cloud warehouse, you have to launch a set of nodes known as a Redshift cluster. Once you have triggered the cluster, data sets can be loaded to run different data analysis operations.  Irrespective of the size of your data set, you can leverage upon fast query performance by using the same SQL-based tools and BI utilities.

What is Snowflake?

Like Redshift, Snowflake is another powerful and renowned relational database management system -RDBMS. It’s introduced as an analytic data warehouse to support structured and semi-structured data that follows a Software-as-a-Service (SaaS) infrastructure.

This means it’s not set up on an existing database or a big data platform (like Hadoop). Instead, Snowflake serves as an SQL database engine with a unique infrastructure specifically developed to offer cloud services.This data and analytics solution is also quick, interactive, and offers more scalability than conventional data warehouses.

Redshift vs Snowflake - Comparison

If you have used both Redshift ETL and Snowflake ETL, you’ll probably be aware of several similarities between the two platforms. However, there are additional unique capabilities and other functionalities that each platform offers differently.Suppose you’re gearing up to run your data analytics operations entirely on the cloud. In that case, the similarities between these two state-of-the-art cloud data warehousing platforms are far more than their differences.

Snowflake offers cloud-based storage and analytics in the form of the Snowflake Scalable Data Warehouse. In this case, users can analyze and store data on cloud media.Next, data will be stored in Amazon S3. If you’re using Snowflake ETL, you can benefit from the public cloud environment without any need to integrate utilities like Hadoop.These cloud warehouse infrastructures are powerful and provide some unique features for handling overwhelming amounts of data.To choose a suitable solution for your company, one must compare integrations, features, maintenance, security, and costs.

Snowflake vs Redshift: Integration and Performance

If your business is already based on AWS, then Redshift might seem like the smart choice. However, you can also opt for Snowflake on the AWS Marketplace with on-demand utilities. If you’re already using AWS services like Athena, Database Migration Service (DMS), DynamoDB, CloudWatch, Kinesis Data Firehose, etc., Redshift shows promising compatibility with all these extensions and utilities. However, if you’re planning to use Snowflake, you need to note that it doesn’t support the same integrations as Redshift. This, in turn, will make it complex to integrate the data warehouse with services like Athena and Glue. However, Snowflake is compatible with other platforms like Apache Spark, IBM Cognos, Qlik, Tableau, etc. As a result, you can conclude that both platforms are just about even equally useful and workable. While Redshift is the more defined solution, Snowflake has completed notable miles over the last couple of years.

Snowflake vs Redshift: Database Features

Snowflake makes it simpler to share data between different accounts. So if you want to share data, for instance, with your customers, you can share it without any need to copy any of the data.This is a very smart approach to working with third-party data. But at the moment, Redshift doesn’t provide such functionality. Redshift is not compatible with semi-structured data types like Array, Object, and Variant. But Snowflake is.When it comes to handling String data types, Redshift Varchar limits data types to 65535 characters. You also have to opt from the column length ahead.On the other hand, the String range in Snowflake is limited to 16MB, and the default size is the maximum String size. As a result, you don’t have to know the String size at the start of the exercise.

Snowflake vs Redshift: Maintenance

With Amazon’s Redshift, users are encouraged to look at the same cluster and compete over on-desk resources. You have to utilize WLM queues to handle it, and it can be much complex if you consider the complex set of rules that must be acknowledged and managed. Snowflake is free from this trouble. You can easily initiate different data warehouses (of various sizes) to look at the same data without any need to copy it, and multiple copies of the same data can be distributed to different users and tasks in the simplest way possible. If we talk about Vacuuming and Analyzing the tables on regular basic copying, Snowflake ensures a turnkey solution. With Redshift, it can become troublesome as it can be an overwhelming task to scale up or down. Redshift Resize operations can also become extremely expensive suddenly and lead to notable downtime. This is not the case with Snowflake due to the separate compute and storage domains, and you don’t have to copy data to scale up or down. You can just switch data compute capacity whenever required.  

Snowflake vs Redshift: Security

For any big data project, security is the core of all aspects. However, it can be difficult to maintain consistency as every new data source can likely make your cloud vulnerable to evolving threats. It can generate a gap between the data generated and the data that’s being secured. When it comes to security measures, it’s not a race between Snowflake and Redshift, as both platforms provide enhanced security. However, Redshift also provides tools and utilities to handle Access management, Amazon Virtual Private Cloud, Cluster encryption, Cluster security groups, Data in transition, Load data encryption, Log-in credentials, and Secured Socket List - SSL connections. Snowflake also provides similar tools and utilities to incorporate security and regulatory compliance. But you have to be conscious while the edition as features aren’t available across all its variants.

Snowflake vs Redshift: Costs

Both Snowflake ETL and Redshift ETL have very contrasting pricing structures. If you take a deeper look, you’ll get to know that Redshift is less expensive when it comes to on-demand pricing. Both solutions provide 30% to 70% discounts for businesses who choose prepaid plans.With a one-year or three-year Reserved Instance (RI) price model, you can access additional features that you can miss out on a standard on-demand pricing model.

