How to Select the Best ETL Tool to Integrate with Airflow? Our 3 Picks

How to Select the Best ETL Tool to Integrate with Airflow? Our 3 Picks

Find out if choosing the best ETL tool is easy and which three ETL tools we like to combine with Airflow.

J
Julia Wrzosińska

What is the best ETL tool for your company? It all depends on your specific business requirements and available resources. From finances to technical know-how, it's critical to select a solution that suits your needs. In this article, Astronomer experts have selected the finest tools for the job, so if you're considering developing an ETL process in your organization, you’ve come to the right place. We will walk you through the important capabilities of an ETL tool, help you spot the red flags, and make an informed decision.

Why is ETL Important?

Since you’re curious about our recommended ETL tools, you've probably heard of the ETL process and you're most likely aware of how important it is for businesses… but for a quick recap, feel free to visit our blog post discussing the vital steps of building a reliable ETL process

We all know that companies nowadays are drowning in raw data that must be sorted through. Well-organized data provides a comprehensive view that enables making better data-driven decisions in less time. Developing an automated ETL process enables you to process data without manual effort by providing the control, monitoring, and scheduling of tasks. As a result, the data can be used for BI analysis, analyzing trends, anticipating performance, and predicting future outcomes. Breaking down data silos is an additional benefit. All of the above are definitely worth introducing within any organization.

ETL vs. ELT

Both ETL and ELT involve data extraction, loading, and transformation. However, they carry out these procedures in a different order - ELT transforms the data only after loading it to the destination. It is a novel technology, enabled by high-speed, cloud-based servers. Cloud-based data warehouses provide near-limitless storage and scalable processing capacity, making it easy to transform data after loading. So when is it preferable to use ETL, and when does it make more sense to choose ELT? There is no simple answer to this, as it all depends on specific preferences. ETL vs. ELT is all about what tools you have available and the scale of your data. For example, ELT seems to be the most modern approach to data circulation, perfect for Big Data companies - it stores all the information in one location, allowing for fast access. At the same time, ETL is said to have better information privacy and compliance, as data is cleansed prior to loading.

How to Choose a Perfect Tool?

Before settling on one choice, it is critical to analyze the specifications that an ideal ETL tool must meet. Once we understand the various types of ETL tools available on the market, we need to know what we should base our tool selection on. It is critical to note that there are no superior or inferior tools; rather, some will be more appropriate than others based on the company's specificities, demands, and amount of data. We’ve selected the most important features you should consider before choosing “the one” ETL or ELT tool for your organization.

1. Functionality (User Profile)

Some ETL tools look like they are designed by expert data engineers for expert data engineers only, and can be difficult for others to learn. It is important to remember that some tools have more sophisticated applications than others and that each one requires a particular level of technical understanding. Tools focusing on attractive graphic interfaces and drag and drop interactivity are usually easier to use for non-experts but may be impractical for advanced use cases involving complex data models.

  • Who are your users and what are they looking for? Are they data engineers who want to write Python, or are they folks who would prefer a point and click solution?
  • How simple is it to start up a new ETL process or change an existing one?
  • What degree of expertise will you or your team require to keep the ETL system working smoothly?
  • What is the level of technical understanding within your organization, and how complex is your data?

2. Connectors

An ETL tool must be able to connect to a variety of data structures, including files in various formats, web pages, office tools, content repositories, and, most importantly: relational and non-relational databases, ERP, CRM, or SCM systems.

  • Can the ETL tool adapt to your organization's different operating systems and hardware platforms?

3. Customizability and Integration

ETL must provide compatibility across various components of the data integration tool. Using an ETL tool to design an integration with a data source is much easier than writing a bunch of code, so custom connectors for frequently used data sources are now available in robust ETL solutions. If an ETL tool provides bespoke connectors for most of the data sources you use, the time it takes to get your data warehouse up and operating can be drastically reduced. Instead of requiring weeks or months to integrate your data sources, a large portion of your data may be ready to go in hours or even minutes with the correct connectors.

