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Hadoop HDFS VS Google Cloud Dataflow

Compare Hadoop HDFS VS Google Cloud Dataflow and see what are their differences


The Apache HDFS is a distributed file system that makes it possible to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes.

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Hadoop HDFS Landing Page
Hadoop HDFS Landing Page
Google Cloud Dataflow Landing Page
Google Cloud Dataflow Landing Page

Hadoop HDFS details

Categories
Big Data Stream Processing Big Data Tools
Website ibm.com  
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Google Cloud Dataflow details

Categories
Big Data Stream Processing Data Management
Website cloud.google.com  
Pricing URL-
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Platforms
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Hadoop HDFS videos

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Google Cloud Dataflow videos

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • - Apache Beam and Google Cloud Dataflow

Category Popularity beta

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What are some alternatives?

When comparing Hadoop HDFS and Google Cloud Dataflow, you can also consider the following products

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.β€ŽWhat is Apache Spark?

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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