Redshift charges customers based on a per-hour per-node basis, and you can calculate your monthly billing amount using the following formula:

‍Redshift Monthly Cost = [Price Per Hour] x [Cluster Size] x [Hours per Month]

‍Snowflake’s price is heavily dependent on your monthly usage. This is because each bill is generated at hour granularity for each virtual data warehouse. In addition to that, data storage costs are also separate from computational costs.For instance, storage costs on Snowflake can start at an average compressed amount at a fixed rate of $23 per terabyte. It will be summed up daily and billed each month. But compute costs will be around $0.00056 per second or credit on Snowflake’s On-Demand Standard Edition.However, it can quickly become troublesome because Snowflake offers seven tiers of computational warehouses, with the most basic cluster costing one credit or $2 per hour.

The resultant bill is likely to double as you go up a level.In simple words, if you want to play safe, then Redshift is a less expensive option for you as compared to Snowflake on-demand pricing. But to leverage from notable savings, you’ll have to register for their one or three-year RI.

Snowflake vs Redshift: Pros & Cons

Amazon Redshift Pros

• Amazon Redshift is very interactive user-friendly.
• It also requires less administration and control. For instance, all you have to do is create a cluster, choose a type of instance, and then manage to scale.
• It can be easily integrated with a variety of AWS services
• If your data is stored on Amazon S3, Spectrum can easily run difficult queries. You just have to enable scaling of the compute and storage independently.
• It’s highly favorable for aggregating/denormalizing data in a reporting environment.
• It provides very fast query execution for analytics and enables concurrent analysis.
• It provides a variety of data output formats, including JSON.
• Developers with an SQL background can enjoy the perks of PostgreSQL syntax and work with the data feasibly.
• On-demand reserved instance price structure covers both compute power and data storage, per hour and per node.
• In addition to improved database security capabilities, Amazon also has a wide array of integrated compliance models.
• Offers safe, simple, and reliable backups options

Amazon Redshift Cons

• Not suitable for transactional systems.
• Sometimes you have to roll back to an old version of Redshift while you wait for AWS to launch a new service pack.
• Amazon Redshift Spectrum will cost extra, based on the bytes scanned.
• Redshift lacks modern features and data types.
• There can be complexities with hanging queries in external tables.
• To ensure the integrity of transformed tables, you’ll also have to rely on passive mediums.

Snowflake Pros

• Snowflake is suitable for enterprise-level businesses that operate mainly on the cloud.
• This data warehouse platform is extremely user-friendly and compatible with most other services.
• Its SQL interface is highly intuitive.
• Integration is simple because Snowflake itself is a cloud-based data warehouse.
• Easy to adapt and launch.
• Supports a wide array of third-party services and utilities.
• SaaS can be integrated with cloud services, data storage, and query processing.
• Data storage and compute pricing will be based on different tier and cloud providers and charged separately.
• Enable secure views and secure user-defined functions.
• Account-to-account data transfer can be done via database tables.
• Integrates easily with Amazon AWS.

Snowflake Cons

• Snowflake is not recommended if you’re running a business using on-premise infrastructure that doesn’t easily support cloud services.
• A minute’s worth of Snowflake credits will also be consumed whenever you enter a virtual warehouse but charged by the second after that.
• There’s much room for improvement as Snowflake’s SQL editor needs to be upgraded to handle automated functions.

Conclusion

The choice between Redshift and Snowflake depends upon your usage and specific business requirements. For instance, if your organization manages overwhelming workloads ranging from the millions to billions, the obvious option here is Redshift. While their model is cost-effective, companies also can reduce their expenses by opting for query speeds at a lower price value for daily active clusters. As Redshift is a renowned Amazon product, there’s also comprehensive documentation and support that can help your employees deal with any potential problem. However, the bottom line is that your data warehouse decision has to be made based on your daily usage and the amount of data you will deal with.

ETL stands for Extract, Transform, and Load. With the emergence of modern cloud technologies, many businesses are shifting their data from conventional on-premise systems to cloud environments by using ETL utilities. They used to leverage conventional RDBMS, which lacked performance and scalability. To achieve excellence in performance, scalability, reliability, and recovery, organizations are shifting to cloud technologies such as Amazon Web Services, Google cloud platform, Azure, private clouds, etc.

In a general ETL scenario, ETL is a streamlined process that fetches data from conventional sources by using connectors for analysis, transforms this data by applying different methodologies like filter, aggregation, ranking, business transformation, etc. that serves business needs, and then loads onto the destination systems which is generally a data warehouse.  The illustration below can give you a clear picture of how ETL works.

Approach towards ETL in Snowflake

The journey begins with the Snowipe, an automated utility developed using Amazon SQS and other Amazon Web Services (AWS) solutions that asynchronously listen for upcoming data as it reaches Amazon Simple Storage Service (Amazon S3) and consistently loads it into Snowflake However, Snowpipe alone does not contribute to the phase “E” (Extraction) of ELT, as only the “COPY INTO” command is allowed in a Snowpipe.

In other words, we can achieve the following objectives using Snowpipe:

• Loading data files in different formats such as CSV, JSON, XML, Parquet, and ORC
• Adopting and improving the source database for better synchronization, such as stripping outer array for JSON and stripping outer element for XML
• Altering column names
• Altering column orders
• Omitting columns
• Parsing of data/time string into data/time object

Snowpipe is not capable enough to eliminate all problems that one can face while building a data pipeline. Therefore, for the following three reasons, Streams and Tasks are required for the rest of the process:

1. Snowflake does not support data transformations such as numbers calculation and string concatenation.
2. The data source is not in a typical 3N normalized form, so it must be loaded into multiple tables based on certain relations.
3. The ELT jobs may not be restricted to create table joins but also involve more complex requirements such as SCD (Slowly Changing Dimension).

