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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.
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.
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]
It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). Pinot is designed to scale horizontally. Mondrian OLAP server is an open-source OLAP server written in Java. It supports the MDX query language, the XML for Analysis and the olap4j interface specifications.
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.
Apache Accumulo is a highly scalable sorted, distributed key-value store based on Google's Bigtable. [2] It is a system built on top of Apache Hadoop, Apache ZooKeeper, and Apache Thrift.
CloudStack is open-source Infrastructure-as-a-Service cloud computing software for creating, managing, and deploying infrastructure cloud services.It uses existing hypervisor platforms for virtualization, such as KVM, VMware vSphere, including ESXi and vCenter, XenServer/XCP and XCP-ng.
Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, [9] AWS Batch for offline batch processing, [9] [10] or Amazon Kinesis for real-time processing.