Qubole, the big data-as-a-service company founded by the team the developed Facebook’s data infrastructure, has announced the addition of Apache Spark to the Qubole Data Service (QDS) platform. With the addition of Apache Spark, QDS broadens the types of workloads data scientists and data analysts can run on demand via the cloud, without the hassles, costs and risks of deploying Spark on-premises.
Qubole claims that data scientists can set up a Spark cluster in QDS in less than 15 minutes directly within the QDS web interface, and like all QDS services, the Spark feature auto-scales based on workload, ensuring the most efficient and cost-effective use of compute resources.
Qubole Data Service (QDS) is a self-service platform for big data analytics that runs on the three major public clouds: Amazon AWS, Google Compute Engine and Microsoft Azure. Among its key advantages, QDS automatically sets up and scales up a cluster to match the needs of the particular job, and then winds down nodes when they’re no longer needed. QDS is a fully managed big data offering that leverages the latest open source technologies, such as Apache Hadoop, Hive, Presto, Pig, Oozie, Sqoop and now Spark, to provide the only comprehensive, “everything as a service” data analytics platform complete with enterprise security features, an easy to use UI and built in data governance.
Joydeep Sen Sarma, co-founder and CTO of Qubole
Many organizations are evaluating Apache Spark for their own big data implementations, but deploying and maintaining Spark clusters can be tricky. By adding Spark to QDS, we’ve completely eliminated the barriers to taking full advantage of Spark for rapid data analytics and we’re giving customers the ability to select the best technology for their big data tasks, on the fly.
Krishna Gade, engineering manager at Pinterest
The addition of Apache Spark to QDS makes the platform even more valuable to Pinterest. Qubole empowers us to use the latest Big Data tools at petabyte scale without needing to invest in building out, maintaining and updating our own infrastructure. As a result, we can focus on extracting value from our data using the best technologies for the job, and on driving the business forward.