  • If you don't intend to store your data in a traditional data warehouse—for example, if your datastore uses noSQL— does the ETL tool you chose integrate easily with your data source and storage?
  • How simple would it be to keep up when a data source's model evolves?
  • Does the ETL tool have native integrations to all your data sources? And if it doesn’t, is it capable of:

    • Integrating with other ETL tools that contain the connector you want?
    • Connecting to an S3 bucket that can serve as a conduit between the original data source and your ETL service?
    • Ingesting data from a manually programmed pipeline (not recommended when you have hundreds of pipelines to handle, though)?

4. Support and Documentation

No matter how effortless a tool seems to be, at some point, you will need help. Especially in the beginning, so good training options are beneficial. Your tool should offer quick, efficient support.

  • How thorough is the tool's documentation?
  • Is there training available?
  • Does the tool offer high-quality live support?
  • Is there an online community dedicated to the tool, and how long does it take to answer queries? How helpful and informative do you find the replies of the community?

5. Management and Control

It is crucial to select a tool with control systems for identifying and resolving any problems or errors that may develop. Maintenance can be critical to your success!

  • How user-friendly and robust are the error logs?
  • How fast will you be able to trace down and repair an issue with one of your data sources?

6. Last but not least, the pricing

There’s more to the price than just the acquisition cost, and it can get very confusing. There’s training, support, consulting, and other options. Open-source tools are free to acquire, but the long-term costs frequently can become higher.

  • Is there a free trial period?
  • What is the price per annum?
  • How much will it cost to build out your initial pipeline?
  • Do your costs grow (and by how much) as the amount of data in your data sources or the number of data sources expands?
  • Is the pricing model consistent? How well do you comprehend it?

Our Favorite ETL Tools

Now that you know what to look for, we’ve prepared a selection of our favorites that we often use, as they integrate with Airflow nicely. We broke them down into the essential features in order to answer your questions and find a perfect fit for your needs.

1. Fivetran

Fivetran is rapidly growing in popularity, so if you haven’t heard about this comprehensible ELT tool yet, it’s high time! This tool empowers analytics teams with simple, reliable data integration from disparate sources (websites, browsers, servers, apps, you name it). Obviously, Fivetran effortlessly extracts and loads data, but what’s most important this tool allows for very convenient various data transformations, really highlighting the T in ELT. Functionality is vital in these cases, and Fivetran delivers.

Type Closed-source, "Big Data as a Service" category of the tech stack.

Focus Fivetran is a data ingestion and ELT tool; thus it does not transform data prior to loading.

Connectors Over 150 pre-built source to destination connectors for pushing and pulling data. The pull connectors will get data from data sources using several ways, including ODBC, JDBC, and other API methods. Fivetran’s push connectors receive data that a source sends or pushes to them as events.

Support & docs Fivetran provides support through an in-app form but does not offer chat support. Their documentation is comprehensive.

Pros:

  • Ready-to-query schemas.
  • Automated schema migrations.
  • Scheduled or triggered transformations made easier with features like version control, email notifications, data validation, etc.
  • Nice integration with Airflow.

2. Airbyte

Airbyte took off in 2020, but it has a super-fast growing community. Simply put, Airbyte is an open-source alternative to Fivetran - a platform that syncs data from applications, APIs & databases to data warehouses. Contrary to Fivetran, Airbyte does support data lakes as a destination. They hope to commoditize data integration through their increasing contributor community by tackling the long tail of connections. Airbyte offers scheduled updates, full manual refresh, and real-time monitoring.

Type Open-source, "Big Data as a Service" category of the tech stack.

Focus ELT as a first step, plus reverse-ETL coming in 2022. Airbyte is an ELT tool and thus does not transform data prior to loading.

Connectors Within 14 months of its launch, Airbyte has already provided connectors for more than 120 data sources and major data warehouses, lakes, and databases as destinations. All Airbyte connectors are open source and may be modified to meet the demands of individual clients via the open-source edition or the Cloud offer. Airbyte's Connector Development Kit also allows customers to create bespoke connections in a standardized manner in a couple of hours.

