Apache Spark vs Kylo: 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, Kylo is detailed as "Open-source data lake management software platform". It is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects.
Apache Spark and Kylo can be categorized 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, Kylo provides the following key features:
- Self-service data ingest with data cleansing, validation, and automatic profiling
- Wrangle data with visual sql and an interactive transform through a simple user interface
- Search and explore data and metadata, view lineage, and profile statistics
Apache Spark and Kylo are both open source tools. It seems that Apache Spark with 24.2K GitHub stars and 20.5K forks on GitHub has more adoption than Kylo with 744 GitHub stars and 358 GitHub forks.
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