What is Google Dataproc and How does it work?
How to run Hadoop-based Clusters and Jobs in the Google Cloud
Google Cloud Dataproc is a comprehensively managed cloud service designed for the execution of Apache Hadoop and Apache Spark workloads. Its purpose is to simplify the process of running substantial Big Data workloads on the Google Cloud Platform by automating the configuration and management of cluster resources. This service also includes built-in integration with various other GCP services, such as Google Cloud Storage and BigQuery, and offers inherent support for popular Big Data tools and frameworks like Apache Hive, Pig, and HBase.
While one of Dataproc’s key features is its ability to automatically create and configure cluster resources, thus reducing the need for manual user setup and infrastructure configuration, customers might wonder if it’s truly worthwhile.
For companies that previously managed Hadoop within their own data centers, Google presents a fantastic solution that could potentially simplify deployment and reduce costs when transitioning to Google Cloud. However, one should carefully consider whether it might also be worthwhile to migrate existing Hadoop architectures to platforms like BigQuery. The benefits include streamlined provisioning, along with a multitude of features such as BigQuery ML, excellent integration with Looker Studio, and new additions like Google BigLake.
How does it work?
Dataproc is a managed Spark and Hadoop service that lets you take advantage of open-source data tools for batch processing, querying, streaming, and machine learning.
Dataproc’s automation assists in swiftly creating clusters, managing them seamlessly, and economizing by deactivating clusters when not in use. This leads to reduced time and cost expenditures on administration.
It disintegrates storage and computing. For instance, if an external application is transmitting logs that require analysis, you store them in a data source. Data is retrieved from Cloud Storage (GCS) and processed by Dataproc. Subsequently, the processed data is stored back in GCS, BigQuery, or Bigtable.
As storage is distinct, for a cluster that persists over an extended period, you might opt for a one-cluster-per-job approach. Alternatively, for cost efficiency, you could employ transient clusters that are organized and chosen based on labels. Lastly, you can tailor the memory, CPU, and disk resources to precisely match your application’s requirements.