Roadmap to Build ETL Pipeline

There are multiple ways to build the ETL pipeline. You can either create shell scripts and orchestrate using crontab, or utilize the ETL tools available to develop a customized ETL pipeline. ETL pipelines are mainly classified into two types are Batch processing and Stream processing. Let’s discuss how you can create a pipeline for batch and stream data.

Build ETL Pipeline with Batch Processing

The data is processed in batches from the source database to the destination data warehouses in a conventional ETL infrastructure. There are different tools that you can use to create ETL pipelines for your batch processing. Below are the detailed steps that you need to go through while building an ETL pipeline for batch processing :

• Step 1. Create reference data: Reference data possess the static references or permitted values that your data may involve. You need the reference data while transforming the data from source to destination. However, it is an optional step and can be excluded if you want to omit transformation (as that of an ELT process).

• Step 2. Connectors to Extract data from sources: To build the connection and extract the data from the source, you need the connectors or the defined toolset that establish the connection. The data can be from a multitude of sources and formats like API, RDBMS, XML, JSON, CSV, and any other file formats. You need to fetch all diverse data entities and convert them into a single format for further processing.

• Step 3. Validate data: After fetching or extracting the data, it is crucial to validate the data to ensure it is in the expected range and omit it. For instance, you need to extract the data for the past seven days, and you will filter out the data that will contain records older than seven days.

• Step 4. Transform data: Upon validation, further data makeup includes de-duplication of the data, cleansing, standardization, business rule application, data integrity check, aggregations, and much more.

• Step 5. Stage data: This is the phase where you store the transformed data. It is not recommended to load transformed data directly into the destination warehouse. Instead, the phase allows you to roll back your operations easily if something goes against the criteria. The staging phase also provides Dashboard and Audit Reports for analysis, diagnosis, or regulatory compliance.

• Step 6. Load to data warehouse: From the staging phase, the data is pushed to destination data warehouses. You can either opt to overwrite the existing information or add new data with the existing record whenever the ETL pipeline loads a new batch.

• Step 7. Scheduling: This is the last and most crucial phase of streamlining your ETL pipeline. You can choose the schedule to refresh and load new data based on daily, weekly, monthly, or any custom time frame. The data loaded with the schedules can include a timestamp to identify the load date, making it easier to roll back any information and check the life of available information. Scheduling and task dependencies have to be done critically to refrain from facing any memory and performance issues.

Build ETL Pipeline with Real-time Stream Processing

Many modern sources such as social media, eCommerce platforms, etc., produce real-time data that requires constant transformations as it appears. You cannot perform ETL in the same way as you do in batch processing as it requires you to perform ETL on the streams of the data by cleaning and transforming the data while it is in the transition phase to the destination systems. Several real-time stream processing tools are available in the market, such as Apache Storm, AWS Kinesis, Apache Kafka, etc. The below illustration elaborates the ETL pipeline built on the renowned and frequently used Kafka.

To create a stream processing ETL pipeline with Kafka, you have to:

• Step 1. Data Extraction:

The first step that you need to do is extract data from the source databases to Kafka using the Confluent JDBC connector or by writing custom codes that fetch each record from the source and then shift it into Kafka topic. Kafka automatically fetches the data whenever new records are found and pushes it to the topic as an update, making it a real-time data stream.

• Step 2. Pull data from Kafka topics:

The ETL application extracts the data from the Kafka topics either in JSON or in AVRO format. It is then deserialized to perform transformations by creating Kstreams. Deserialization in computing is the conversion of a string into an object.

• Step 3. Transform data:

Once you fetch the data from Kafka topics, you can perform the transformation on KStream objects with the help of Spark, Java, Python, or any other programming language. The Kafka streams handle one record and generate one or more outputs depending upon the transformation design.

• Step 4. Load data to other systems: The ETL application loads the streams into destination warehouses or data lakes after the transformation.

Conclusion

We can create an ETL pipeline using Snowflake to continuously shift data from the source to the destination data lake or warehouse. Often, raw data is first loaded temporarily into a staging table used as an interim container and then transformed with SQL queries before it is loaded into the destination. In-stream processing, this interim container is replaced by Kafka for deserialization.

There are many buzzwords related to data management; the most recurring ones are data lake and data warehouse. This blog covers the unique features, key differences, and contemporary trends related to these terminologies. Let’s discuss what they offer and how they work.

Data Lake

A data lake is a highly scalable storage space mainly occupied by large volumes of raw data in its primitive form until it is called for a process. Data in lake data comes from various sources that comprise a combination of clustered or organized formats and are stored with a flat architecture in different file sizes. For organizations that need to collect and store a lot of data but do not find it necessary to process and analyze it instantaneously, a data lake serves as an effective repository that provides large storage spaces quickly without any need for data being transformed.

Data Warehouse

Traditional data warehouses collect and manage data for further usage in a more structured ecosystem. It performs data to information transition and provides meaningful business insights. Businesses that use data warehouses learn and analyze from their data to perform data-driven management and operational decisions.

