Senzing Launches Agentic Entity Resolution for Apache Spark

This section is Partnership Content suppliedThe content in this section is supplied by Business Wire for the purposes of distributing press releases on behalf of its clients. Postmedia has not reviewed the content. by Business Wire Article contentNew Spark-native offering makes Senzing the first entity resolution vendor to offer batch, transactional, and hybrid deployment models with full end-to-end agentic automationSign In or Create an AccountEmail AddressContinueor View more offersArticle contentLAS VEGAS — Senzing, an identity intelligence company, today announced the opening of its Senzing for Apache Spark beta program, bringing the company’s industry-leading entity resolution technology to distributed batch workloads for the first time. Organizations running Spark on AWS EMR, Databricks, or Snowflake can now resolve and relate billions of records across multiple data sources—from fully autonomous data profiling and preparation through to publishing the resolved entity graph to downstream systems.Article contentWe apologize, but this video has failed to load.Try refreshing your browser, ortap here to see other videos from our team.Article contentArticle contentThe launch marks a significant milestone for the entity resolution market. Until now, enterprises faced a binary choice: batch processing systems built on Spark, or real-time transactional systems. Senzing for Spark eliminates that tradeoff. With this release, Senzing becomes the only entity resolution vendor to offer all three entity resolution deployment modes—Spark batch, transactional SQL, and hybrid.Article contentTop StoriesGet the latest headlines, breaking news and columns.There was an error, please provide a valid email address.Sign UpBy signing up you consent to receive the above newsletter from Postmedia Network Inc.Thanks for signing up!A welcome email is on its way. If you don't see it, please check your junk folder.The next issue of Top Stories will soon be in your inbox.We encountered an issue signing you up. Please try againInterested in more newsletters? Browse here.Article content“Picking an entity resolution vendor has long forced a binary choice: Batch Spark or Transactional SQL. We’re excited to turn this ‘or’ into an ‘and.’ With Senzing for Spark, customers get all the intelligence found in our real-time SDK—principle-based entity resolution, entity-centric learning, relationship awareness, global name, address and cross-script matching, and explainability—running natively inside their Spark platform of choice. Article content— Brian Macy, Head of Operations and Engineering, SenzingArticle contentFully Agentic from Preparation to PublicationArticle contentSenzing® entity resolution for Spark is designed for agentic AI workflows end-to-end. Powered by the Senzing MCP Server, AI agents execute each stage of the pipeline autonomously:Article contentData preparation and mapping: Agents profile, prepare, map, and validate each data source to Senzing-ready dataframes autonomously.Distributed entity resolution: With validated dataframes, agents trigger and manage distributed entity resolution jobs across the Spark cluster, executing across all data sources in parallel at any scale.Publishing the resolved entity graph: Agents propagate results to any downstream destination—Elasticsearch, knowledge graphs, data lakes—or implant the resolved entity graph directly into an existing live Senzing instance, giving real-time systems an immediate entity intelligence boost.Article contentAvailability and RoadmapArticle contentSenzing for Spark v1.0, entering beta testing with select partners, supports multi-source batch entity resolution on AWS EMR, Databricks, Snowflake, and standalone Apache Spark deployments. The resolved entity graph output can also be used to pre-populate a Senzing real-time SQL instance.Article contentSenzing for Spark v2.0 (Hybrid), next on the roadmap, will allow organizations to splice batch entity resolution results directly into a live transactional Senzing instance with no downtime and no record-by-record ingestion, enabling rapid onboarding of large new datasets at Spark speed.Article contentArticle contentOrganizations with a Spark cluster and active use cases in financial crime detection, insurance fraud, national security, or customer 360 are encouraged to apply for the beta program.Article contentFor more information or to apply for early access, visit Senzing Agentic Entity Resolution for Apache Spark.Article contentAbout SenzingArticle contentSenzing delivers the identity intelligence organizations need to achieve their agentic AI aspirations. As the creator of Agentic Entity Resolution, Senzing enables AI agents to autonomously identify and act on real-world entities in real time or batch—keeping all data secure within customer infrastructure. Backed by 40+ years of innovation and 300+ years of combined team experience, Senzing is trusted by organizations worldwide to ensure their AI agents operate on accurate and trustworthy data. Senzing is headquartered in Las Vegas, Nevada. For more information, visit www.senzing.ai.Article contentArticle contentArticle contentArticle contentView source version on businesswire.com: Article content https://www.businesswire.com/news/home/20260327621155/en/Article contentArticle contentContactsArticle contentTrending Meet the Canadian e-bike maker who is redefining the factory floor Electric Vehicles Posthaste: Believe it or not but home prices rose in these 11 major Canadian cities News Markets could be making the wrong call on interest rates Investor Bank of Canada better at handling supply shocks after 'difficult' inflation lesson, says deputy Economy Dad won’t talk about estate planning. How can I avoid being blindsided? FP Answers Share this article in your social networkCommentsYou must be logged in to join the discussion or read more comments.Create an AccountSign in Join the Conversation Postmedia is committed to maintaining a lively but civil forum for discussion. Please keep comments relevant and respectful. 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