Qubole vs Stitch: What are the differences?
Developers describe Qubole as "Prepare, integrate and explore Big Data in the cloud (Hive, MapReduce, Pig, Presto, Spark and Sqoop)". Qubole is a cloud based service that makes big data easy for analysts and data engineers. On the other hand, Stitch is detailed 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.
Qubole and Stitch belong to "Big Data as a Service" category of the tech stack.
Some of the features offered by Qubole are:
- Intuitive GUI
- Optimized Hive
- Improved S3 Performance
On the other hand, Stitch provides the following key 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
"Simple UI and autoscaling clusters" is the primary reason why developers consider Qubole over the competitors, whereas "3 minutes to set up" was stated as the key factor in picking Stitch.
What is Qubole?
What is Stitch?
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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.
We ultimately migrated our Hadoop jobs to Qubole, a rising player in the Hadoop as a Service space. Given that EMR had become unstable at our scale, we had to quickly move to a provider that played well with AWS (specifically, spot instances) and S3. Qubole supported AWS/S3 and was relatively easy to get started on. After vetting Qubole and comparing its performance against alternatives (including managed clusters), we decided to go with Qubole