About DataStax Enterprise
DataStax Enterprise is a data management platform from DataStax that supports enterprise-grade AI data needs. It provides capabilities for managing data for AI at scale, using key technologies, and facilitating value acceleration together with the user. This platform enables organizations to handle large volumes of data efficiently while integrating with AI applications to maximize insights and operational effectiveness. By utilizing DataStax Enterprise, businesses can ensure reliable data accessibility and bolster their AI initiatives. Key capabilities: manage data for AI at scale key technologies accelerate value together Best for: enterprises that need to support large-scale AI applications and manage complex data environments effectively.
DataStax Enterprise (DSE) is a powerful, enterprise-grade database platform built on the rock-solid foundation of Apache Cassandra™, specifically engineered for organizations that demand ultra-low latency, real-time data access, and high availability at massive scale. Designed for mission-critical workloads, DSE goes far beyond standard NoSQL capabilities, integrating advanced features like vector and knowledge graph support, which are particularly useful for AI and Generative AI applications. One of its most compelling strengths is the ability to function as a multi-model database, supporting tabular, document (JSON), key-value, and graph data formats within a single platform. This flexibility allows businesses to streamline their tech stack and handle diverse data types without switching systems. The user experience begins with CQL (Cassandra Query Language), which mirrors SQL syntax and offers an easy transition for developers familiar with relational databases. For enhanced usability, developers can work through DataStax Studio—an intelligent, interactive CQL editor that provides code completion, query validation, and rich visualizations, especially useful for navigating graph datasets. This makes the tool accessible while still delivering on performance for complex tasks.
Pros & Cons
- +Integrated Platform: Provides an integrated AI development platform with Langflow and Astra DB, reducing the need for a complex tool stack.
- +Advanced Database Capabilities: Astra DB offers near-zero latency with built-in vector and knowledge graph capabilities, improving query relevancy and reducing hallucinations.
- +Scalability and Performance: Designed for deployment at scale (cloud or self-managed), guarantees performance, and helps lower costs and manage Total Cost of Ownership (TCO).
- +Production-Ready: Emphasizes security and scalability for production environments.
- +Simplified AI Development: Simplifies AI development with features like Agent Builder, data management tools, and a visual development interface for complex logic flows.
- +Flexibility and Integration: Works with various open-source tools and integrates with a wide range of services, allowing it to fit into existing tech stacks.
- +Cost Savings: Migration to Astra DB can lead to significant cost savings (e.g., 85% cost savings mentioned).
- +Data Management: Helps manage data intelligently to produce relevant AI applications.
- −Steep Learning Curve: While CQL is SQL-like, mastering the intricacies of distributed systems and advanced DSE features can be complex for beginners.
- −Operational Complexity: Managing large-scale DSE clusters, especially on-premise, requires specialized knowledge and operational effort.
Features
Key features
- Near-Zero Latency with Vector and Knowledge Graph
- Provides a high-performance database optimized for real-time AI applications, integrating vector search and knowledge graph capabilities.
- RAG-First Developer Experience (Langflow)
- Offers a visual development interface (Langflow) that simplifies creating and understanding complex AI logic flows, especially for Retrieval-Augmented Generation (RAG).
- Integrated AI Dev Platform
- Combines an AI development platform (Langflow) with a data platform (Astra DB/DSE) to reduce friction and complexity in building AI apps.
- Cloud or Self-Managed Deployment
- Offers flexible deployment options including cloud, self-managed (on-premise), and hybrid environments.
- Unified Data Platform for GenAI
- Consolidates all data on one platform, specifically designed to support Generative AI applications and more.
- Enterprise Apache Cassandra Experts
- Leverages deep expertise in Apache Cassandra to deliver a scalable, always-on database for demanding enterprise workloads.
Additional features
- Fastest Way to Build and Deploy AI Apps
- Accelerates AI application development.
- Langflow Integration
- Uses Langflow, an AI development platform.
