😎🔎Selection of useful OLAP services for processing Big Data
Apache Druid is a real-time OLAP engine. It is focused on time series data, but can be used for any data. It uses its own columnar format that can highly compress data, and it has many built-in optimizations such as inverted indexes, text encoding, automatic data folding, and more.
Apache Pinot - Offers lower latency thanks to the Startree index, which does partial precomputation, so it can be used for user-facing applications (it was used to fetch LinkedIn feeds). This uses a sorted index instead of an inverted one, which is faster.
Apache Tajo - Designed to perform ad hoc queries with low latency and scalability, online aggregation and ETL for large data sets stored in HDFS and other data sources. It supports integration with Hive Metastore to access shared schemas.
Solr is a very fast open source enterprise search platform built on Apache Lucene. Solr is robust, scalable, and fault-tolerant, providing distributed indexing, replication and load-balanced queries, automatic failover and recovery, centralized configuration, and more.
Presto is an open source platform from Facebook. It is a distributed SQL query engine for running interactive analytical queries against data sources of any size. Presto lets you query data where it lives, including Hive, Cassandra, relational databases, and file systems. It can query large data sets in seconds. Presto is independent of Hadoop, but integrates with most of its tools, especially Hive, to run SQL queries.