Data · GitHub

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

• Separation of concerns. Apache Hamilton separates the DAG "definition" and "execution" which lets data scientists focus on solving problems and engineers manage production pipelines.
• Effective collaboration. The Apache Hamilton UI provides a shared interface for teams to inspect results and debug failures throughout the development cycle.

A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends)

• Schema Metadata Management: VertexLabel, EdgeLabel, PropertyKey, and IndexLabel
• Multi-type Indexes: Exact query, range query, and complex conditions combination query

Apache Ignite

Apache Kyuubi is a distributed and multi-tenant gateway to provide serverless SQL on data warehouses and lakehouses.

Apache Linkis builds a computation middleware layer to facilitate connection, governance and orchestration between the upper applications and the underlying data engines.

• Support for diverse underlying computation storage engines : Spark, Hive, Python, Shell, Flink, JDBC, Pipeline, Sqoop, OpenLooKeng, Presto, ElasticSearch, Trino, SeaTunnel, etc.;
• Support for diverse language : SparkSQL, HiveSQL, Python, Shell, Pyspark, Scala, JSON and Java;

Apache Phoenix Adapters

A single-node analytical database engine with geospatial as a first-class citizen

• High Performance: Built in Rust for exceptional speed and memory efficiency
• Comprehensive Spatial Toolkit: Supports both vector and raster functions in a single library

Distributed scheduled job

• Support job sharding and high availability in distributed system
• Scale out for throughput and efficiency improvement