- Astra DB Integration
- Integrates with Astra DB, a near-zero latency database.
- Vector Database Capabilities
- Built-in support for vector search.
- Knowledge Graph Capabilities
- Built-in support for knowledge graph functionality.
- Simplify AI Dev
- Tools designed to make AI development easier.
- Agent Builder
- Provides capabilities for building AI agents.
- Data Management
- Offers comprehensive tools for managing data.
- NoSQL + Vector DB
- Combines NoSQL database features with vector database capabilities.
- Reduce Friction and Deploy Quickly
- Streamlines the deployment process for AI applications.
- Integrated AI Dev Platforms
- Provides a unified platform for AI development.
- Harness Data in Apps
- Allows easy integration of data into applications.
- AI PAAS
- Functions as an AI Platform as a Service.
- Orchestration
- Supports orchestration of AI workflows.
- Data Platform
- Serves as a foundational data platform.
- Integrated Services
- Offers a suite of integrated services.
- Runtime Deployment
- Facilitates deployment of AI applications at scale.
- Cloud Deployment
- Supports deployment in cloud environments.
- Self-Managed Deployment
- Supports self-managed (on-premise) deployments.
- Integrations with Top AI Cloud Providers
- Works with major cloud providers.
- Guaranteed Performance
- Aims to ensure consistent high performance.
- Lower Costs and Manage TCO
- Helps reduce total cost of ownership.
- Build for Production
- Focuses on security and scalability for production environments.
- Visual Development Interface (RAG-First)
- Simplifies creating and understanding complex logic flows, particularly for RAG.
- Prebuilt Integrations
- Offers ready-to-use integrations for faster testing with real data.
- Low-Code Visual IDE
- Provides a low-code integrated development environment.
- Test and Iterate Faster
- Accelerates the testing and iteration cycles.
- Works with Your Stack
- Compatible with open-source tools and integrations.
- Integrations (Specific examples)
- Airbyte, Amazon Bedrock, Anthropic, Azure, Bing, Cassandra, Chroma, Clickhouse, Cohere, Composio, Confluence, Couchbase, CrewAI, DuckDuckGo, Evernote, Firecrawl, GitHub, Glean, Gmail, Google Cloud, Google Drive, Groq, Hugging Face, LangChain, MeriTalk, Meta, Milvus, Mistral, MongoDB, Notion, NVIDIA, Ollama, OpenAI.
- Intelligent Data Management
- Helps manage data effectively for relevant AI applications.
- Improve Query Relevancy
- Enhances the relevance of search queries.
- Reduce Hallucinations
- Aims to minimize AI model hallucinations.
- Support for Vector Data
- Enables efficient handling and search of vector embeddings.
- Support for Knowledge Data
- Supports management of knowledge graph data.
- Near-Zero Latency Database
- Provides extremely low-latency data access.
- Powered by Apache Cassandra®
- Built on the highly scalable and available Apache Cassandra database.
- Hybrid Cloud Data Solution
- Supports hybrid cloud deployment models.
- Enterprise Apache Cassandra Experts
- Offers specialized expertise for Apache Cassandra.
- Apache Cassandra Distribution
- Based on Apache Cassandra, providing its core benefits.
- Operational Reliability
- Adds features for reliable operations.
- Monitoring Tools
- Includes tools for monitoring database performance and health.
- Security Tools
- Provides enterprise-grade security features.
- Analytics Capabilities
- Supports analytical workloads.
- Search Capability
- Integrated search functionalities.
- Reporting Tools
- Tools for generating reports from data.
- Intelligent Data Replication
- Designed for efficient data replication and distribution.
- Multi-Model Capabilities
- Supports various data models including JSON/document, key-value, tabular, and graphs.
- Distributed File System (DSEFS)
- For storing very large datasets (replaces CFS).
- Graph Features
- Includes new fuzzy search, advanced configuration of search indexing, improved data loading, geospatial querying, Kerberos support, and customizable visualizations for graph data.
