Apache Spark vs StreamSets: What are the differences?
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.
What is StreamSets? Where DevOps Meets Data Integration. The industry's first data operations platform for full life-cycle management of data in motion.
Apache Spark and StreamSets can be primarily classified as "Big Data" tools.
Some of the features offered by Apache Spark are:
- 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
On the other hand, StreamSets provides the following key features:
- Build Batch & Streaming Pipelines in Hours
- Map and Monitor Runtime Performance
- Protect Sensitive Data as it Arrives
Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
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What is Apache Spark?
What is StreamSets?
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