Stitch vs Panoply: What are the differences?
Developers describe Stitch as "All your data. In your data warehouse. In minutes". 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. On the other hand, Panoply is detailed as "Collect, combine, and integrate all your data with any analytics tools". It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.
Stitch and Panoply belong to "Big Data as a Service" category of the tech stack.
Some of the features offered by Stitch are:
- 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
On the other hand, Panoply provides the following key features:
- Data warehouse
- Business Intelligence
- Optimized Query Engine
What is Panoply?
What is Stitch?
Need advice about which tool to choose?Ask the StackShare community!
Why do developers choose Panoply?
What are the cons of using Panoply?
What are the cons of using Stitch?
What companies use Panoply?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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.