Hadoop as a Service without vendor lock-in
April 02, 2018
Hadoop as a Service without vendor lock-in
Score 8 out of 10
Vetted Review
Verified User
Overall Satisfaction with Qubole
Qubole was used across the company to ease migration strategies from an on-premise Hadoop environment into the cloud. The end place for the data was between Amazon services such as S3 and RDS, but the initial goal was to use multiple clouds, as some parts of the company were using Google's BigQuery.
From what I've seen, Qubole abstracts away the setup, scalability, and installation of many Hadoop services by providing an a la carte offering of big data processing services from query engines of Hive, Spark, and Presto to useful UI tools of the query editors and Zeppelin Notebooks.
From what I've seen, Qubole abstracts away the setup, scalability, and installation of many Hadoop services by providing an a la carte offering of big data processing services from query engines of Hive, Spark, and Presto to useful UI tools of the query editors and Zeppelin Notebooks.
- From a UI perspective, I find Qubole's closest comparison to Cloudera's HUE; it provides a one-stop shop for all data browsing and querying needs.
- Auto scaling groups and auto-terminating clusters provides cost savings for idle resources.
- Qubole fits itself well into the open-source data science market by providing a choice of tools that aren't tied to a specific cloud vendor.
- Providing an open selection of all cloud provider instance types with no explanation as to their ideal use cases causes too much confusion for new users setting up a new cluster. For example, not everyone knows that Amazon's R or X-series models are memory optimized, while the C and M-series are for general computation.
- I would like to see more ETL tools provided other than DistCP that allow one to move data between Hadoop Filesystems.
- From the cluster administration side, onboarding of new users for large companies seems troublesome, especially when trying to create individual cluster per team within the company. Having the ability to debug and share code/queries between users of other teams / clusters should also be possible.
- We like to say that Qubole has allowed for "data democratization", meaning that each team is responsible for their own set of tooling and use cases rather than being limited by versions established by products such as Hortonworks HDP or Cloudera CDH
- One negative impact is that users have over-provisioned clusters without realizing it, and end up paying for it. When setting up a new cluster, there are too many choices to pick from, and data scientists may not understand the instance types or hardware specs for the datasets they need to operate on.
Qubole was decided on by upper management rather than these competitive offerings. I find that Databricks has a better Spark offering compared to Qubole's Zeppelin notebooks.