Ref: N-ix

Also Read: ETL Pipeline and Data Pipeline – How to create an ETL Process

Differences Between Data Lake & Data Warehouse

Due to the more flexible and scalable nature, data lakes are usually considered complementary solutions to data warehouses. But both technologies have their unique features and limitations.Below are the key differences between a data lake and a data warehouse.

Layout

Raw data is data that waits to be processed for further usage. The main difference between data lakes and data warehouses is their ability to deal with raw or processed data. Data lakes primarily store raw and unprocessed data. On the other hand, data warehouses store processed and refined data.Because of this notable difference, data lakes require a much larger storage capacity than data warehouses. Secondly, raw & unprocessed data is much elastic and can be quickly called for an analysis of any kind, making it ideal for machine learning.The ability to store the raw data comes with the curse of data swamps due to the lack of appropriate quality check measures active onboard. To address this problem, data warehouses, by storing only processed and useful data save too much storage space by eliminating the portion of data that can be considered junk.

Purpose

The purpose of independent and disconnected data pieces in a data lake is not determined. Raw data is being pushed into a data lake, sometimes with predetermined future use and sometimes to store for the sake of nothing. This unfiltered data inflow makes data lakes less organized than its opponent.Since data warehouses only store processed data, all of the data in a data warehouse has been stored for a determined purpose and use within the organization. This means that storage space is not wasted on unidentified or useless data junk.

Users

Data lakes are often difficult to navigate by immature staff with less or no experience dealing with unprocessed data. Raw, unstructured data usually demands the role of a data scientist and specialized tools to transform and translate it for a useful business purpose.Processed data can be represented through bar diagrams, graphs, spreadsheets, tables, etc. This makes it understandable by most employees at a company. As we have discussed earlier, this processed data is handled by data warehouses.

Accessibility

Accessibility directly depends on how easy it is to use and access the whole data repository, not the data within. Data in Data lake architecture is stored unstructured and unconnected, which makes Data Lake easier to access. Secondly, any changes made to the data can be done instantaneously since data lakes have very few limitations and no data connections. But this environment can lead to issues like data redundancy.To overcome the issue of data redundancy, data warehouses are designed to be more structured, protected, and secure. But the strictness of structure and management controls makes data warehouses difficult and costly to manipulate as every intended change without a structured & directional mechanism is considered a violation or breach of management measures and demands expertise to manipulate.

Contemporary & Future Trends

Instead of serving as a single source of the data, the data lake provides an adaptable ecosystem that holds a variety of data, with the ability to evolve in accordance with the open access data libraries. With scalability and flexibility preferred over management and control, the data lake is made to ensure cloud storage’s core values and capabilities.As data consumers refine and analyze data, the patterns and insights they find can be pushed back into the data lake, so they are readily available to other data consumers, thus creating an ocean of data and data analytics that has never been experienced before.

This critical feedback loop makes the data lake better and easy to utilize by data consumers.Data Architecture once only portrays an ideal data warehouse, but now the cloud opens up new windows for short-lived data warehousing. A database or visualization tool is not mandatory with methodologies that can call or retrieve data from Data Lake directly.Both technologies are unique in offering their services as Data Lake is more suitable for implementing business intelligence, and data warehouse houses more managed and structured data. The critical question is not what to use but how to extract meaning and insights from data to drive a directed and fruitful business process. As data volume increases with the every day passing, so is the complexity of dealing with it, whether it stores in a lake or a warehouse.

Conclusion

The data warehouse stands as a logical representation of refined and filtered data that almost all employees in a business can use to make decisions at different levels. Without a data warehouse, decision-makers have to make a blind and slow decision that results in a business model, more vulnerable to error and mistakes.But as the amount of structured and unstructured data increases, businesses need to deploy a data lake to entertain a vast ocean of data. The contents and layout of the data lake can be determined by the nature and size of data that cannot be behold using the mainstream data warehouse.

Using both technologies, the organizations create a Business Intelligence ecosystem, a more logical model that is a data warehouse to process and manage the data with several other data visualization tools and technologies, including a data lake in parallel to increase storage scalability. In this scenario, the data lake and the conventional data warehouse work side by side to deliver fruitful results and work together as components of the larger, integrated, and more connected BI ecosystem, which in turn, add value to the data stock by delivering insights and enabling experts to make precise decisions & predictions, previously impossible.

The Data Science Hierarchy of Needs can be well explained by Data Science Pyramid that focuses on the firm data foundation mandatory to attain good data science stability. The pyramid starts with the raw data itself, which may come from many sources, in different formats, and massive amounts. Data Engineers add the context and layout to turn this data into information. Data Management and Governance ensure coordination and quality before this data reaches the final phase. Reporting and Business Intelligence are equally important as they provide a foundation for insight gathering, where information is collected, categorized, and processed to provide analytical outcomes. Finally, Data Science showcases the summit of data into action, depending upon all the foundational phases while also providing a fresh set of robust statistical methodologies.

The data science pyramid is not necessarily a linear approach, meaning that an organization does not need to attain perfection in each phase before transitioning to the next. Instead, a certain level of expertise is required in each phase before moving ahead, and each consecutive transition to the advanced level informs improvements to previous ones. For instance, an organization with a confident grasp on its Data Management and Governance advance towards Reporting and BI, only to figure out different areas for improving data quality. It is essential to know that the data science pyramid depends on the initial value potential. If a company has not already developed a firm data foundation, it is not rational to jump levels in most cases. Instead, organizations would likely enrich more initial value by improving their fundamental and foundational basis before advancing towards data science maturity. The performance of a statistical model directly depends on the value and purity of information it is trained on. Other primary drivers like significant sources, infrastructure, governance, and dashboards come into frame.

