Google Cloud Dataflow vs Google Cloud Dataproc

June 03, 2023 | Author: Michael Stromann
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Google Cloud Dataflow
Build, deploy, and run data processing pipelines that scale to solve your key business challenges. Google Cloud Dataflow enables reliable execution for large scale data processing scenarios such as ETL, analytics, real-time computation, and process orchestration.
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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.
Google Cloud Dataflow and Google Cloud Dataproc are two data processing services provided by Google Cloud, each with its own key features and use cases.

Google Cloud Dataflow is a fully managed service for real-time and batch data processing. It allows you to build and execute data pipelines using a serverless model, abstracting away the underlying infrastructure. Dataflow provides a unified programming model for both batch and streaming data, making it easy to process and analyze large datasets in a distributed manner. It offers built-in connectors to various data sources and sinks, along with support for data transformation and enrichment. Dataflow is suitable for scenarios where you need a scalable, managed solution for processing data in real-time or batch mode, such as data analytics, ETL (Extract, Transform, Load) processes, and real-time event processing.

Google Cloud Dataproc, on the other hand, is a managed service for running Apache Spark and Apache Hadoop clusters. It provides a fully managed environment for big data processing, allowing you to create and manage Spark and Hadoop clusters with ease. Dataproc offers features like auto-scaling, cluster management, and integration with other Google Cloud services. It is particularly useful for running large-scale data processing and analytics workloads, leveraging the power of Spark and Hadoop for distributed processing. With Dataproc, you have fine-grained control over the cluster configuration and can use various data processing frameworks and tools available in the Hadoop and Spark ecosystem.

See also: Top 10 Big Data platforms
Google Cloud Dataflow vs Google Cloud Dataproc 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.


2015. Google partners with Cloudera to bring Cloud Dataflow to Apache Spark



Google has announced a collaboration with Cloudera, the Hadoop specialists, to integrate its Cloud Dataflow programming model into Apache's Spark data processing engine. By bringing Cloud Dataflow to Spark, developers gain the ability to create and monitor data processing pipelines without the need to manage the underlying data processing cluster. This service originated from Google's internal tools for processing large datasets at a massive scale on the internet. However, not all data processing tasks are identical, and sometimes it becomes necessary to run tasks in different environments such as the cloud, on-premises, or on various processing engines. With Cloud Dataflow, data analysts can utilize the same system to create pipelines, regardless of the underlying architecture they choose to deploy them on.

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