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.