ETL A number of Enterprise Information Sources Right into a Ruled Information Graph for AI and Determination Intelligence
Tom Sawyer Software program, the chief in graph and knowledge visualization expertise, introduced a beta launch of their new product providing, Tom Sawyer Information Streams, a schema-driven platform to extract, rework, and cargo (ETL) structured and unstructured knowledge right into a single, ruled, query-ready data graph. Information Architects and AI Platform Engineers can use Information Streams to subscribe to Apache Kafka (or Confluent) matters sourced from databases, recordsdata, and APIs; apply transformations and filters; then run flows repeatedly to normalize, enrich, and hyperlink altering streams in actual time. The ensuing data graph is continued in a graph database for scalable sharing and downstream evaluation.
Information Streams works with present knowledge pipelines and catalogs in help of information preparation and processing inside AI pipelines, creation of enormous language fashions, validating generative AI outcomes, and integrating agentic outcomes right into a single-source-of-truth database. The platform lowers integration overhead, providing a unified view to help lineage, affect evaluation, and operational decision-making.
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Information Streams is a breakthrough functionality for enterprises fighting remoted and legacy datasets, pricey migrations, and streamlining AI pipelines.”
— Brendan Madden, CEO of Tom Sawyer Software program
“Information Streams is a breakthrough functionality for enterprises fighting remoted and legacy datasets, pricey migrations, and streamlining AI pipelines,” mentioned Brendan Madden, CEO of Tom Sawyer Software program. “Information Streams makes use of Kafka, CDC, and well-defined transformations to assemble a ruled data graph alongside your present stack. The result’s decrease storage and migration spend, and a dependable context layer for operations and AI.”
New in This Launch:
Unified Streaming Information Integration: Information Streams subscribes to Apache Kafka (or Confluent) matters that customers provision, then transforms and persists these occasions right into a data graph mannequin, unifying legacy and streaming knowledge in actual time. Use commonplace instruments, resembling Kafka Join, Change Information Seize (CDC) instruments, or customized producers, to publish from the programs of report and subscribe in Information Streams. Customers management which matters symbolize nodes, edges, or attributes, and safe entry with present Kafka authentication and encryption. Information Streams can consolidate disparate knowledge from almost any system—relational databases, graph databases, knowledge warehouses, recordsdata, APIs, and extra—right into a single, ruled, query-ready data graph with out costly migrations and re-platforming.
Highly effective Schema and Information Transformations: Information Streams routinely extracts schemas from Kafka matters, and customers refine them in a visible editor together with renaming fields, changing node varieties to edges, and making use of superior filters and guidelines with Spring Expression Language (SpEL). This supplies customers with management over normalization, enrichment, and linking so the ensuing data graph mirrors enterprise semantics and stays constant throughout sources.
Visible Information Circulation Design: An online-based designer lets customers outline sources, transformations, situations, and sinks to construct and monitor knowledge flows—accelerating supply whereas lowering customized code. Automated graph format organizes complicated pipelines for fast readability, so customers can iterate quicker, validate logic visually, and promote modifications with confidence, shortening the trail from prototype to manufacturing.
Actual-Time, Steady Processing: Information flows could be run in batch or repeatedly to ingest and rework occasions as they arrive, preserving the data graph at all times present. Low-latency processing helps operational selections and analytics alike, making certain downstream instruments see the most recent relationships and context.
Versatile Output and Storage: Remodeled knowledge is continued right into a graph database, and optionally customers can publish outcomes again to Kafka for downstream providers. The ensuing data graph plugs immediately into present analytics, AI, visualization, and knowledge science stacks—supporting retrieval-augmented era and validation of generative outputs, in addition to aggregating agentic outcomes right into a ruled supply of reality. Groups can question, discover, and operationalize insights with out disruptive modifications or pricey knowledge migrations.
Enterprise-Grade Safety and Authentication: Information Streams helps authentication with OAuth 2.0 and Keycloak for safe, multi-user environments. It aligns with enterprise requirements for entry and encryption whereas sustaining a clear, auditable boundary round delicate knowledge.
Docker-Primarily based, Air-Gapped Set up: A devoted Docker installer streamlines deployment in cloud, on-premises, and absolutely offline (air-gapped) environments. Operations groups can standardize installs and upgrades throughout clusters, assembly safety constraints with out sacrificing velocity.
Seamless Integration with Tom Sawyer Graph and Information Visualization Instruments: Information Streams pairs with Tom Sawyer Views and Tom Sawyer Explorations to visualise the ensuing data graph from the information streams, offering computerized graph layouts, interactive filtering, and intuitive knowledge exploration. Shortly transferring from ingestion to interactive perception allows stakeholders to discover patterns, validate fashions, and share findings.
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