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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.

Pros of Google Cloud Dataflow

Pros of Apache Spark

1
Unified batch and stream processing
1
Autoscaling
1
Fully managed

56
Open-source
45
Fast and Flexible
7
One platform for every big data problem
6
Easy to install and to use
6
Great for distributed SQL like applications
3
Works well for most Datascience usecases
2
Machine learning libratimery, Streaming in real
2
In memory Computation
0
Interactive Query

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Cons of Google Cloud Dataflow

Cons of Apache Spark

No cons available

0
Speed

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- 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.

What companies use Google Cloud Dataflow?

What companies use Apache Spark?

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What tools integrate with Google Cloud Dataflow?

What tools integrate with Apache Spark?

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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.

Akutan

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.

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Google Cloud Data Fusion

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

See all alternatives

Interest over time

Google Cloud Dataflow vs Apache Spark