As organizations accumulate vast amounts of data, there is an increasing need for efficient and scalable ETL tools. These tools help manage, process, and transform this data into meaningful insights. ETL tools aren’t just about moving data from one place to another; they’re crucial in enabling businesses to leverage their data effectively for analytics and decision-making.
Amazon Web Services (AWS), a leader in cloud computing, offers a suite of ETL tools designed to cater to different data integration and transformation needs. From real-time data streaming to handling massive datasets, the different AWS ETL tools help streamline the transformation of raw data into actionable insights.
Let’s look into the features, benefits, and potential pitfalls of the best AWS ETL tools available in 2024.
1. AWS Glue
AWS Glue, formerly known as AWS Stitch, is a serverless, fully managed ETL platform that simplifies the process of preparing your data for analytics. It streamlines the process of transferring data between data repositories. All it takes are a few clicks in the AWS Management Console to create and run an ETL job.
AWS Glue comprises an AWS Glue Data Catalog containing central metadata, an ETL engine that automatically generates Python or Scala code, and a flexible scheduler that handles job monitoring, dependency resolution, and retries.
After you configure AWS Glue to point to your data stored in AWS, it automatically discovers your data, infers schemas, and stores the related metadata in the AWS Glue Data Catalog. Once this is done, your data is available for ETL and can also be searched and queried immediately.
Suitable For: AWS Glue is a suitable choice if your organization already uses the AWS ecosystem and you’re looking for a managed ETL service that simplifies the data preparation and loading process.
Key Features of AWS Glue
- GUI Support: AWS Glue has a GUI that allows you to build no-code data transformation pipelines. This will benefit those who don’t have a fair knowledge of Python or PySpark to easily design a basic Glue job from scratch. As you start designing your job in the GUI, Glue automatically generates PySpark code in the background.
- Integration with other AWS Services: As an AWS-native service, AWS Glue offers better integration with other services within the ecosystem, such as Amazon Redshift, S3, RDS, etc.
- Scalability: With petabyte-scaling capabilities, AWS Glue automatically scales the resources based on the workload. This allows it to manage varying data loads efficiently.
- Glue Data Catalog: This is a centralized metadata repository that stores metadata about your data sources, transformations, and targets, making it easy to find and access data.
- Automated Schema Discovery: Glue automatically discovers and catalogs metadata of your data sources. It creates ETL jobs to transform, normalize, and categorize data.
- AWS Glue Crawlers: These help establish a connection to your source or target data store. AWS Glue crawlers navigate a predefined set of classifiers to identify the data schema and generate metadata in your AWS Glue Data Catalog.
2. AWS Data Pipeline
AWS Data Pipeline, one of the most reliable AWS ETL tools, is a web service that helps you move data between different AWS storage and compute services and on-premises data sources at specified intervals. This tool automates the ETL process: it specifies all your ETL jobs, schedules them to run at a specified time and date, and manages their execution across AWS services.
A simple UI allows drag-and-drop of different source and target nodes onto a canvas. It also lets you define connection attributes, thus helping create ETL data pipelines. AWS Data Pipeline allows you to access your data where it’s stored regularly, transform it, and process it as needed. Then, you can efficiently transfer the resulting data to AWS services such as Amazon RDS, Amazon S3, Amazon DynamoDB, and Amazon EMR.
You can use preconditions and activities that AWS provides or write custom ones. This allows you to execute SQL queries directly against databases, run Amazon EMR jobs, or execute custom applications running on Amazon EC2 or in your own data center.
Suitable For: If your business requires regular data movement and processing across various AWS services, AWS Data Pipeline is a suitable choice.
Key Features of AWS Data Pipeline
- Flexible Scheduling: You can schedule your data processing jobs to run at specific intervals. This helps handle dependencies in the data and ensure that tasks are executed in the right order.
- Integration with AWS Services: Seamless integration with other AWS services allows smooth data transfer and processing across different AWS resources.
- Improved Reliability: The distributed, highly-available infrastructure of AWS Data Pipeline is designed for fault-tolerant execution of your activities. If a task fails, it can automatically retry the task. This helps ensure the integrity of your data processing workflows.
- Monitoring and Alerts: If a failure in your activity logic or data sources persists despite retries, AWS Data Pipeline will send you failure notifications via Amazon Simple Notification Services. The notifications can be configured for successful runs, failures, or delays in planned activities.
- Resource Management: AWS Data Pipeline eliminates the need to manually ensure resource availability. It can automatically manage the underlying resources required for data processing tasks.
- Prebuilt Templates: AWS Data Pipeline provides a library of pipeline templates, making it simpler to create pipelines for more complex use cases. Examples of such use cases include regularly processing your log files, archiving data to Amazon S3, or running periodic SQL queries.
3. AWS Kinesis
AWS Kinesis is a fully managed AWS service that offers real-time data processing of large-scale streaming data. It consists of three components: Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Video Streams.
Kinesis Data Streams can collect and process huge streams of data records in real-time. You can process and analyze the data as soon as it is available and immediately respond to these events.
