has announced the latest release of its . This release offers integrations with popular databases and storage systems, Docker and Kubernetes support, availability on the AWS Marketplace and Microsoft Azure, and a new graph algorithm library.

New integrations with other databases include RDBMS, Kafka, Amazon S3, and HDFS. It will also add an integration with Spark in the near future. The company has added a new TigerGraph EcoSys GitHub repository that will host new open-source connectors to TigerGraph as it rolls out.

TigerGraph now supports one-click installations for major cloud marketplaces, including AWS and Microsoft Azure. This will enable customers to have better control over where their data is stored and avoid vendor lock-in, the company explained. New support for Docker and Kubernetes allows for easy portability across on-premise and cloud environments.

Finally, it added a new graph algorithm library that includes GSQL implementations of popular graph analytics functions such as PageRank, Shortest Path, Connected Components, and Community Detection.

“Today’s release makes it even easier for enterprises to connect TigerGraph to their existing infrastructure,” said Dr. Yu Xu, CEO and founder of TigerGraph. “It’s all part of our commitment of delivering the best graph engine on the market to power the applications that are paving the way of the future.”

In addition to the platform update, the company also released a Neo4j Migration Toolkit. The toolkit will enable developers to transform Cypher queries into GSQL.

“GSQL is the conceptual descendant of SQL, Cypher, Gremlin, MapReduce and SPARQL, so adding support of Cypher in GSQL was a natural step,” said Dr. Alin Deutsch, chief scientist, TigerGraph.



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