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Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala.The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Apache Samza is an open-source, near-realtime, asynchronous computational framework for stream processing developed by the Apache Software Foundation in Scala and Java. It has been developed in conjunction with Apache Kafka. Both were originally developed by LinkedIn. [2]
By way of illustration, the following code fragments demonstrate detection of patterns within event streams. The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams ...
The term "stream" is used in a number of similar ways: "Stream editing", as with sed, awk, and perl. Stream editing processes a file or files, in-place, without having to load the file(s) into a user interface. One example of such use is to do a search and replace on all the files in a directory, from the command line.
On April 30, 2015 version 1.0.0 of Reactive Streams for the JVM was released, [5] [6] [11] including Java API, [12] a textual specification, [13] a TCK and implementation examples. It comes with a multitude of compliant implementations verified by the TCK for 1.0.0, listed in alphabetical order: [11] Akka Streams [14] [15] MongoDB [16] Ratpack [17]
Example named components are shown. The speed layer processes data streams in real time and without the requirements of fix-ups or completeness. This layer sacrifices throughput as it aims to minimize latency by providing real-time views into the most recent data.
Apache Flink includes two core APIs: a DataStream API for bounded or unbounded streams of data and a DataSet API for bounded data sets. Flink also offers a Table API, which is a SQL-like expression language for relational stream and batch processing that can be easily embedded in Flink's DataStream and DataSet APIs.
StreamSQL extends the type system of SQL to support streams in addition to tables. Several new operations are introduced to manipulate streams. Selecting from a stream - A standard SELECT statement can be issued against a stream to calculate functions (using the target list) or filter out unwanted tuples (using a WHERE clause). The result will ...