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What is Stitch?

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.
Stitch is a tool in the Big Data as a Service category of a tech stack.

Who uses Stitch?

35 companies reportedly use Stitch in their tech stacks, including Postman, tumblbug-com, and Peloton.

43 developers on StackShare have stated that they use Stitch.

Stitch Integrations

Google Analytics, MySQL, MongoDB, PostgreSQL, and Amazon S3 are some of the popular tools that integrate with Stitch. Here's a list of all 68 tools that integrate with Stitch.
Public Decisions about Stitch

Here are some stack decisions, common use cases and reviews by companies and developers who chose Stitch in their tech stack.

Ankit Sobti

Looker , Stitch , Amazon Redshift , dbt

We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

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Ajit Parthan
Shared insights

Multiple systems means there is a requirement to cart data across them.

Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box Stitch

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Stitch's Features

  • Connect to your ecosystem of data sources - UI allows you to configure your data pipeline in a way that balances data freshness with cost and production database load
  • Replication frequency - Choose full or incremental loads, and determine how often you want them to run - from every minute, to once every 24 hours
  • Data selection - Configure exactly what data gets replicated by selecting the tables, fields, collections, and endpoints you want in your warehouse
  • API - With the Stitch API, you're free to replicate data from any source. Its REST API supports JSON or Transit, and recognizes your schema based on the data you send.
  • Usage dashboard - Access our simple UI to check usage data like the number of rows synced by data source, and how you're pacing toward your monthly row limit
  • Email alerts - Receive immediate notifications when Stitch encounters issues like expired credentials, integration updates, or warehouse errors preventing loads
  • Warehouse views - By using the freshness data provided by Stitch, you can build a simple audit table to track replication frequency
  • Scalable - Highly Scalable Stitch handles all data volumes with no data caps, allowing you to grow without the possibility of an ETL failure
  • Transform nested JSON - Stitch provides automatic detection and normalization of nested document structures into relational schemas
  • Complete historical data - On your first sync, Stitch replicates all available historical data from your database and SaaS tools. No database dump necessary.

Stitch Alternatives & Comparisons

What are some alternatives to Stitch?
Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Amazon EMR
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)鈥攏o infrastructure to manage and no knobs to turn.
See all alternatives

Stitch's Followers
74 developers follow Stitch to keep up with related blogs and decisions.