Top ETL Options for AWS Data Pipelines

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With so many data sources, your landscape already looks complicated. There are a lot of business requirements, process changes, and new regulations that make it even more difficult.

Therefore, finding the perfect ETL process and tools like Skyvia for your company makes a huge difference.

And there is no one-size-fits-all solution. The ideal concept is based on your data warehouse, data sources, and business requirements. Let’s find out more!

How Does ETL Work?

ETL (Extract, Transform, Load) is a three-step process designed to move and prepare data for analysis or storage:

1. Extract: The process begins by retrieving data from one or multiple sources, which could be databases, APIs, or cloud storage. In AWS, common data sources include Amazon S3, Amazon Aurora, Relational Database Service (RDS), DynamoDB, and even compute services like EC2.
2. Transform: Once extracted, the data is transformed using supported methods. This step includes data cleaning, filtering, and structuring it into the desired format, such as combining multiple data sets or applying business rules.
3. Load: Finally, the processed data is loaded into its destination, typically a data warehouse, such as Amazon Redshift or another target system, where it can be used for further analysis or reporting.

In AWS, this ETL process is essential for handling various data types and ensuring that all data sources, whether structured or unstructured, are ready for meaningful insights.

Redshift is a great example of cloud data warehouses. It can scale easily to accommodate processing loads. This allows the data engineers to do the transformations after loading. This means that the data pipeline process will be changed from ETL to ELT.

Data Pipeline

ETL consists of several key steps that involve replicating data from one system to another. The first critical step is identifying all of your data sources, whether they are databases, applications, or cloud services.

Once you’ve identified your data sources, you need to determine when the source data has changed. This step is essential for optimizing the ETL process, as it prevents the system from replicating the entire data set unnecessarily. Instead, only the modified or new data is extracted, saving both time and resources.

Additionally, your chosen data warehouse destination needs to have the right architecture to support the types of data analysis you require. The warehouse must also be compatible with your current software ecosystem and, of course, fit within your budget.

You could assign a data engineer from your team to manually develop a reusable data pipeline. However, building ETL code is far from straightforward. Data engineers will need to:

• Understand how to interact with the APIs of various data sources
• Write custom logic to handle the extraction of data
• Integrate security measures, logging mechanisms, and alert systems
• Conduct thorough testing to ensure the pipeline works as expected
• Monitor and evaluate the pipeline’s performance regularly
• Continuously revisit and refine the code to keep the pipeline functioning efficiently over time

AWS Glue for ETL

AWS Glue is a service you can access. It is good if you want to transfer data from an Amazon data source to an Amazon data warehouse.

The process:

1. Schedule ETL jobs or set up event-based triggers to kickstart the process.
2. Pull data from relevant AWS sources such as S3, RDS, or DynamoDB.
3. Use AWS Glue to automatically generate the transformation code and apply the necessary changes to the data.
4. Move the transformed data to its final destination, either Amazon Redshift or S3, depending on your requirements.
5. Log details about the ETL process in the AWS Glue Data Catalog to maintain metadata for future use and tracking.

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