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InfoQ Homepage News Hadoop-as-a-Service Provider Qubole Now Runs on Google Compute Engine

Hadoop-as-a-Service Provider Qubole Now Runs on Google Compute Engine

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Qubole, a managed Hadoop-as-a-Service offering is now available on Google Compute Engine (GCE). Qubole was so far only available on Amazon’s AWS and this announcement follows only a few days after Google releasing GCE into general availability.

Community reactions were by and large positive and it seems people consider the Big Data theme as a potential killer app for GCE. Alex Popescu of DataStax puts it like this:

If you look at these, you’ll notice a theme: covering data from every angle; Cassandra/DSE from DataStax for OLTP, DataTorrent for stream processing, Qubole for Hadoop, MapR for their Hadoop-like solution. I can see this continuing for a while and making Google Compute Engine a strong competitor for Amazon Web Services.

With Hadoop-as-a-Service (HaaS, also known as Hadoop in the cloud) come different options:

  • Rolling your own deployment, that is, installing Apache Hadoop or one of the distributions (Cloudera, Hortonworks, MapR) in an IaaS offering, such as GCE or EC2. This allows for fine-grained control over what is running but also comes with deployment and management complexity.
  • Pre-packaged services such as Amazon’s EMR or Savvis’ Big Data offering that help with reduced deployment complexity and offer mid-level control over installed services.
  • Managed HaaS such as Qubole or Mortar, promising reduced deployment and management complexity.

The key differences of HaaS versus on-premise deployments are around elasticity, spot pricing, separation between compute and storage (for example, eventually consistent object stores such as Amazon’s S3 or Google’s Cloud Storage, and enhanced security standards. Managed HaaS offerings such as Qubole are often used in development cases, for evaluation and testing, short-running analysis jobs and to realise hybrid cloud setups. They do, however, also come with their own limitations:

  • Getting data into the cloud and getting it out again has its own price tag.
  • There may be privacy and data protection issues stemming from legal requirements that prevent or limit the use cases.
  • The TCO of a 24/7 operation has to be calculated through on a case-by-case basis.
  • There is a general mismatch between Hadoop, Hive, etc. on the one hand and the eventually consistent object stores on the other.

Ashish Thusoo and Joydeep Sen Sarma gathered experience running Hadoop and Hive during their tenure at Facebook, where they ran a data infrastructure team. Then, in June 2012, they launched Qubole that completed a $7 million Series A funding round in April 2013. Joydeep gave a deep-dive on the challenges they faced implementing their HaaS offering and provided insights on the internals in his Hive London Meetup talk Cloud Friendly Hadoop & Hive. Further, Christian Prokopp (Data Scientist at Rangespan) recently wrote up a detailed rundown and comparison of Qubole and EMR.

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