Perspectives in Data Science

To utilize your data completely, you have to consider two different perspectives while looking at and handling any data. First of all, there are two perspectives people hold while looking at the data. Either they can see from the perspective of a developer, data scientist, or Machine Learning Engineer, or they may see it from the lens of a business owner. All of these perspectives and viewpoints are very equally critical in deriving benefits from data. Most engineers look at it from the bottom up. It means they focus on how the data will be collected, stored, accessed, and then analyzed to extract actionable insights and patterns. They primarily focus on the engineering aspect of data science to fetch insight and valuable patterns.

Also Read: 8 Applications of Data Clustering Algorithms

On the other hand, an enterprise owner or business person shows interest in the profits they are likely to gain from the data. They are more interested in the profits they can drive from the data. The best approach to implement a data science pyramid is to merge both perspectives. You need to know how the data is collected, the data roadmap, and the different types of data analytic methodologies to fetch valuable and profitable insight and then how to use these insights to influence your decision-making process and boost profits.

The Data Science Pyramid of Needs

Let’s discuss the hierarchy of needs needed to add value, context, and perspective to the raw data and transform it into valuable insights.

1. Data Acquisition

Data Acquisition focuses on many raw data sources, ranging from various traditional data sources, including ERP systems, Legacy Data Stores, and Operational Systems, to more dynamic and advanced runtime sources such as social media platforms and natural language. Data science has provided immense opportunities and possibilities in data acquisition, as previously seemingly absurd data types can now be used for different purposes using advanced methodologies.

2. Data Engineering

Data Engineering possesses all the activities linked with processing, moving, and storing data. Data Engineering can range from conventional tool-based ETL to custom-built data pipelines, which develop the underlying infrastructure through which data flows and is controlled. It is crucial as it provides the tools and methodologies necessary for the ETL workflows that enable data to move efficiently for advanced processes further up the pyramid.

3. Data Management and Governance

It ensures that intense scrutiny and check mechanisms are being placed on the meta-attributes of data such as data types, cardinality, and value distribution. This phase controls the various activities linked with improving the quality and usability of data by cleaning it and adding useable features. Data Management is a vital middle component because of the algorithms that enable AI and Machine Learning to learn and analyze data.  Therefore, data must be organized, free from errors, up-to-date, and useable.

4. Reporting and Business Intelligence

It includes the tools and methodologies linked with making information readily available to organizations for the analytical processes. It focuses on showcasing information compellingly and understandably to use various decision-making processes; and possesses different data and OLAP data schemas. Reporting and BI add value because it effectively represents your data science outcomes and results to the rest of the organization and non-technical department in the most understandable way possible.  It serves as a medium that connects data science to the primary decision-makers who can then make rational and data-driven decisions to boost the business’s business’s overall performance and profit margin.

5. Data Science

Data Science can be instrumental at the intersection of advanced mathematics, statistics, computer science, and domain expertise. It is an interdisciplinary approach to creating diagnostic, predictive, or contextual insights from massive, complex, and exotic data sources using approved, attentive, and reproducible methodologies.

END WORDS

The overall concept of the pyramid lies in the question of why and how we use data. To turn data into information, then into insight, you need to build massive IT systems to turn raw and seemingly useless and scattered data into organized information to derive actionable insights. Every step you go up the pyramid, you stream or improve some portion of the data, information, or insight process. For instance, data infrastructure & engineering is intended to transform the raw information into something with more context & organization onwards.  The transition from Reporting & BI to Data Science represents the last step of this automation drive.

Also Read: A Basic Guide on Cross-Entropy in Machine Learning

Keep in mind, in the end, if the foundation is weak and based on noisy, incomplete, and unorganized data, the solution will not be optimized. The outcomes could be downright devastating. Instead of jumping steps or avoiding the mandatory internal challenges, ensure the foundation is as strong as possible. By doing so, even if you don’t attain the highest level of the data pyramid, your business will still enjoy the perks of the processed data and analytics for more satisfactory solutions.

Throughout history, perspective in the data domain has experienced multiple transformations. Due to the recent advances made in machine learning, the data management processes of organizations have started to reform like never before. The exponential growth of available and accessible data demands the modern management and handling of immense data assets. The end-to-end routes of data architecture are known as pipelines. Every pipeline possesses one or more sources and target systems to access and manipulate the available data.

In these pipelines, data goes through various stages, including transformation, validation, normalization, etc. People often confuse the ETL Pipeline with Data Pipeline.This blog post is intended to answer two questions.

• What is the difference between the ETL Pipeline with Data Pipeline?
• How to make an ETL Pipeline?

ETL Pipeline

Data ETL Pipelines are architectures that involve certain processes, including the extraction of data from a source, its transformation, and then loading it into the target destination for different purposes like machine learning, statistical modeling, extracting insights, etc. The said target destination could be a data warehouse, data mart, or database.

