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Category: Computing & data (Page 3 of 98)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

300-node clusters now supported in CockroachDB

February 12, 2026 /

From the CockroachDB Blog…

As AI-driven and agentic applications push data platforms into new territory, data architects are increasingly forced to choose between correctness, simplicity, and scale. To remove that tradeoff we’re announcing support for 300-node clusters with 2.2M tpmC and 1.2PB of data in CockroachDB v25.4.4 and beyond. Also, On CockroachDB Cloud, we’re announcing support for 64 vCPU per node. All customers will be able to self-serve and select these larger instance types if desired.

Highlights include:

  • ~610K QPS, which when compared to PUA on a 9-node cluster with 17K QPS shows that CockroachDB near linearly scales with the size of the cluster.
  • Compared to a previous run on 25.2, a run with the same amount of imported data on 25.4 took 30% less storage space than the previous run and enhanced compression.
  • Imports for this run on 25.4 were 2× faster compared to 25.1, for migrations to CockroachDB.
  • ADD COLUMN across 120 B rows completed without regression.
  • 330TB backup and 6 concurrent changefeeds completed in 2 hours and 40 min with no impact on foreground traffic.

Start with $400 in free credits. Or get a free 30-day trial of CockroachDB Enterprise on self-hosted environments.

https://www.cockroachlabs.com/blog/300-node-clusters-supported-cockroachdb

Snowflake makes enterprise data AI-ready with Snowflake Postgres

February 3, 2026 /

Snowflake, an AI Data Cloud company, announced advancements that make data AI-ready by design, allowing enterprises to rely on data that is continuously available, usable, and governed as AI transitions from experimentation into production systems. With new enhancements to Snowflake Postgres, the database now runs natively in the AI Data Cloud so enterprises can consolidate their transactional, analytical, and AI use cases onto a single, secure platform. To help ensure AI systems are trusted at enterprise scale, Snowflake is embedding enhanced interoperability, governance, and resilience features into its platform.

Powered by pg_lake, a set of PostgreSQL extensions that allow Postgres to easily work within an organization’s open and interoperable lakehouse grounded in Apache Iceberg, enterprises can leverage Snowflake Postgres to directly query, manage, and write to Apache Iceberg tables using standard SQL. This capability is delivered within a Postgres environment, so enterprises can eliminate data movement between transactional and analytical systems.

Enterprises need data that remains open, governed, and resilient as it flows across engines, formats, and environments. Snowflake is expanding how customers access, share, and govern their data. Open Format Data Sharing extends Snowflake’s zero-ETL sharing model to include formats such as Apache Iceberg and Delta Lake.

https://www.snowflake.com/en/news/press-releases/snowflake-makes-enterprise-data-ai-ready-with-snowflake-postgres-and-advanced-innovations-for-open-data-interoperability

Elastic adds high-precision multilingual reranking to new Elastic Inference Service

February 3, 2026 /

Elastic, a Search AI Company, made two Jina Rerankers available on Elastic Inference Service (EIS), a GPU-accelerated inference-as-a-service that makes it easy to run fast, high-quality inference without complex setup or hosting. These rerankers bring low-latency, high-precision multilingual reranking to the Elastic ecosystem.

Rerankers improve search quality by reordering results based on semantic relevance, helping surface the most accurate matches for a query. They improve relevance across aggregated, multi-query results, without reindexing or pipeline changes. This makes them valuable for hybrid search, RAG, and context-engineering workflows where better context boosts downstream accuracy. The two new Jina reranker models are optimized for different production needs:

Jina Reranker v2 (jina-reranker-v2-base-multilingual)
Built for scalable, agentic workflows.

  • Low-latency inference with strong multilingual performance.
  • Ability to select relevant SQL tables and external functions that best match user queries..
  • Scores documents independently to handle arbitrarily large candidate sets.

Jina Reranker v3 (jina-reranker-v3)
Optimized for high-precision shortlist reranking.

  • Optimized for low-latency inference and efficient deployment in production settings.
  • Strong multilingual performance; maintains stable top-k rankings under permutation.
  • Cost-efficient, cross-document reranking: v3 reranks up to 64 documents together in a single inference call, reasoning across the full candidate set to improve ordering when results are similar or overlapping.

https://ir.elastic.co/news/news-details/2026/Elastic-Adds-High-Precision-Multilingual-Reranking-to-Elastic-Inference-Service-with-Jina-Models/default.aspx

Upland announces BA Insight Platform with integrated AI search experiences for enterprises

February 3, 2026 /

Upland Software, Inc., a provider of AI-powered knowledge and content management software, announced the Upland BA Insight Platform. The new BA Insight Platform incorporates SmartHub, ConnectivityHub, AutoClassifier, Smart Preview, and Connectors to deliver search experiences that are more connected, more contextual, and more actionable. Features include:

  • Knowledge Graphs to deliver deeper, connected, and more contextualized insights by mapping relationships across complex datasets
  • Agentic Retrieval-Augmented Generation (RAG) to provide more accurate answers to complex questions through conversational AI interfaces
  • Amazon Q Business Integration to enable users to connect and perform generative actions against organizational content via the seamless AI-powered assistant

BA Insight introduces native integrations with Amazon Q Business and AWS generative AI assistant, enabling organizations to unlock conversational search and gain actionable insights across all content sources, securely. By working closely with AWS, BA Insight ensures customers benefit from seamless deployment, robust security, and continuous innovation, empowering organizations to maximize the value of their information and accelerate their AI journey. The unified BA Insight enterprise search and AI enablement platform is available in AWS Marketplace.

https://investor.uplandsoftware.com/news/news-details/2026/New-Upland-BA-Insight-Platform-Delivers-Integrated-AI-Search-Experiences-for-Enterprises/default.aspx

MongoDB expands MongoDB for startups

January 15, 2026 /

MongoDB, Inc. announced an upcoming expansion to MongoDB for Startups, designed to help founders and builders take applications from prototype to global deployment. MongoDB for Startups companies now represent more than $200 billion in combined valuation, and this expansion gives early-stage companies a faster, more reliable path to scale by providing a production-ready data foundation and an integrated stack that works from day one.

