Ontul Key Features
Discover the core features of the distributed data engine that unifies batch processing, stream processing, and interactive SQL in a single engine.
Unified Data Engine
Run batch processing, stream processing, and interactive SQL queries in a single cluster. Consolidate all data workloads without separate systems.
Arrow-Native Execution Engine
Process all data in Apache Arrow columnar format. Achieve high performance with vectorized execution and zero-copy, eliminating serialization overhead.
Interactive SQL
JDBC connections (DBeaver, DataGrip) via Arrow Flight SQL with multi-catalog federation queries. Full standard SQL support including JOINs, window functions, and CTEs.
Flink-style Streaming
Continuous processing — events are processed as they arrive, not in Spark-style micro-batches. Supports TUMBLING, SLIDING, and SESSION windows with multi-worker hash shuffle.
Exchange Manager
Unified fault-tolerance infrastructure for Query, Batch, and Streaming. Handles data spill on memory pressure and streaming checkpoint state — all through a single system with KMS envelope encryption.
Exactly-Once Semantics
Master-coordinated barrier checkpoint guarantees exactly-once delivery for transactional sinks (Iceberg, JDBC, NeorunBase, Kafka Transactions). Sink commit before offset commit ensures data consistency.
Connector Architecture
Access diverse data sources through plugin-based connectors. Dynamically register and unregister Iceberg, NeorunBase, JDBC, Kafka, Elasticsearch, and more at runtime.
Federation Queries
Execute cross-catalog joins across multiple data sources in a single SQL query. Combine Iceberg, NeorunBase, and JDBC tables seamlessly.
Apache Iceberg Integration
Iceberg REST catalog integration with read/write, CTAS, and MERGE INTO support. Automated maintenance including snapshot expiration and data compaction.
Security (IAM & KMS)
AES-256-GCM envelope encryption, built-in KMS, Exchange Manager data encryption, catalog/table/column/row-level IAM policies, and STS temporary credentials.
Use Cases
Unified Data Processing
Handle all data workloads — batch, streaming, and SQL — with a single Ontul cluster instead of separate systems.
Real-Time Data Pipelines
Ingest data from Kafka, process in Ontul, and load into Iceberg tables for real-time ETL pipelines.
Data Lake Analytics
Run federation queries across Iceberg, JDBC, and other sources for unified analytics.
ETL Automation
Program batch ETL jobs with SDK and REST API, integrating with workflow orchestrators.
Considering Ontul for your data platform?
Unified. Arrow-Native. Single Engine.
Revolutionize your data infrastructure with a distributed data engine that unifies batch, streaming, and SQL.