TIGERGRAPH CONNECTORS

Connect Your Data and Analytics Network with TigerGraph Connectors
TigerGraph’s mission is to help you harvest the benefits of connected data.   TigerGraph connectors provide the high-speed and reliable data throughput you need to complete your graph data pipeline, for AI and advanced analytics.

Kafka Connectors

Kafka’s distributed processing, scalability, and fault tolerance led TigerGraph to make it part of our internal design from day one. Our Kafka data ingestion connector is built-in; nothing to install. Based on the trusted Kafka Connect framework, the TigerGraph Kafka Connector ensures seamless and reliable data integration.

Cloud Object Stores:
  • Amazon S3
  • Azure Blob Storage
  • Google Cloud Storage
Data Warehouses:
  • Snowflake
  • Google BigQuery
Databases:
  • PostgreSQL
Kafka

Spark-Based Two-Way Connectors

Today’s data lakes — or lakehouses — marry storage capabilities of traditional data lakes with data processing capabilities of Spark. A Spark-based connector is a natural fit for these environments. The TigerGraph Spark connector connects directly to Spark, transforming dataframes into graph data. You can also use it as a high-throughput, bidirectional portal to transport data to and from a wide range of data platforms.

Spark   Lakehouses:
  • Delta Lake
  • Iceberg
  • Hudi
Cloud Object Stores:
  • Amazon S3
  • Azure Blob Storage
  • Google Cloud Storage
Data Warehouses:
  • Snowflake
  • Google BigQuery

JDBC Connector

TigerGraph’s open-source JDBC driver is a type-4 driver that converts JDBC calls directly into TigerGraph REST API calls. Handling both data loading and queries, this connector can serve a wide variety of needs from a wide range of applications.

For example, use the JDBC connector to ingest DataFrames from Spark.