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Kapacitor vs Faust: What are the differences?
Kapacitor: A real-time streaming data processing engine. It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.; Faust: A library for building streaming applications in Python. It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.
Kapacitor and Faust can be categorized as "Stream Processing" tools.
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What is Faust?
It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.
What is Kapacitor?
It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform.
It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.
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What companies use Faust?
What companies use Kapacitor?
What companies use Kapacitor?
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What tools integrate with Faust?
What tools integrate with Kapacitor?
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What are some alternatives to Faust and Kapacitor?
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Celery
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Apache Spark
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.
JUCE
It is a C++ framework for low-latency applications, with cross-platform GUI libraries to get your apps running on Mac OS X, Windows, Linux, iOS and Android.