ETL stands for Extraction, Transformation, and Loading. As the title suggests, the ETL process involves:

• Data integration
• Data warehousing
• Data Transformation

The extraction involves the fetching up of data from different heterogeneous sources. For instance, business systems, applications, sensors, and databanks. The next stage is data transformation that involves converting into a defined and improved format to use by many applications. Last but not the least, the accessible and improvised form of data finally loads into a target destination. The primary objective of building an ETL Pipeline is to employ the right data, make it available for reporting, and store it for instant and handy access. An ETL tool assists businesses and developers to spare time and effort to focus on core business processes. There exists a variety of strategies to build ETL pipelines depending on a businesses’ unique requirements.

ETL Pipeline - Use Case

There are a variety of scenarios where ETL pipelines can be used to deliver faster, superior-quality decisions. Data ETL pipelines are implemented to centralize all data sources and allow businesses to have a consolidated data version. Consider the Customer Resource Management (CRM) department that uses an ETL pipeline to extract customers’ data from multiple touchpoints during the purchase process. It can also allow the department to develop comprehensive dashboards that can serve as a single source containing customer information from different sources. Similarly, it often becomes essential for the companies to internally transit and transforms data between multiple data shelves. For instance, if data is stored in different intelligence systems, it becomes difficult for a business user to drive clear insights and make rational decisions.

Data Pipeline

A data pipeline is an architecture that involves moving data from the source to the target destination. These steps involve copying and loading data from an onsite location into the cloud or merging it with other data sources. The primary objective of a data pipeline is to make sure that all this transition process is applied consistently to all available data.

If handled properly, a data pipeline allows businesses to access consistent and well-organized data for further processing. By practicing data transfer and transformation, data engineers will fetch information from various sources rationally.

Data Pipeline - Use Case

Data pipelines are helpful for accurately extracting and driving useful data insights. The methodology works well for businesses or companies that store and depend on multiple, huge chunks of data sources, perform real-time data analysis, and have their data stored on the cloud. For instance, data pipeline tools and methodologies perform predictive analysis to filter the most probable future trends from the least probable ones. A production department can perform predictive analytics to determine if the raw material is likely to run out. It could also allow making forecasts about the possible delays in a supply line. In this way, these insights can help the production department handle its operations free from any resistance or errors.

Difference between ETL Pipelines and Data Pipelines

Although ETL and data pipelines are closely related concepts, they have multiple differences; however, people often use the two terms interchangeably. Data pipelines and ETL pipelines are both designated to shift data from one source to another; the main difference is the application for which the pipeline is designed, a detail of which is discussed in the following article.

• The difference of terminology between ETL pipeline & data pipeline

ETL pipeline possesses a series of mechanisms that fetch data from a source, transform it, and load it into the target destination. Whereas a data pipeline is a kind of broader terminology with ETL pipeline as its subset. It lacks the transformation phase and only includes transferring data from a source to the target destination.

• Purpose of ETL pipeline VS data pipeline

In a simpler means, a data pipeline is intended to transfer data from sources, such as business processes, applications, and sensors, etc., into a data warehouse to run intelligent and analytical processes. On the other hand, ETL pipeline, as the name suggests, is a specific kind of data pipeline in which data is extracted, transformed, and then loaded into a target destination. After extracting data from the source, the critical step is to adjust this data into a designated data model that’s designed following the specific business intelligence requirements. This adjustment includes accumulation, cleaning, and transformation of the data. In the end, the resulting data is then loaded into the target system.

• Differences in how ETL and data pipeline run

An ETL pipeline operates to fetch data in batches, which moves a certain amount of data to the target system. These batches can be organized in such a way as to run at a specific time daily when incase of low system traffic. On the other hand, a data pipeline doesn’t stockpile from the source and can be deployed as a real-time process by ensuring every event must be handled as soon it happens instead of batches. For instance, to transfer data coming from an air traffic control (ATC) system. Moreover, the data pipeline doesn’t require adjusting data before loading it into a database or a data warehouse. This data can be loaded into any destination system, such as the Amazon Web Services bucket.

How to Build an ETL Process

When you build an ETL infrastructure, you must first gather and combine data from many sources. Then you are required to carefully outline the strategy and test to ensure error-free transfer of data. This is a lengthy and complex process.

Let’s discuss in detail how.

Building an ETL Pipeline for Batch Processing

As discussed earlier in an ETL pipeline, you handle data in batches from source databases to a target destination (a data lake or warehouse). It’s a complicated task to build an enterprise ETL architecture from scratch. Data engineers usually use ETL tools such as Stitch or Blendo, each serving as a simplifier and automating much of your tasks.To develop an ETL pipeline using batch processing, you are required to:

• Create a dataset of the primary key (Unique Variable)

Create a dataset that possesses the set of permitted variables and values your data may contain. For instance, in air traffic control data, specify the flight numbers or flight designator allowed.

• Extract data from multiple sources

The foundations of successful ETL are based on the correct extraction of data. Fetch data from various sources, such as Apps Data, DBMSm RDBMS, XML, CSV files, and transform it into a single format for mutual processing as per standards.

• Validate data

Filter the data with values in the expected ranges from the rest. For instance, if you only want cars record from the last decade, reject any older than ten years. Analyze abandoned records on an ongoing basis, outline issues, adjust the source data, and enhance the extraction process to resolve the issues that can lead to future batches.

• Transform data

Eliminate duplicate data, apply filters ensuring business rules, ensure data integrity (to refrain from losing any data), and create aggregates as necessary. To do so, you need to implement numerous functions to automate the transformation of data.

