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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 is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases).
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]
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.
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.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
The UKV [45] project allows users to use RocksDB on par with LevelDB as the underlying key-value store. It represents a shared abstraction for create, read, update and delete (CRUD) operations common to every storage engine. It augments it with structured bindings for several high-level languages, including Python, Java, and Go.
input_lines = LOAD '/tmp/my-copy-of-all-pages-on-internet' AS (line: chararray);-- Extract words from each line and put them into a pig bag-- datatype, then flatten the bag to get one word on each row words = FOREACH input_lines GENERATE FLATTEN (TOKENIZE (line)) AS word; -- filter out any words that are just white spaces filtered_words = FILTER words BY word MATCHES '\\w+';-- create a group ...