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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.
Stream-processing technologies typically used in this layer include Apache Kafka, Amazon Kinesis, Apache Storm, SQLstream, Apache Samza, Apache Spark, Azure Stream Analytics, Apache Flink. Output is typically stored on fast NoSQL databases., [6] [7] or as a commit log. [8]
Most (90%) of a stream processor's work is done on-chip, requiring only 1% of the global data to be stored to memory. This is where knowing the kernel temporaries and dependencies pays. Internally, a stream processor features some clever communication and management circuits but what's interesting is the Stream Register File (SRF). This is ...
Apache Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. [3] [4] Flink executes arbitrary dataflow programs in a data-parallel and pipelined (hence task parallel) manner. [5]
V7.5 [13] released July 28, 2017 introduced the kafkaloader, for consuming streaming events from Kafka directly into a database table or into a stored procedure for processing. V7.6 [14] (August 28, 2017) introduced User-Defined SQL Functions, allowing customers to write custom functions in Java and make them callable from a SQL statement.
Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing. [2] Beam Pipelines are defined using one of the provided SDKs and executed in one of the Beam’s supported runners (distributed processing back-ends) including Apache Flink, Apache Samza, Apache Spark, and Google Cloud Dataflow.
RabbitMQ is an open-source message-broker software (sometimes called message-oriented middleware) that originally implemented the Advanced Message Queuing Protocol (AMQP) and has since been extended with a plug-in architecture to support Streaming Text Oriented Messaging Protocol (STOMP), MQ Telemetry Transport (MQTT), and other protocols.
The MapR File System (MapR FS) is a clustered file system that supports both very large-scale and high-performance uses. [1] MapR FS supports a variety of interfaces including conventional read/write file access via NFS and a FUSE interface, as well as via the HDFS interface used by many systems such as Apache Hadoop and Apache Spark.