- Granular Access Control
- Allows permissions on table rows and search indexes.
- DSE Proxy Management
- Manages web services via DSE Proxy.
- JMX Authentication
- Integrates with DSE Unified Authentication (LDAP or internal).
- Faster DSE Search (Apache Solr™)
- Improved search performance using Apache Solr.
- Index Management CQL and cqlsh Commands
- Streamlines operations and development for search indexes.
- Advanced Replication (CDC-based)
- Re-designed using Change Data Capture (CDC) logs for configurable replication, suitable for sporadic connectivity.
- DSE Studio
- Visually navigates database objects, creates and tunes CQL queries, with intelligent CQL editor, syntax highlighting, validation, code completion, and query profiling.
- Graph Query Profiling
- More complete profiling for graph queries in DSE Studio.
- Customizable Graph Visualization
- Colors and sizes vertices by label/property value, custom shapes and icons.
- Schema Visualization
- Improved schema display.
- In-Memory Capabilities
- MemoryOnlyStrategy works with compression.
- Faster Server Startup
- Operational performance improvements.
- Improved Help for CQL and cqlsh
- Better command-line assistance.
- Tab Completion for cqlsh
- Enhances command-line productivity.
- Improved dsetool and dse client-tool
- Better management tools.
- cassandra-stress -graph option
- Automatically graphs stress test results.
- Clustering Columns Improvement
- Allows using clustering columns in WHERE clauses without secondary index if ALLOW FILTERING is used.
- CREATE TABLE WITH ID
- Recreates dropped tables and replays commitlog for data recovery.
- JBOD Compaction and Backup
- Improved support for Just a Bunch Of Disks compaction and backup.
Pricing
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About DataStax Enterprise
DataStax Enterprise is a data management platform from DataStax that supports enterprise-grade AI data needs. It provides capabilities for managing data for AI at scale, using key technologies, and facilitating value acceleration together with the user. This platform enables organizations to handle large volumes of data efficiently while integrating with AI applications to maximize insights and operational effectiveness. By utilizing DataStax Enterprise, businesses can ensure reliable data accessibility and bolster their AI initiatives. Key capabilities: manage data for AI at scale key technologies accelerate value together Best for: enterprises that need to support large-scale AI applications and manage complex data environments effectively.
DataStax Enterprise Details
DataStax Enterprise's In-App Market Place
Does DataStax Enterprise have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
Usd ($), Eur (€), Gbp (£), Cad (C$), Aud (A$), Jpy (¥), Cny (¥), Inr (₹), Rub (₽), Brl (R$)
Pros & Cons
- Integrated Platform: Provides an integrated AI development platform with Langflow and Astra DB, reducing the need for a complex tool stack.
- Advanced Database Capabilities: Astra DB offers near-zero latency with built-in vector and knowledge graph capabilities, improving query relevancy and reducing hallucinations.
- Scalability and Performance: Designed for deployment at scale (cloud or self-managed), guarantees performance, and helps lower costs and manage Total Cost of Ownership (TCO).
- Production-Ready: Emphasizes security and scalability for production environments.
- Simplified AI Development: Simplifies AI development with features like Agent Builder, data management tools, and a visual development interface for complex logic flows.
- Flexibility and Integration: Works with various open-source tools and integrates with a wide range of services, allowing it to fit into existing tech stacks.
- Cost Savings: Migration to Astra DB can lead to significant cost savings (e.g., 85% cost savings mentioned).
- Data Management: Helps manage data intelligently to produce relevant AI applications.
- Steep Learning Curve: While CQL is SQL-like, mastering the intricacies of distributed systems and advanced DSE features can be complex for beginners.
- Operational Complexity: Managing large-scale DSE clusters, especially on-premise, requires specialized knowledge and operational effort.
DataStax Enterprise's Support Options
Email Address
info-jp@datastax.comContact
+81-3-4360-9127Documentation
https://docs.datastax.com/en/home/index.htmlChatbot
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