• Stage data

You cannot typically load transformed data directly into the target destination. Instead, data is first injected into a staging database, making it easier to reverse any change if something goes wrong. This is where you can produce audit reports for regulatory purposes and perform diagnoses to repair any problem.

• Publish to your target system

While loading data to the target database, some data warehouses overwrite existing information upon loading a new batch. These overwrites may occur daily, weekly, or monthly. In other cases, the ETL process can add new data without overwriting the old one, assigning a time flag to indicate it is updated or recent. This practice needs to be handled carefully to secure the data warehouse from overflowing due to disk space.

2. Building an ETL Pipeline for Stream Processing

Modern practices involve data-time processing, such as web analytics data from a large e-commerce website. As discussed earlier, you cannot extract and transform data in large batches, but instead, it requires performing ETL on data streams. As soon as client applications write data to the data source, you must clean and transform it while transitioning between source and destination. Different stream processing tools are available, including Apache Samza, Apache Storm, and Apache Kafka. The illustration below showcases an ETL pipeline based on Kafka (S3 Sink Connector to stream the data to Amazon S3).

(Source - Confluent)

To create a stream processing ETL pipeline using Apache Kafka, you are required to:

• Extract data into Kafka Topics

Java Database Connectivity (JDBC) is an application programming interface (API) for Java's programming language. Here, the JDBC connector attracts each source table row and feeds it into a key/value pair into a Kafka topic as message feeds. Kafka’s organized message feeds into categories called topics. Each topic has a title that is unique across the entire Kafka cluster. Applications interested in the state of this table read from this topic. As client applications add rows to the source table, Kafka automatically updates them as new messages to the Kafka topic, allowing a real-time data stream.

• Pull data from Kafka topics

The ETL application fetches messages from the Kafka topic in Avro records, creates an Avro schema file, and deserializes them. Deserialization does the opposite of serialization by converting bytes of arrays into the desired data type. Then it produces KStream objects from the messages.

• Transform data in KStream objects

Using the Kafka Streams API, the stream processor receives a single record, processes it, and generates one or more output records for downstream process handlers. These process handlers can transform one message, filter them as per regulations, and perform different operations on many messages.

• Load data to other systems

The ETL application still possesses the enriched data and now requires to stream it into destination systems, such as a data warehouse or data lake. Amazon S3 or Amazon Simple Storage Service is a service provided by Amazon Web Services that allows object storage through a web service interface. In the diagram above, the S3 Sink Connector is used to stream the data to Amazon S3. PS: One can also integrate with other systems, such as a Redshift data warehouse using Amazon Kinesis Data Firehouse, integrated with Amazon S3, Amazon, and Amazon Elasticsearch Service. Now you know how to perform ETL processes the conventional way (Batch Process) and streaming data.

Conclusion

As you’ve seen, although used interchangeably, ETL and data Pipelines are two different architectures. While the ETL process involves data extraction, transformation, and loading, the data pipeline doesn’t necessarily include data transformation. Shifting data from source to target system enables various operators to query more systematically and correctly than possible instead of dealing with complex, diverse, and raw source data. A well-structured data pipeline and ETL pipeline improve the efficiency of data management and enable data managers to easily make instant iterations to fulfill the evolving data requirements of the business.

The notable advantage of data science over mainstream statistical practices is that it can extract insights and build accurate predictions from the junk of irrelevant data. That’s how sports analysts and team selectors in cricket and football could find the most accurate KPIs and build a team that can be regarded as the best suitable option against the rival teams.

To build such intelligent models, data scientists are trained and employed to produce solutions in machine learning data models. This blog post reveals the crucial steps the data science project manager and all the stakeholders need to go through to deal with complex data science projects.

Defining Data Science

To free from any confusion or misconception, it’s essential to clear your perspective about data science and its related methodologies. Data science itself is complicated to explain in an absolute frame of reference. If you ask different Data Science experts about the absolute definition of the theme, you will be bombarded with aspects and ideologies of varying degrees.

Also Read: Data Science and Machine Learning In Demand Forecasting For Retail

The more straightforward way to define Data science is a combination of different fields of mathematics and computer science, specifically statistics and programming that deals with the practices of extracting insights and hidden patterns from lengthy datasets. Since the dependence of businesses on data increases at a high pace, the need to grasp the concept of data science and its related methodologies is the primary step to solve most of the data-related problems.

Data science and its related concepts

Despite being a technical methodology, managing a data science project is not a purely technical routine. It is a combination of both hard and soft skills. Like any other project, a data science project needs a roadmap that will guide you along the way of problem-solving. The Following rules will lead you to successful project completion while allowing you to save your valuable resources, time, and money.

Data Science Project Management

The Data Science Project involves a lifecycle to ensure the timely delivery of data science products or services. Like Agile and Scrum methodology, it is a non-linear, repetitive process richly inhabited with the critical queries, updates, and iterations based on further research and exposure to unseen depths of data as more information becomes available.

Let’s have a deeper look into how these steps allow you to handle your data science project efficiently.

Step 1: Frame the problem

Every data science project should be initialized to understand business workflows and their related challenges or hurdles. In this segment, all the stakeholders on board play a vital role in defining the problems and objectives. Even a negligible aspect of the business model should be taken into consideration to avoid any trouble later. The destiny of your project is determined at this level, making this step one of the toughest and crucial ones. But when carried out correctly, this phase can promise you a lot of savings in energy, cost, and time.

