Apache Pinot
Real-time distributed OLAP datastore for ultra-low latency analytics at massive scale.

Enterprise-grade ACID transactions for high-scale distributed key-value stores.
Apache Omid (Optimized Transaction Manager) is a sophisticated transaction management system providing ACID properties on top of non-transactional distributed key-value stores, primarily Apache HBase. In the 2026 data landscape, Omid maintains a critical role for enterprises that require transactional integrity within Hadoop-based data lakes without migrating to monolithic RDBMS. Its architecture centers around a Transaction Status Oracle (TSO) that handles timestamp allocation and conflict detection using an optimistic concurrency control model. This design allows for high throughput and horizontal scalability. Omid supports Snapshot Isolation (SI), ensuring that readers never block writers and vice versa. It is particularly valued in 2026 for its ability to retroactively add multi-row atomicity to legacy HBase deployments, enabling complex financial ledgers and metadata management systems to operate with the consistency of a traditional SQL database while retaining the massive scale of NoSQL. Its integration with Apache Phoenix and HDFS makes it a cornerstone for resilient, consistent big data pipelines.
Uses an optimistic concurrency control mechanism where conflicts are detected at commit time rather than using pessimistic locking.
Real-time distributed OLAP datastore for ultra-low latency analytics at massive scale.
The industry-standard distributed event streaming platform for high-performance data pipelines and real-time AI telemetry.
Scalable parallel computing in Python for high-performance data science and machine learning.
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A centralized component that assigns monotonically increasing timestamps and tracks write-write conflicts.
Optimizes read paths by storing transaction metadata directly in HBase cells to avoid TSO lookups for every read.
Leverages Zookeeper for leader election among TSO instances, ensuring no single point of failure.
Ensures that a transaction sees a consistent snapshot of the database as it existed at the start of the transaction.
Custom HBase compaction filters that clean up transactional metadata (shadow cells) during background maintenance.
Enables multiple client applications to share the same Omid infrastructure with isolated namespaces.
Ensuring double-entry bookkeeping across multiple rows in HBase without data loss.
Registry Updated:2/7/2026
Commit transaction; Omid ensures both occur or neither.
Preventing overselling by atomically updating stock levels during high-concurrency events.
Maintaining consistency between a data file's content and its metadata stored in HBase.