AWS Kinesis is widely used for real-time analytics, IoT data processing, and creating real-time applications for app monitoring, fraud detection, and live leaderboards.
Suitable For: AWS Kinesis is a good choice if your business needs to process large streams of real-time data with minimal latency. It’s particularly beneficial for a wide range of real-time data processing applications.
Key Features of AWS Kinesis
- Kinesis Data Firehose: This is an ETL service that reliably captures, transforms, and delivers streaming data to data lakes, data stores, and analytics services. By loading streaming data into Amazon S3 or Amazon Redshift, you can achieve near real-time analytics.
- Kinesis Data Streams: This is a serverless streaming data service that makes it easier to capture, process, and store data streams at any scale. Data Streams synchronously replicates your streaming data across three Availability Zones in the AWS Region. It stores the data for up to 365 days, providing multiple layers of data loss protection.
- Kinesis Data Analytics: This allows you to process and analyze streaming data using standard SQL, making it easier to feed real-time dashboards and create real-time metrics.
- Scalability: AWS Kinesis is a highly scalable service. It can handle high volumes of streaming data without requiring any infrastructure management.
- Integration with Other AWS Services: Kinesis can seamlessly work with other AWS services like Amazon Redshift, Amazon S3, and Amazon DynamoDB.
Factors to Consider for Choosing an AWS ETL Tool
Now that you’ve seen the best AWS ETL tools, here’s a list of factors to consider when choosing the most suitable one for your organizational needs:
- Data Sources and Destinations: Ensure the tool supports the required data sources (databases, file formats, APIs, etc.) and destinations (data warehouses, data lakes, etc.) for seamless integrations.
- Real-Time Processing vs. Batch Processing: Determine whether you need batch data processing or real-time processing (streaming ETL). AWS Kinesis is designed for real-time data streaming, while others may be suitable for batch processing.
- Scalability: The size and scale of the data can also impact the choice of tool. Assess how well the tool can handle increasing data volumes and complexity. It’s essential to consider the scalability limits to ensure the tool can adapt to your data needs without requiring extra charges for smaller workloads.
- Cost: Evaluate the pricing models of the different tools. While some have fixed pricing models, others may offer pay-as-you-go pricing. Based on your specific data volumes and processing requirements, it’s crucial to understand the cost implications.
- Security and Compliance: If your organization handles sensitive data, ensure the tool meets certain security and compliance requirements. Features such as encryption, access control, and audit trails are essential.
- Support: Look into the level of support the provider offers, including comprehensive documentation, customer service, and community forums. This can be valuable for troubleshooting and optimizing your ETL processes.
An Equivalent Alternative to AWS ETL Tools: Airbyte
Airbyte is a popular data integration platform and a suitable alternative to the AWS ETL tools. While the AWS ETL tools are highly optimized for moving and transforming data primarily within the AWS ecosystem, Airbyte can help you synchronize data from various sources to data warehouses, lakes, and databases.
You can use the expansive catalog of connectors Airbyte offers to load data to and from AWS effortlessly. There are Airbyte connectors for Amazon Redshift, Amazon S3, Amazon DynamoDB, AWS Data Lake, Kinesis, and S3 Glue, among others. Apart from these, you can also set up ETL pipelines to seamlessly integrate data to or from non-AWS sources, catering to diverse data integration needs.
You can deploy Airbyte in the cloud (on platforms like AWS) or on-premises. This offers your organization flexibility based on your infrastructure preferences.
Suitable For: Airbyte is particularly beneficial if you want a customizable, scalable, and easy-to-navigate data integration solution.
Key Features of Airbyte
- Readily-available Connectors: Airbyte offers about 350+ connectors for different databases, SaaS applications, data warehouses, and other data sources. Among these are several AWS service-based connectors that allow seamless integration of data.
- Custom Connector Development: If the connector you require isn’t available, you can use Airbyte’s no-code Connector Builder to build your own connector in only ten minutes.
- Scalability: Airbyte is designed to be highly scalable and can handle large volumes of data. It offers horizontal scalability through its microservices-based architecture, enabling seamless scaling by adding or removing Docker containers or Kubernetes pods.
- Incremental and Full-Refresh Sync: Airbyte supports both incremental and full-refresh synchronization. This provides flexibility in how to update and maintain data in target systems.
- Monitoring and Logging: You can use Airbyte’s detailed monitoring and logging capabilities to track your data integration process. This allows you to identify and troubleshoot issues and ensure data integrity.
The availability of diverse AWS ETL tools caters to organizations’ data integration and transformation needs. From Airbyte’s extensive connector catalog and AWS Glue’s serverless architecture to AWS Data Pipeline’s flexible scheduling and AWS Kinesis’ real-time data streaming, each AWS ETL tool has its own benefits.
When selecting the right tool for your organization, you must consider factors such as supported data sources and destinations, processing needs, scalability, cost, security, and support offered. The choice of an ETL tool—a crucial decision—can significantly impact the effectiveness of your data management and analytics strategies.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Other potential services
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: