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Data has become an integral part of every company. The complexity of data processing is rising with the data amount, rate, and variety. The problem arises from the amount and complexity of measures required to bring information to a state easily accessed by business users. Data engineering teams also invest their time constructing and improving ETL pipelines. In this article, we will discuss the usage and process to design and implement the serverless ETL pipelines on AWS.
Technical developments have substantially changed the software development environment. Cloud platforms such as IaaS and PaaS have prompted businesses towards better feasibility. Likewise, serverless computing has facilitated code implementation into development.
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Serverless Cloud Providers such as Amazon Web Services (AWS) operate the server and technology needed for data collection, computation, routing, event notification, and display for data applications. AWS offers a range of professionally controlled software, including planning, scaling, and management, utilizing a smart payment model to develop and maintain business software. As businesses create a serverless pipeline, Amazon S3 is typically the primary data store that they use. Since Amazon S3 is very versatile and accessible, it is ideally suited as a unified source for data.
Usage of ETL Pipeline
ETL (extract, transform, load) is a data aggregation related to the three measures used to integrate data from different sources. It is frequently used for the building of a data warehouse.
Serverless ETL has become the vision for teams who wish to concentrate on their core tasks instead of operating a massive data pipeline infrastructure.
Data is obtained and analyzed in the ETL pipeline's extraction portion from various networks, such as CSVs, online services, social networking sites, CRMs, and other business systems.
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In the process transformation phase, the data is then modeled in a form that simplifies documentation. Data cleansing is also often part of this phase. The processed data is loaded into a single hub in the loading phase to make it convenient for all participants.
Related: ETL Pipeline and Data Pipeline – How to create an ETL Process
The ETL pipeline aims to identify, report, and maintain the correct data in a form that makes it simple to view and evaluate. An ETL tool would encourage developers to concentrate on logic/rules rather than create the means to apply the code. It eliminates the time that the developers use to develop the tools, which helps them concentrate on other essential jobs.
Building Serverless ETL Pipelines on AWS
A serverless architecture on AWS is based upon the following processes:
Data Ingestion
AWS provides a wide variety of data intake tools to help. You may use the Kinesis streaming media tools family to ingest data and use Kinesis analytics to evaluate the details while streaming and deciding on the data before it goes on in the data lake. Kinesis Analytics can evaluate the log data from the system and assess if the logs are out of the data range, and mark it for intervention before it fails. AWS also provides Database Migration Services (DMS). It offers businesses for on-prem devices that do not generally communicate to object storage or the analytics interface but instead speak to a file system. You may use an AWS Storage Gateway for the integration or an AWS Snowball for data collection and 'Lift & Shift' to the cloud.
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After that, you should set up AWS Direct Connect and start creating a direct network link between the on-prem setting in the AWS services, whether the cluster is existing on-prem, a data warehouse, or even a big storage unit. Data intake is essential to keeping the data feasible, and you need to select the best method with the right data form.
A Searchable Catalog
A searchable catalog is essential to build a data lake. Without it, you will just have a storage platform and not a data lake. You need it to get insights from your data. AWS Glue comes into play here as it is a stable, scalable data catalog created as data enters the Data Lake. It quickly crawls data to build a classified catalog. After processing the results, you have to be able to show them and gain insights. This can be achieved directly by Spark SQL analytical software. You get a range of resources utilizing the AWS framework, such as API Gateway, Cognito, AWS AppSync, to help you create those user interfaces over your data lake.
Also Read: Next Gen Application Microservices Architecture on Kubernetes
Data Security Management
Data confidentiality and governance management are fundamental factors, as an unsafe data lake is unusable. In the end, a data lake means taking a bunch of individual data silos, combining them, and providing more information from a full perspective. On AWS, you have a broad range of protection services such as Identity and Access Management, enabling you to safely manage access to AWS facilities and benefits. With IAM, you can build, control, and use permissions to allow and reject access to AWS services to AWS users and groups. AWS's Key Management Service (KMS) helps you to build, maintain, and track cryptographic keys through a wide variety of AWS services and applications.
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Amazon CloudWatch helps you continue tracking AWS services and programs such that configurations and adjustments can be conveniently measured, evaluated, and registered. You can use various resources to handle data in a very stable, scalable, and granular fusion.
Data Transformation
AWS helps you quickly access serverless analysis using AWS Step Functions and AWS Lambda to coordinate various ETL workflows utilizing multiple technologies. AWS Lambda helps you to execute applications without servers. One can run code with zero administration for almost any form of program or back-end operation using Lambda. You need to upload your code, and Lambda manages all the scaling. AWS Step Functions is a web service that helps organize distributed apps and microservices modules using visual workflows. Each component executes a particular role or activity that allows you to scale and modify applications quickly.
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AWS offers easy access to scalable computational and processing capabilities to quickly extend almost every large-scale framework, including data collection, fraud identification, clickstream analytics, serverless computing, and IoT processing.
Conclusion
You can use the AWS Glue ETL for two types of works: the Apache Spark and the Python shell. The Python shell work enables you to execute small tasks utilizing a fraction of the machine resources. The Apache Spark helps you conduct medium to broad functions that are more numerical and memory demanding using a distributed processing system. As serverless services become more efficient, we will see more companies using traditional architectures to be serverless. Anyone who is planning to build a new data platform should start with serverless. Let us know know in the comments section below if you have any query regarding designing and implementing the Serverless ETL Pipelines on AWS.
