doap_Pinot.rdf

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl"?>
<rdf:RDF xml:lang="en"
         xmlns="http://usefulinc.com/ns/doap#"
         xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
         xmlns:asfext="http://projects.apache.org/ns/asfext#"
         xmlns:foaf="http://xmlns.com/foaf/0.1/">
<!--
    Licensed to the Apache Software Foundation (ASF) under one or more
    contributor license agreements.  See the NOTICE file distributed with
    this work for additional information regarding copyright ownership.
    The ASF licenses this file to You under the Apache License, Version 2.0
    (the "License"); you may not use this file except in compliance with
    the License.  You may obtain a copy of the License at

         https://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
-->
  <Project rdf:about="https://pinot.apache.org">
    <created>2023-08-07</created>
    <license rdf:resource="https://spdx.org/licenses/Apache-2.0" />
    <name>Apache Pinot</name>
    <homepage rdf:resource="https://pinot.apache.org" />
    <asfext:pmc rdf:resource="https://pinot.apache.org" />
    <shortdesc>Apache Pinot is a real-time distributed OLAP datastore purpose-built for low-latency, high-throughput analytics.</shortdesc>
    <description>Apache Pinot is a real-time distributed online analytical processing (OLAP) datastore. Use Pinot to ingest and immediately query data from streaming or batch data sources (including, Apache Kafka, Amazon Kinesis, Hadoop HDFS, Amazon S3, Azure ADLS, and Google Cloud Storage).
Apache Pinot includes the following:
Ultra low-latency analytics even at extremely high throughput.
Columnar data store with several smart indexing and pre-aggregation techniques.
Scaling up and out with no upper bound.
Consistent performance based on the size of your cluster and an expected query per second (QPS) threshold.
It's perfect for user-facing real-time analytics and other analytical use cases, including internal dashboards, anomaly detection, and ad hoc data exploration.</description>
    <bug-database rdf:resource="https://github.com/apache/pinot/issues" />
    <mailing-list rdf:resource="https://docs.pinot.apache.org/community-1/community" />
    <download-page rdf:resource="https://pinot.apache.org/download" />
    <programming-language>Java</programming-language>
    <category rdf:resource="https://projects.apache.org/category/big-data" />
    <release>
      <Version>
        <name>1.1.0</name>
        <created>2024-03-24</created>
        <revision>1.1.0</revision>
      </Version>
    </release>
    <repository>
      <GitRepository>
        <location rdf:resource="https://gitbox.apache.org/repos/asf/pinot.git"/>
        <browse rdf:resource="https://github.com/apache/pinot"/>
      </GitRepository>
    </repository>
    <maintainer>
      <foaf:Person>
        <foaf:name>Apache Pinot Team</foaf:name>
          <foaf:mbox rdf:resource="mailto:dev@pinot.apache.org"/>
      </foaf:Person>
    </maintainer>
  </Project>
</rdf:RDF>