Google Cloud Dataproc vs Qubole

June 03, 2023 | Author: Michael Stromann
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Google Cloud Dataproc
Google Cloud Dataproc is a managed Hadoop MapReduce, Spark, Pig, and Hive service designed to easily and cost effectively process big datasets. You can quickly create managed clusters of any size and turn them off when you are finished, so you only pay for what you need. Cloud Dataproc is integrated across several Google Cloud Platform products, so you have access to a simple, powerful, and complete data processing platform.
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Qubole
Qubole is a Big Data as a Service (BDaas) Platform Running on Leading Cloud Offerings Like AWS. Qubole enables you to utilize a variety of Cloud Databases and Sources, including S3, MySQL, Postgres, Oracle, RedShift, MongoDB, Vertica, Omniture, Google Analytics, and your on-premise data
Google Cloud Dataproc and Qubole are two cloud-based data processing platforms with their own unique features and capabilities.

Google Cloud Dataproc is a fully managed service for running Apache Spark and Apache Hadoop clusters. It allows users to create and manage clusters of any size quickly and easily, with the ability to auto-scale based on workload demands. Dataproc integrates seamlessly with other Google Cloud services and provides extensive monitoring and logging capabilities. It is well-suited for organizations looking for a scalable and managed environment to process large volumes of data using popular big data processing frameworks.

Qubole, on the other hand, is a cloud-native data platform that offers a comprehensive suite of data processing and analytics tools. It supports a wide range of data processing engines, including Apache Spark, Presto, and Hive, providing users with flexibility in choosing the right engine for their specific use cases. Qubole also offers advanced data governance and security features, along with automated optimization and workload management capabilities. It is suitable for organizations seeking a comprehensive data platform that covers various data processing engines and provides additional features for data governance and optimization.

See also: Top 10 Big Data platforms
Google Cloud Dataproc vs Qubole in our news:

2015. Google launched new managed Big Data service Cloud Dataproc



Google is expanding its portfolio of big data services on the Google Cloud Platform with the introduction of Cloud Dataproc. This new service fills the gap between directly managing the Spark data processing engine or Hadoop framework on virtual machines and utilizing a fully managed service like Cloud Dataflow for orchestrating data pipelines on Google's platform. With Cloud Dataproc, users can quickly deploy a Hadoop cluster in less than 90 seconds, which is considerably faster than other available services. Google charges only 1 cent per virtual CPU/hour within the cluster, in addition to the standard costs associated with running virtual machines and storing data. Users can also incorporate Google's more affordable preemptible instances into their clusters to reduce compute costs. Billing is calculated per minute, with a minimum charge of 10 minutes. Thanks to the rapid cluster deployment capabilities of Dataproc, users can easily create ad-hoc clusters when necessary, while Google takes care of the administrative tasks.


2014. Big Data as a Service company Qubole raises $13 million



Hadoop-as-a-service startup, Qubole, has secured $13 million in a series B venture capital funding round. Qubole operates on the Amazon Web Services cloud and is also compatible with Google Compute Engine. It functions as a cloud-native Hadoop service, featuring a user-friendly graphical interface, connectors to various data sources (including cloud object stores), and takes advantage of cloud capabilities such as autoscaling and spot pricing for computing resources. Qubole stands out by offering optimized versions of Hive and other MapReduce-based tools, but it also enables data analysis through the use of Facebook's Presto SQL-on-Hadoop engine. Additionally, Qubole is developing a service centered around the Apache Spark framework, which has gained significant popularity due to its speed and efficiency.

Author: Michael Stromann
Michael is an expert in IT Service Management, IT Security and software development. With his extensive experience as a software developer and active involvement in multiple ERP implementation projects, Michael brings a wealth of practical knowledge to his writings. Having previously worked at SAP, he has honed his expertise and gained a deep understanding of software development and implementation processes. Currently, as a freelance developer, Michael continues to contribute to the IT community by sharing his insights through guest articles published on several IT portals. You can contact Michael by email stromann@liventerprise.com