Apache Pinot is a distributed OLAP data store, built to deliver real-time analytics with low latency. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, Azure ADLS or Google Cloud Storage) as well as stream data sources (such as Apache Kafka). If the need is user-facing, low-latency analytics, SQL-on-Hadoop solutions don't offer the low latency that is needed. Modern OLAP engines like Apache Pinot (or Apache Druid and Clickhouse among others) can achieve much lower latency and are particularly suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion. Originally built by LinkedIn, Apache Pinot entered Apache incubation in late 2018 and has since added a plugin architecture and SQL support among other key capabilities. Apache Pinot can be fairly complex to operate and has many moving parts, but if your data volumes are large enough and you need low-latency query capability, we recommend you assess Apache Pinot.