With initial launch partners Temporal and Fireworks AI, MongoDB for Startups introduces a founder-first ecosystem to help startups avoid early infrastructure decisions that slow them down over time. In the AI era, founders face unprecedented complexity when selecting their infrastructure; choosing the wrong stack early can create long-term AI debt that stalls innovation. Through a curated partner ecosystem, the program gives startups access to infrastructure designed to scale without constant rework, by delivering a cohesive, production-ready stack through matched credits, coordinated onboarding and enablement content, and joint events across complementary technologies.

This expansion to MongoDB for Startups creates a simple, opt-in experience designed to help founders scale without assembling and maintaining disparate technologies. Eligible MongoDB for Startups organizations can access matched credit offers across a curated set of complementary technologies, including Fireworks and Temporal.

https://www.mongodb.com/press/mongodb-for-startups-expands-to-give-founders-a-faster-and-smarter-start-from-day-one

Box announces general availability of Box Extract

January 15, 2026 /

Box, an Intelligent Content Management (ICM) platform, released Box Extract. Powered by generative AI models from companies like Google, Anthropic, and OpenAI, and combined with agentic capabilities, Box Extract enables enterprises to intelligently and securely pull valuable information from content and save it as metadata in Box. With Box Extract, it is now easier for enterprises to automate workflows, accelerate decision-making, and get faster access to information and insights.

Organizational knowledge resides in the collection of contracts, product specifications, policy documents, charts, and other forms of unstructured content involved in day-to-day business operations. This content provides the critical context AI models and agents require to unlock meaningful business value.

Box’s agentic approach enables Box Extract to understand document structure and meaning, break it down into components, such as paragraphs, tables, or charts, and then pull out the most important information. Teams can create custom Extract Agents tailored to their business needs. These Box Extract Agents give customers the flexibility to store structured data alongside unstructured content as custom metadata, which can also be exported or synced to other systems such as Databricks and Snowflake.

https://blog.box.com/introducing-box-extract-get-actionable-data-enterprise-content-scale

Algolia and Microsoft collaborate on retail experiences

January 12, 2026 /

Algolia, an AI Search and Retrieval Platform, today announced a collaboration with Microsoft to provide retailers and brands with greater influence, accuracy, and visibility in AI-driven shopping experiences. The collaboration integrates Algolia’s real-time enriched product attributes (product data, inventory availability, and product pricing) into Microsoft Copilot, Microsoft Bing Shopping, and Microsoft Edge, to help retailers ensure their products appear correctly and competitively across emerging AI discovery surfaces.   

Algolia customers gain greater influence over how their products are represented across Microsoft digital sites. Retailers benefit from enhanced brand control and stronger context awareness within Copilot, Bing Shopping, and Edge, backed by fresher, deeper, and more accurate product data.  

AI-driven discovery becomes shoppable through real-time, retailer-approved data, giving merchants influence over external, “off property” AI surfaces. Retailers can now extend their merchandising strategies into LLM environments that were previously inaccessible. 

Algolia, with the help of Microsoft, is address critical gaps in agentic commerce, more dynamic product storytelling, and deeper insights into how products perform across Microsoft experiences for richer reporting and retail media measurement. 

https://www.algolia.com/about/news/algolia-collaborates-with-microsoft-to-drive-real-time-product-data-to-shopping-experiences

Microsoft announces agentic AI capabilities for retail

January 8, 2026 /

Microsoft announced a set of agentic AI capabilities aimed at supporting automation and decision-making across retail operations, including merchandising, marketing, store operations, and fulfillment. The tools are designed to connect data and workflows so teams can act on context in real time.

Copilot Checkout, now available in the U.S. on Copilot.com, allows shoppers to complete purchases directly within Copilot without being redirected to a retailer’s website, while merchants remain the merchant of record. The service integrates with partners including PayPal, Shopify, and Stripe, and supports participating retailers such as Urban Outfitters, Anthropologie, Ashley Furniture, and Etsy sellers.

Microsoft also introduced Brand Agents for Shopify merchants and a personalized shopping agent template in Copilot Studio. These tools enable conversational shopping experiences using a retailer’s product catalog, with options ranging from turnkey deployment to fully customizable implementations.

In public preview, a catalog enrichment agent template automates product onboarding and categorization by extracting attributes from images and enriching data for search and recommendations.

For physical stores, a store operations agent template provides natural-language access to inventory, policies, and operational insights, helping staff manage workflows, staffing, and day-to-day decisions using internal and external data signals.

https://news.microsoft.com/source/2026/01/08/microsoft-propels-retail-forward-with-agentic-ai-capabilities-that-power-intelligent-automation-for-every-retail-function

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