Product Name: DataStax Enterprise (DSE Graph), Astra DB
Company Name: DataStax, Inc.
URL: https://www.datastax.com
Entry Year: 2010 (Graph capability added in 2016 with DSE Graph)

Market Share (or Graph DB Revenue):
DataStax is a major player in the NoSQL database market, with a focus on distributed cloud databases. DataStax doesn’t disclose specific graph database revenue, but it is recognized for providing graph capabilities on top of its widely adopted Apache Cassandra platform. DataStax has a substantial enterprise presence with Astra DB, its cloud-native service.

Number of Employees: Approximately 500-600 employees

Capital: Not publicly disclosed

Funding:
DataStax has raised over $190 million through multiple rounds of funding. The most recent round was a $115 million Series E in 2014, backed by companies like Crosslink Capital and Meritech Capital Partners.

Major Users:
DataStax is used by Fortune 500 companies and notable organizations such as Capital One, eBay, Comcast, Macy’s, Intuit, and Sony. It is popular in industries like retail, finance, telecommunications, and e-commerce.

Key Application Areas:
DataStax is widely used for customer 360 views, recommendation engines, fraud detection, real-time analytics, supply chain management, and Internet of Things (IoT) applications.

Product Overview:
DataStax offers DataStax Enterprise (DSE), which provides a multi-model NoSQL database with graph, wide-column, and key-value store capabilities. DSE Graph is the graph database component, built on top of Apache Cassandra, enabling enterprises to run real-time, distributed graph queries on large-scale data. DataStax also offers Astra DB, a fully managed Cassandra-as-a-Service, with support for graph and other database models.

Data Compatibility:
DataStax supports a wide range of data formats, including JSON, CSV, and key-value data. It integrates with popular data processing and analytics platforms like Apache Spark, Kafka, and Kubernetes, making it easy to ingest and process various types of data. DataStax also provides APIs for connecting to other databases and cloud systems.

Knowledge Graph Implementation:
DataStax’s DSE Graph is designed to handle knowledge graph applications. It enables enterprises to model and manage large-scale knowledge graphs, with high performance for real-time graph traversals and deep link analytics. It uses the property graph model and supports complex queries on highly connected datasets.

Query Method:
DataStax uses Gremlin (a graph traversal language) as its primary query language for DSE Graph. Gremlin is part of the Apache TinkerPop framework and allows for expressive graph traversals across nodes and relationships.

Natural Language Queries:
DataStax does not natively support natural language queries, but external natural language processing (NLP) tools can be integrated to interpret queries and convert them into Gremlin queries. Some custom applications use this capability to enable domain-specific querying.

Native Machine Learning:
DataStax offers integrations with Apache Spark and DSE Analytics, which can be used for graph-based machine learning tasks. While it doesn’t offer native machine learning features in the graph engine, its integration with Spark allows users to run ML models on graph data for tasks like link prediction and classification.

Support for Traditional Machine Learning:
Through integration with Apache Spark, DataStax can extract graph features and use them in traditional machine learning models. This allows for scalable machine learning and analytics on graph data across distributed clusters.

Support for LLMs:
DataStax has emerging integrations with large language models (LLMs) for enhancing data retrieval and enriching analytics. By combining LLMs with graph data, it’s possible to improve the contextual understanding of complex datasets and enhance the knowledge graph use cases.

Support for RAG (Retrieval-Augmented Generation):
DataStax’s distributed graph architecture, coupled with its real-time querying capabilities, can be leveraged for RAG models. By providing structured graph data as the retrieval source for LLMs, DataStax enables enhanced, context-rich text generation, making it a powerful tool for real-time RAG applications.

Other Notable Features:

  • Multi-model database: DataStax supports graph, wide-column, and key-value models, making it flexible for different types of data workloads.
  • Astra DB: A fully managed, serverless Cassandra database-as-a-service that supports enterprise scalability with zero-downtime architecture.
  • Security: DataStax provides enterprise-grade security with encryption, role-based access control, and multi-factor authentication.
  • High Availability and Scalability: Built on Apache Cassandra, DataStax supports horizontal scaling, fault tolerance, and multi-cloud capabilities.
  • Real-time analytics: DataStax is optimized for real-time data streaming and analytics through Apache Kafka and Spark integrations.