Google Cloud Dataflow
Google Cloud Dataflow

70
48
0
Apache Spark
Apache Spark

975
749
98
Add tool

Google Cloud Dataflow vs Apache Spark: What are the differences?

What is Google Cloud Dataflow? A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Google Cloud Dataflow and Apache Spark are primarily classified as "Real-time Data Processing" and "Big Data" tools respectively.

Some of the features offered by Google Cloud Dataflow are:

  • Fully managed
  • Combines batch and streaming with a single API
  • High performance with automatic workload rebalancing Open source SDK

On the other hand, Apache Spark provides the following key features:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

Apache Spark is an open source tool with 22.5K GitHub stars and 19.4K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Google Cloud Dataflow is used by Spotify, Resultados Digitais, and Handshake. Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Google Cloud Dataflow, which is listed in 32 company stacks and 8 developer stacks.

- No public GitHub repository available -

What is Google Cloud Dataflow?

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

What is Apache Spark?

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Want advice about which of these to choose?Ask the StackShare community!

Why do developers choose Google Cloud Dataflow?
Why do developers choose Apache Spark?
    Be the first to leave a pro

    Sign up to add, upvote and see more prosMake informed product decisions

    What are the cons of using Google Cloud Dataflow?
    What are the cons of using Apache Spark?
      Be the first to leave a con
      What companies use Google Cloud Dataflow?
      What companies use Apache Spark?

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Google Cloud Dataflow?
      What tools integrate with Apache Spark?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to Google Cloud Dataflow and Apache Spark?
      Kafka
      Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
      Beam
      A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
      Amazon Kinesis
      Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.
      See all alternatives
      Decisions about Google Cloud Dataflow and Apache Spark
      No stack decisions found
      Interest over time
      Reviews of Google Cloud Dataflow and Apache Spark
      No reviews found
      How developers use Google Cloud Dataflow and Apache Spark
      Avatar of Wei Chen
      Wei Chen uses Apache SparkApache Spark

      Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.

      Avatar of Ralic Lo
      Ralic Lo uses Apache SparkApache Spark

      Used Spark Dataframe API on Spark-R for big data analysis.

      Avatar of Kalibrr
      Kalibrr uses Apache SparkApache Spark

      We use Apache Spark in computing our recommendations.

      Avatar of BrainFinance
      BrainFinance uses Apache SparkApache Spark

      As a part of big data machine learning stack (SMACK).

      Avatar of Dotmetrics
      Dotmetrics uses Apache SparkApache Spark

      Big data analytics and nightly transformation jobs.

      How much does Google Cloud Dataflow cost?
      How much does Apache Spark cost?
      Pricing unavailable
      Pricing unavailable
      News about Google Cloud Dataflow
      More news
      News about Apache Spark
      More news