Databricks vs Qubole: What are the differences?
What is Databricks? A unified analytics platform, powered by Apache Spark. Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
What is Qubole? 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.
Databricks and Qubole are primarily classified as "General Analytics" and "Big Data as a Service" tools respectively.
Some of the features offered by Databricks are:
- Built on Apache Spark and optimized for performance
- Reliable and Performant Data Lakes
- Interactive Data Science and Collaboration
On the other hand, Qubole provides the following key features:
- Intuitive GUI
- Optimized Hive
- Improved S3 Performance
Pinterest, Snowplow Analytics, and SaleCycle are some of the popular companies that use Qubole, whereas Databricks is used by Auto Trader, Snowplow Analytics, and Fairygodboss. Qubole has a broader approval, being mentioned in 3 company stacks & 9 developers stacks; compared to Databricks, which is listed in 7 company stacks and 4 developer stacks.
What is Databricks?
What is Qubole?
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Why do developers choose Databricks?
Why do developers choose Qubole?
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What are the cons of using Databricks?
What are the cons of using Qubole?
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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