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 build everything in-house. We put together this ETL tool comparison guide to help you choose the product that’s the best fit for your business.

Overview

Azure Data Factory, AWS Data Pipeline, and Stitch are all popular platforms. Here's a side-by-side look at how they stack up against each other.

Focus ETL Data transfer Data ingestion, ELT
Database replication Full table; incremental via custom SELECT query Full table; incremental replication via timestamp field Full table; incremental via change data capture or SELECT/replication keys
SaaS sources About 20, with several more in preview None More than 100
Ability for customers to add new data sources No No Yes
Connects to data warehouses? Data lakes? Yes / Yes Yes / Yes Yes / Yes
Transparent pricing Yes Yes Yes
G2 customer satisfaction 4.6/5 Not rated 4.8/5
Support SLAs Yes Yes Available
Purchase process Options for self-service or talking with sales Self-service Options for self-service or talking with sales. Also available from the AWS store.
Compliance, governance, and security certifications HIPAA, GDPR, ISO 27001, others None HIPAA, GDPR, SOC 2
Data sharing No Yes Yes, through Talend Data Fabric
Vendor lock-in Month to month Month to month. No open source Month to month or annual contracts. Open source integrations
Developer tools REST API, .Net and Python SDKs AWS Data Pipeline API gives programmatic control over most Data Pipeline operations. SDKs are available for several languages. 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.

Transformations

Azure Data Factory

Azure Data Factory supports both pre- and post-load transformations. Users apply transformations either by using a GUI to map them, or in code using Power Query Online. Azure Data Factory supports a wide range of transformation functions.

AWS Data Pipeline

Data Pipeline supports preload transformations using SQL commands. You can create a pipeline graphically through a console, using the AWS command line interface (CLI) with a pipeline definition file in JSON format, or programmatically through API calls.

Stitch

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.

Azure Data Factory

Azure Data Factory integrates with about 80 data sources, including SaaS platforms, SQL and NoSQL databases, generic protocols, and various file types. It supports around 20 cloud and on-premises data warehouse and database destinations.

AWS Data Pipeline

Data Pipeline supports four types of what it calls data nodes as sources and destinations: DynamoDB, SQL, and Redshift tables and S3 locations. Data Pipeline doesn't support any SaaS data sources.

Stitch

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.

Azure Data Factory

Azure Data Factory provides support via online forums and an online support request form. Email and phone support are available. Documentation is comprehensive. Digital training materials are available.

AWS Data Pipeline

AWS provides online support through a ticketing system and a knowledgebase. Support tickets may get phone or chat responses. Documentation is comprehensive. Digital training materials are available.

Stitch

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.

Pricing

Azure Data Factory

Pricing for Azure Data Factory's data pipeline is calculated based on number of pipeline orchestration runs; compute-hours for flow execution and debugging; and number of Data Factory operations, such as pipeline monitoring.

AWS Data Pipeline

Data Pipeline pricing is based on how often your activities and preconditions are scheduled to run and whether they run on AWS or on-premises.

Stitch

Stitch has pricing that scales to fit a wide range of budgets and company sizes. All new users get an unlimited 14-day trial. After the trial, there's a free plan for smaller organizations and nonproduction workloads. 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 best 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.