What is Qubole?
Who uses Qubole?
Why developers like Qubole?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Qubole in their tech stack.
By mid-2014, around the time of the Series F, Pinterest users had already created more than 30 billion Pins, and the company was logging around 20 terabytes of new data daily, with around 10 petabytes of data in S3. To drive personalization for its users, and to empower engineers to build big data applications quickly, the data team built a self-serve Hadoop platform.
To start, they decoupled compute from storage, which meant teams would have to worry less about loading or synchronizing data, allowing existing or future clusters to make use of the data across a single shared file system.
A centralized Hive metastore act as the source of truth. They chose Hive for most of their Hadoop jobs “primarily because the SQL interface is simple and familiar to people across the industry.”
Dependency management takes place across three layers: *** Baked AMIs, which are large slow-loading dependencies pre-loaded on images; **Automated Configurations (Masterless Puppets), which allows Puppet clients to “pull their configuration from S3 and set up a service that’s responsible for keeping S3 configurations in sync with the Puppet master;” and Runtime Staging on S3, which creates a working directory at runtime for each developer that pulls down its dependencies directly from S3.
Finally, they migrated their Hadoop jobs to Qubole, which “supported AWS/S3 and was relatively easy to get started on.”
Jobs that mention Qubole as a desired skillset
- Intuitive GUI
- Optimized Hive
- Improved S3 Performance
- Auto Scaling
- Spot Instance Pricing
- Managed Clusters
- Cloud Integration
- Cluster Lifecycle Management