Google Cloud Dataproc vs Cloudera CDH

Struggling to choose between Google Cloud Dataproc and Cloudera CDH? Both products offer unique advantages, making it a tough decision.

Google Cloud Dataproc is a Ai Tools & Services solution with tags like hadoop, spark, big-data, analytics.

It boasts features such as Managed Spark and Hadoop clusters, Integrated with other GCP services, Autoscaling clusters, GPU support, Integrated monitoring and logging and pros including Fast and easy cluster deployment, Fully managed so no ops work needed, Cost efficient, Integrates natively with other GCP services.

On the other hand, Cloudera CDH is a Ai Tools & Services product tagged with hadoop, hdfs, yarn, spark, hive, hbase, impala, kudu.

Its standout features include HDFS - Distributed and scalable file system, YARN - Cluster resource management, MapReduce - Distributed data processing, Hive - SQL interface for querying data, HBase - Distributed column-oriented database, Impala - Massively parallel SQL query engine, Spark - In-memory cluster computing framework, Kudu - Fast analytics on fast data, Cloudera Manager - Centralized management and monitoring, and it shines with pros like Open source and free to use, Includes many popular Hadoop ecosystem projects, Centralized management and monitoring, Pre-configured and tested combinations of components, Active development and support from Cloudera.

Feature	Google Cloud Dataproc	Cloudera CDH
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

Google Cloud Dataproc

Description: Google Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simple, cost-efficient way.

Cloudera CDH

Description: Cloudera CDH (Cloudera Distribution Including Apache Hadoop) is an open source data platform that combines Hadoop ecosystem components like HDFS, YARN, Spark, Hive, HBase, Impala, Kudu, and more into a single managed platform.

Key Features Comparison

Google Cloud Dataproc Features

• Managed Spark and Hadoop clusters
• Integrated with other GCP services
• Autoscaling clusters
• GPU support
• Integrated monitoring and logging

Cloudera CDH Features

• HDFS - Distributed and scalable file system
• YARN - Cluster resource management
• MapReduce - Distributed data processing
• Hive - SQL interface for querying data
• HBase - Distributed column-oriented database
• Impala - Massively parallel SQL query engine
• Spark - In-memory cluster computing framework
• Kudu - Fast analytics on fast data
• Cloudera Manager - Centralized management and monitoring

Pros & Cons Analysis

Google Cloud Dataproc

Pros

• Fast and easy cluster deployment
• Fully managed so no ops work needed
• Cost efficient
• Integrates natively with other GCP services

Cons

• Only supports Spark and Hadoop workloads
• Less flexibility than DIY Hadoop cluster
• Lock-in to GCP

Cloudera CDH

Pros

• Open source and free to use
• Includes many popular Hadoop ecosystem projects
• Centralized management and monitoring
• Pre-configured and tested combinations of components
• Active development and support from Cloudera

Cons

• Can be complex to configure and manage
• Requires dedicated hardware/cluster
• Steep learning curve for Hadoop and related technologies
• Not as flexible as rolling your own Hadoop distribution

Pricing Comparison

Google Cloud Dataproc

• Pay-As-You-Go

Cloudera CDH

• Open Source
• Subscription-Based (Cloudera Enterprise)