Most businesses have data stored in a variety of locations, from in-house databases to SaaS platforms. To get a full picture of their finances and operations, they pull data from all those sources into a data warehouse or data lake and run analytics against it. But they don't want to build and maintain their own data pipelines.
Fortunately, it’s not necessary to code everything in-house. Here's an comparison of two such tools, head to head.
About Apache Airflow
Apache Airflow is an open source project that lets developers orchestrate workflows to extract, transform, load, and store data.
Stitch Data Loader is a cloud-based platform for ETL — extract, transform, and load. More than 3,000 companies use Stitch to move billions of records every day from SaaS applications and databases into data warehouses and data lakes, where it can be analyzed with BI tools. Stitch is a Talend company and is part of the Talend Data Fabric.
|Focus||Orchestration, scheduling, workflows||Data ingestion, ELT|
|Database replication||Only via plugins||Full table; incremental via change data capture or SELECT/replication keys|
|SaaS sources||Only via plugins||More than 100|
|Ability for customers to add new data sources||Yes||Yes|
|Connects to data warehouses? Data lakes?||Yes / Yes||Yes / Yes|
|Purchase process||Free to download and use||Options for self-service or talking with sales. Also available from the AWS store.|
|Compliance, governance, and security certifications||None||HIPAA, GDPR, SOC 2|
|Data sharing||Yes, via plugins||Yes, through Talend Data Fabric|
|Vendor lock-in||Month to month or annual contracts. Open source integrations|
|Developer tools||Experimental REST API||Import API, Stitch Connect API for integrating Stitch with other platforms, Singer open source project|
Let's dive into some of the details of each platform.
Apache Airflow is a powerful tool for authoring, scheduling, and monitoring workflows as directed acyclic graphs (DAG) of tasks. A DAG is a topological representation of the way data flows within a system. Airflow manages execution dependencies among jobs (known as operators in Airflow parlance) in the DAG, and programmatically handles job failures, retries, and alerting. Developers can write Python code to transform data as an action in a workflow.
Stitch is an ELT product. Within the pipeline, Stitch does only transformations that are required for compatibility with the destination, such as translating data types or denesting data when relevant. Stitch is part of Talend, which also provides tools for transforming data either within the data warehouse or via external processing engines such as Spark and MapReduce. Transformations can be defined in SQL, Python, Java, or via graphical user interface.
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Connectors: Data sources and destinations
Each of these tools supports a variety of data sources and destinations.
Airflow orchestrates workflows to extract, transform, load, and store data. It run tasks, which are sets of activities, via operators, which are templates for tasks that can by Python functions or external scripts. Developers can create operators for any source or destination. In addition, Airflow supports plugins that implement operators and hooks — interfaces to external platforms. The Airflow community has built plugins for databases like MySQL and Microsoft SQL Server and SaaS platforms such as Salesforce, Stripe, and Facebook Ads.
Stitch supports more than 100 database and SaaS integrations as data sources, and eight data warehouse and data lake destinations. Customers can contract with Stitch to build new sources, and anyone can add a new source to Stitch by developing it according to the standards laid out in Singer, an open source toolkit for writing scripts that move data. Singer integrations can be run independently, regardless of whether the user is a Stitch customer. Running Singer integrations on Stitch’s platform allows users to take advantage of Stitch's monitoring, scheduling, credential management, and autoscaling features.
Support, documentation, and training
Data integration tools can be complex, so vendors offer several ways to help their customers. Online documentation is the first resource users often turn to, and support teams can answer questions that aren't covered in the docs. Vendors of the more complicated tools may also offer training services.
The open source community provides Airflow support through a Slack community. Documentation includes quick start and how-to guides. Other than a tutorial on the Apache website there are no training resources.
Stitch provides in-app chat support to all customers, and phone support is available for Enterprise customers. Support SLAs are available. Documentation is comprehensive and is open source — anyone can contribute additions and improvements or repurpose the content. Stitch does not provide training services.
Airflow is free and open source, licensed under Apache License 2.0.
Stitch has pricing that scales to fit a wide range of budgets and company sizes. All new users get an unlimited 14-day trial. Standard plans range from $100 to $1,250 per month depending on scale, with discounts for paying annually. Enterprise plans for larger organizations and mission-critical use cases can include custom features, data volumes, and service levels, and are priced individually.
Get started now
Which tool is better overall? That's something every organization has to decide based on its unique requirements, but we can help you get started. Sign up now for a free trial of Stitch.