Apache Spark vs Vespa: What are the differences?
Developers describe Apache Spark 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. On the other hand, Vespa is detailed as "Store, search, rank and organize big data". Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
Apache Spark and Vespa can be primarily classified as "Big Data" tools.
Apache Spark and Vespa are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Vespa with 2.85K GitHub stars and 339 GitHub forks.
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What is Apache Spark?
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