Apache Ignite vs Apache Spark: What are the differences?
Developers describe Apache Ignite as "An open-source distributed database, caching and processing platform *". It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale. On the other hand, *Apache Spark** is detailed as "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.
Apache Ignite belongs to "In-Memory Databases" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".
Some of the features offered by Apache Ignite are:
- Memory-Centric Storage
- Distributed SQL
- Distributed Key-Value
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 Ignite and Apache Spark are both open source tools. It seems that Apache Spark with 22.9K GitHub stars and 19.7K forks on GitHub has more adoption than Apache Ignite with 2.67K GitHub stars and 1.3K GitHub forks.
According to the StackShare community, Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Apache Ignite, which is listed in 4 company stacks and 4 developer stacks.
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What is Apache Ignite?
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