HortonWorks Data Platform vs Google Cloud Dataproc

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

HortonWorks Data Platform is a Ai Tools & Services solution with tags like hadoop, big-data, analytics.

It boasts features such as Distributed storage and processing using Hadoop, Real-time data processing with Storm, Data governance and security, Simplified management and monitoring, Integration with R, Python, Spark and more and pros including Open source and free, Scalable and flexible, Supports wide variety of workloads, Enterprise-grade security and governance, Large ecosystem of integrations.

On the other hand, Google Cloud Dataproc is a Ai Tools & Services product tagged with hadoop, spark, big-data, analytics.

Its standout features include Managed Spark and Hadoop clusters, Integrated with other GCP services, Autoscaling clusters, GPU support, Integrated monitoring and logging, and it shines with pros like Fast and easy cluster deployment, Fully managed so no ops work needed, Cost efficient, Integrates natively with other GCP services.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

HortonWorks Data Platform

HortonWorks Data Platform

HortonWorks Data Platform (HDP) is an open source distributed data management platform based on Apache Hadoop. It provides scalable and flexible data storage and processing for big data workloads.

Categories:
hadoop big-data analytics

HortonWorks Data Platform Features

  1. Distributed storage and processing using Hadoop
  2. Real-time data processing with Storm
  3. Data governance and security
  4. Simplified management and monitoring
  5. Integration with R, Python, Spark and more

Pricing

  • Open Source
  • Subscription-Based

Pros

Open source and free

Scalable and flexible

Supports wide variety of workloads

Enterprise-grade security and governance

Large ecosystem of integrations

Cons

Complex to set up and manage

Requires expertise in Hadoop and big data

Not as user friendly as some alternatives

Limited support options


Google Cloud Dataproc

Google Cloud Dataproc

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.

Categories:
hadoop spark big-data analytics

Google Cloud Dataproc Features

  1. Managed Spark and Hadoop clusters
  2. Integrated with other GCP services
  3. Autoscaling clusters
  4. GPU support
  5. Integrated monitoring and logging

Pricing

  • Pay-As-You-Go

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