How to Build ETL Pipeline using Snowflake

April 2, 2023
Table of content

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.

ETL on Snowflake

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.


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.

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Data Science
Process Mining vs. RPA: Benefits, Costs, and Comparison
5 min read

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.

Data Science
Snowflake vs BigQuery: Best Cloud Data Warehouse in 2023
5 min read

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 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


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


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.

Data Science
Data Science Project Life Cycle: Stages & Significance
5 min read

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.


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!

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