StarTree
StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning:
- Data Volume: query performance sustained at petabyte scale
- Ingest Rates: millions of events per second, continuously indexed for freshness
- Concurrency: thousands to millions of simultaneous users served with sub-second latency
With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.
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Apache Iceberg
Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. Iceberg supports flexible SQL commands to merge new data, update existing rows, and perform targeted deletes. Iceberg can eagerly rewrite data files for read performance, or it can use delete deltas for faster updates. Iceberg handles the tedious and error-prone task of producing partition values for rows in a table and skips unnecessary partitions and files automatically. No extra filters are needed for fast queries, and the table layout can be updated as data or queries change.
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Apache Impala
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. For Apache Hive users, Impala utilizes the same metadata and ODBC driver. Like Hive, Impala supports SQL, so you don't have to worry about reinventing the implementation wheel. With Impala, more users, whether using SQL queries or BI applications, can interact with more data through a single repository and metadata stored from source through analysis.
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OLake Fusion
OLake Fusion is a self-hosted application for maintaining Apache Iceberg tables in data lakehouses. It automates scheduled table compaction, merging small data files and converting delete files to improve table layout as data is continuously ingested, updated, or deleted. Users can configure maintenance at the table level and schedule Lite, Medium, and Full compaction tiers, review table health and file metrics, and track maintenance runs and logs through a web UI. Fusion connects to Iceberg catalogs, including AWS Glue, Hive Metastore, JDBC, and REST-based catalog implementations such as Nessie, Polaris, Unity Catalog, and Lakekeeper, and can maintain tables created outside OLake. It is deployed on Docker or Kubernetes in a team's cloud or on-premises environment, and is designed for data engineers and platform teams operating analytical lakehouses with Apache Iceberg.
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