Ask business owners what they are trying to acquire. Being an analyst, the biggest challenge you will face here is to translate the blurry and obscure requirements into a well-explained and crystal-clear problem statement. The technical gap between stakeholders and experts can push a lot of communication-related problems in the process.Input from the business owner is essential, but in many cases, not substantial enough, or he/she may not convey his/her thoughts properly. A deeper understanding of business-related problems and technical expertise can help you in filling this gap. Don’t hesitate to learn anything from the sponsors regarding their domain if necessary.

A clear and concise storyline should be the primary outcome at this stage.

Since the framework is repetitive, rolling back to this stage will occur in later stages. Try to form a firm foundational basis of your project that everyone can understand and agrees on.

Step 2: Get the data

Now you can shift your focus to data gathering. You will find structured, unstructured, semi-structured; all types of data useful in different ways. Multiple data sources can cause a lot of confusion and provide space for creativity and alternatives. Potential data sources, including websites, social media, open data, or enterprise data, can be merged into data pools. The data has been identified but is not ready to be used. Before using it for the more significant motive, you need to pass it through several channels, from importing and cleaning to labeling and clustering. Cleaning the data ensures that data is free from all the errors and anomalies and loaded well into the machine. Make sure the data you are using is correct and authentic. Neglecting this crucial aspect can lead to long-term damages. Importing the data is the most time-consuming exercise as it could consume around 70 to 90 percent of the overall project time. If all the data sources are well organized, it can fall to 50 percent. You can also spare your time and effort by automating some processes in data preparation.

Step 3: Explore the data

After ensuring the quality of the collected data content, you can initiate the exploration drive. Observe the organizational efficiency of existing labels, segments, categories, and other distribution formats. Analyze the links and connections in various attributes and entities. Visualization tools may save you most of your time and energy while allowing you to better understand the layout of data content better understand the structure of data content and uncover essential better insights. The first purposeful glance at the data reveals you most of the gaps and misunderstanding that might be overseen in previous steps. Don't hesitate to roll back to the earlier stages, specifically the data collection, to close the gaps.

Example of data visualization tool to discover insights

The most troublesome phase in this step is to test those concepts and processes that are most likely to turn into insights. Revisit the results from the problem framing step to find some essential questions or ideas that will enable you to improve your data exploration drive. Like all the other projects, project duration, timelines, and deadlines will also restrict you from rolling back again and again. It bounds you to reconsider all the scenarios and aspects before revisiting the previous levels. Set your priorities aligned. Once you start observing clean patterns and insights, you are almost ready to model your data. Rethink your conclusions at every single step and filter your perspectives carefully. You are almost there.

Step 4: Model the data

At this critical level, you can perform the practical modeling of the data. Data scientists use a train set to train their machines to perform empirical predictive analysis and decision-making over a test set. A train set is a mighty chunk of the dataset with known output. This stage is highly repetitive. To make data align with the model, data scientists perform multiple adjustments to the parameters to best fit the data. Here fit means adjusting the parameters so that it can be used successfully with unseen data known as the test set. Once you’ve developed a model, you need to perform a validation test. This is how you ensure the accuracy and perfection of the model. If your model is either overfit or underfit, you need to rebuild it by changing the parameters and the size of the train set. Overfitting means the model gives accurate results only on the train set, and underfitting means the model delivers accurate results not even on the train set. The final product should be generalized, giving accurate results on both the training set and test set. Must ensure that your model addresses the business problem by revisiting the first stage. Keep in mind; not every model is perfect. The key to success is extracting the best out of your model in terms of accuracy and objectivity.

Step 5: Communicate the results

Last but not least, communicating the result means presenting your model to the business owner or sponsor. They certainly don’t know what to do with it and how it works. The more precise and clear the presentation, the easier for you to explain the project. Refrain from going into too many technical details; the client may not understand or may not be interested in knowing that. Document all your complex calculations and use easy wording to explain everything. Just Delivery of the model is not necessarily the last step in a data science project. It depends on the pact between you and the client. Sometimes, you and your developer team will deliver the developed model to those who will deploy it in a facility intended to perform. If you’re developing a predictive model used, for example, an e-commerce platform, you need to stay active on the server and extract insights and make predictions based on real-time customer data. If it is not part of your agreement, you must ensure that your whole work and calculations are documented to read like a user’s manual. Archive all of the methodologies that you’ve implemented. It includes evolution diagrams, graphs, and other visualizations of the data in every step, from raw form to cleaned information and the final model with relevant notes, comments, illustrations, and the programming code. By doing so, your client will be able to crack all the technical stuff you have done developing the model. If anybody needs to go back and verify the analysis, it becomes easier for them to use the guidelines and manual you provided.

Conclusion

The Data Science project demands immense transparency and collaboration. Irrespective of how much expertise you have, you cannot build an accurate model without knowing the crux of the business process. In the same way, business owners, CEOs, managers, and sales executives cannot achieve success without data science in a highly dynamic market. Refrain from working in isolation. Communicate with all the involved stakeholders and don’t hesitate to visit any previous stage if necessary. For the sake of authenticity, use authentic and information-rich data sets or results will always be the same or imperfect, while expenditure will be much higher in terms of time and money.