Support & docs Airbyte is rapidly expanding, and it has a thriving support network to back it up. Airbyte offers in-app chat assistance with an average response time of one hour. Their documentation is extensive and chock-full of tutorials. Airbyte also features a Slack and Discourse forum where users and contributors may get support from the Airbyte team. However, Airbyte does not offer training.

Pros:

  • Airbyte allows you to create your own pipelines and connectors in any language.
  • Airbyte connectors are ready to use out of the box because they operate as Docker containers.
  • Since Airbyte requires self-hosting, there’s no need to worry about the compliance difficulties that third-party service providers might cause.
  • The Connector Development Kit is instrumental.
  • Nice integration with Airflow.

3. Azure Data Factory

Definitely the most significant player within our selection, Azure Data Factory is a data integration solution that creates ETL and ELT pipelines in the Cloud, so it’s the only tool here that supports both pre-and post-load transformations. It enables users to develop cloud-based data processing workflows for coordinating and automating data transportation and transformation, either using a graphical interface or by code. But instead of dragging and dropping, each visually designed block in the pipeline represents a piece of JSON-code that may alternatively be developed manually or ingested from a GIT repository. Data Factory's core process is organized in pipelines.

Type Closed-source.

Focus Cloud-based ETL and ELT.

Connectors Azure Data Factory integrates with about 80 data sources, including SaaS platforms, generic protocols, file formats, SQL, and NoSQL databases. It is compatible with around 20 Cloud and on-premises data warehouse and database destinations.

Support & docs Azure Data Factory offers help through online forums and a support request form; there is also email and phone help accessible for old-school fans. The documentation is extensive, but there’s a number of users who believe there’s room for improvement. The good news is there are digital training resources, too!

Pros:

  • Azure Data Factory supports both pre-and post-load transformations, offering a wide range of transformation functions.
  • Azure Data Factory allows you to design scheduled processes (pipelines) without writing any code, but you can use coding, too, if you prefer.
  • Pre-defined templates to design the pipelines
  • Easy integration with other Azure services

Airflow for ETL

As we mentioned before, there is no single superior ETL / ELT tool. However, this isn't neccessarily bad news. What truly matters is having an orchestrating tool which can integrate all the other solutions you use on a daily basis for your data analysis. This is where Airflow comes in.

Airflow was designed to be an orchestrator, not an execution framework. which means in practice that it's a best practice to use Airflow Providers for orchestrating jobs with other tools. Airflow allows you to pick whatever ETL tool is best for you, and orchestrate it with any logic that you have. For example, if you want to validate row counts after choosing Fivetran to load your data into bigquery, you can follow the approach in this DAG. If you want to write custom Python scrips to get your data out of Salesforce into Snowflake in an ELT pattern, you can use the approach shown here. Obviously in Airflow, your pipelines are written as code, which means you're going to have the flexibility of Python behind you, and it was designed with scalability and extensibility in mind. Using Provider Packages allows you to orchestrate services with Airflow with very little code. That's one of the biggest benefits of Airflow.

Summary

What truly matters is choosing a robust, customizable orchestrator, like Airflow. What's also worth highlighting here is that compared to traditional ETL approaches, it is evident that ELTs are the way of the future for data processing. ELT approaches, which are more sustainable, effective, and timely in general, allow greater flexibility and customization for enterprises that wish to govern their data integration and execution. Looking ahead, it stands to reason that ELTs will quickly become the de facto solution for enterprises focused on efficiency, scalability, and dependability. While both options have advantages and disadvantages, ELT has emerged as the undisputed preference of many businesses worldwide.

To sum up, no matter which tool you choose, we are starting to recommend the ELT approach in most cases. We won’t tell you which tool is the best, as that is something each organization must decide based on its own needs. However, we can (and would love to) assist you in taking off and integrating your ETL or ELT tool of choice with Airflow, the absolute best workflow manager. In the long run, the orchestrator is much more important than the ETL tool.

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