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Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. [ 2 ]
Pinot supports near real-time ingestion from streams such as Kafka, AWS Kinesis and batch ingestion from sources such as Hadoop, S3, Azure, GCS. Like most other OLAP datastores and data warehousing solutions, Pinot supports a SQL-like query language that supports selection, aggregation, filtering, group by, order by, distinct queries on data.
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]
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.
AWS launches identity and access management (IAM) – Preview Beta. [42] 2010: November: Product: Amazon announces that Amazon.com has migrated its retail web services to AWS. [43] 2010: December 5: Product (Internet delivery) AWS launches Amazon Route 53, a scalable and highly available Domain Name System that can be accessed via programmatic ...
Sequence diagram for depicting the Message Broker pattern. A message broker (also known as an integration broker or interface engine [1]) is an intermediary computer program module that translates a message from the formal messaging protocol of the sender to the formal messaging protocol of the receiver.
Flink offers ready-built source and sink connectors with Apache Kafka, Amazon Kinesis, [19] HDFS, Apache Cassandra, and more. [ 16 ] Flink programs run as a distributed system within a cluster and can be deployed in a standalone mode as well as on YARN, Mesos, Docker-based setups along with other resource management frameworks.
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...