An open source big data platform for distributed storage and processing.

Multitenant ecosystem

• A set of interrelated subsystems: MapReduce, a SQL query engine, a job scheduler, and a key-value store for OLTP workloads.
• Support for large numbers of users, eliminating the need for multiple installations and improving hardware utilization.

Reliability and stability

• No single point of failure.
• Automated replication between servers.
• Updates with no loss of progress.

Scalability

• Up to 1 million CPU cores and thousands of GPUs.
• Exabytes of data on different media: HDD, SSD, NVME, RAM.
• 10 000+ nodes.
•  Automated server scaling up and down.

Rich functionality

• An expansive MapReduce model.
• Distributed ACID transactions.
• A variety of SDKs and APIs.
• Secure isolation for compute resources and storage.
• A user-friendly and easy-to-use UI.

CHYT powered by ClickHouse®

• A well-known SQL dialect and familiar functionality.
• Fast analytic queries.
• Integration with popular BI solutions via JDBC and ODBC.

SPYT powered by Apache Spark

• A set of popular tools for writing ETL processes.
• Support for multiple isolated clusters of various sizes.
• Easy migration for existing solutions.

Usage scenarios

Batch processing

MapReduce and SPYT for processing structured and unstructured data: logs and financial transactions.

Ad hoc analytics

CHYT provides rapid analytical queries without exporting data to an outside OLAP system. External dashboards and BI tools can access data using JDBC/ODBC protocols.

OLTP
Low-latency transactional key-value store allows building interactive pipelines and services.

Machine learning
Managing GPU clusters to train models with billions of parameters.

Metadata storage
Transactional metadata storage and a reliable distributed coordination service.

ETL pipelines
Build data processing pipelines using familiar tools: Apache Spark, SQL, MapReduce.