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Category: Computing & data (Page 44 of 97)

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.

DataStax’s Astra Streaming now supports for Kafka and RabbitMQ

DataStax announced the general availability of Astra Streaming, a managed messaging and event streaming service built on Apache Pulsar. Now featuring built-in API-level support for Kafka, RabbitMQ and Java Message Service (JMS), Astra Streaming makes it easier for enterprises to get real-time value from their data-in-motion. Capabilities include:

  • Mobilizes all data-in-motion An enterprise’s data-in-motion encompasses all data in platforms that provide streaming, queuing and pub/sub capabilities, Astra Streaming can address these use cases at the scale enterprises need.
  • Modernizes event-driven architectures: Seamlessly leverage existing messaging/pub sub apps and turn them into streaming apps with a drop-in replacement; easily modernize Kafka applications with zero rewrites
  • Runs across an entire IT estate: multi-cloud + on prem: Supports a unified event fabric that stretches across an enterprise’s data-in-motion spread across their entire data estate: on premises, in the cloud and at the edge.
  • Powers a real-time data ecosystem: Through a wide range of connectors, Astra Streaming is connected to an enterprise’s data ecosystem, enabling real-time data to flow instantly from data sources and applications to streaming analytics and machine learning systems. It’s also integrated with Astra DB, powering its CDC capabilities.

https://www.datastax.com/press-release/datastax-s-astra-streaming-goes-ga-with-new-built-in-support-for-kafka-and-rabbitmq

Acquia adds data subject deletion requests to Acquia CDP

Acquia announced new regulatory compliance features that help organizations using Acquia Customer Data Platform (CDP) to comply with data subject requests and privacy laws in general. Using a new self-service interface, organizations can rapidly process “Right to Erasure” (otherwise known as “Right to be Forgotten”) requests associated with regulations such as GDPR, CCPA, and more from their customers. The feature for legal and compliance workflows is to make it simple for organizations using Acquia CDP to process deletion requests from their own customers, ensuring that these requests are handled quickly.

Other recent self-service updates include secure credentials management for Acquia CDP out-of-the-box connectors. Organizations can now generate and manage their own credentials for pre-built connectors to external services such as Facebook or Google. In addition, they can set up new credentials for their own custom connectors. Both self-service credentials management and compliance features are meant to accelerate workflows within Acquia CDP, without having to wait for assistance from an Acquia customer support team member.

https://www.acquia.com

Tellius and Databricks partner to democratize data analysis

Tellius announced a partnership with Databricks to give joint customers the ability to run Tellius natural language search queries and automated insights directly on the Databricks Lakehouse Platform, powered by Delta Lake, without the need to move any data.

With Tellius, organizations can search and analyze their data to identify what is happening with natural language queries, understand why metrics are changing via AI-powered Insights, and determine next best actions with deep insights and AutoML. Connecting to Delta Lake on Databricks only takes a few clicks, and then users can perform a natural language search of their unaggregated structured and unstructured data to answer their own questions. They can drill down to get granular insights, leverage single-click AI analysis to uncover trends, key drivers, and anomalies in their data, and create predictive models via AutoML in Tellius. Answers and insights can be utilized to write back to source applications to operationalize insights. Faster data collaboration helps democratize data access across analytics teams with less worrying about performance or IT maintenance.

https://www.tellius.com/tellius-and-databricks-partner-to-deliver-ai-powered-decision-intelligence-for-the-data-lakehouse/

Snowflake launches Unistore

Snowflake announced the launch of Unistore, a new workload that expands the capabilities of Snowflake and delivers a modern approach to working with transactional and analytical data together in a single platform. Unistore extends the Snowflake Data Cloud to streamline and simplify the development of transactional applications, while providing consistent governance, performance, and scale to customers.

Transactional and analytical data have typically been siloed, creating complexities when moving data between systems and hindering the speed required for modern development. With Unistore, teams can expand the Data Cloud to include transactional use cases such as application state and data serving. As a part of Unistore, Snowflake is introducing Hybrid Tables, which offer fast single-row operations and allow customers to build transactional business applications directly on Snowflake. Hybrid Tables, currently in private preview, enable customers to perform swift analytics on transactional data for immediate context, and join Hybrid Tables with existing Snowflake Tables for a holistic view across all data. Unistore and Hybrid Tables enable customers to build transactional applications with the same simplicity and performance they’re used to with Snowflake, and a unified approach to data governance and security.

https://www.snowflake.com/blog/introducing-unistore/

Adobe announces new Adobe Analytics services

Adobe announced new services in Adobe Analytics, delivering a single workspace for brands to unify data and insights across all media types. Adobe also introduced a new service to transition data from other analytics products while preserving historical compliance with regulations such as Global Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

Streaming media: Adobe is introducing new capabilities for brands to understand how streaming fits into the overall customer journey. Through Customer Journey Analytics (CJA), teams can tie digital media consumption to engagement on other channels like social media, websites and offline channels.

Seamlessly bring data together: With the bulk data insertion API now available, teams can move or activate any volume of historical data into Adobe Analytics. It covers any online or offline channel, allowing brands to transition data sources from point-of-sale devices, CRM systems and mobile applications.

Intelligent data mapping: Adobe Analytics is providing flexibility for brands to bypass the data migration preparation work while avoiding data destruction. As data comes through, Adobe Analytics preserves the underlying structure, and also suggests new ways to measure the customer journey. Brands can also retroactively apply dimensions to historical data, such as new attribution models.

https://news.adobe.com/news/news-details/2022/Next-Generation-Adobe-Analytics-Delivers-Customer-Insights-From-Streaming-Media-and-the-Metaverse/default.aspx

MongoDB unveils vision for a developer data platform

MongoDB, Inc. unveiled its developer data platform vision with new capabilities, helping development teams with a wider set of use cases, servicing more of the data lifecycle, optimizing for modern architectures, and implementing sophisticated levels of data encryption. MongoDB 6.0 has extended its approach of working with data beyond operational and transactional use cases to serve search and analytics use cases within a unified platform and consistent developer experience to reduce the complexity of data infrastructure required for modern applications. Upcoming capabilities include:

  • Making it easier for developers to leverage in-app analytics. Column Store Indexes will enable users to create and maintain a purpose-built index that speeds up many analytical queries without requiring changes to the document structure. 
  • Time series collections will support secondary indexes on measurements, and feature read performance improvements and optimizations for sorting time-based data more quickly.
  • With Search Facets, developers are able to rapidly build search experiences that allow end users to more seamlessly browse, narrow down or refine their results.

MongoDB also announced new products and capabilities that enable development teams to better analyze, transform, and move their data in Atlas while reducing reliance on batch processes and ETL jobs.

https://www.mongodb.com/new

Algolia launches additional AI models in Algolia Recommend

Algolia, an API-First Search & Discovery platform, unveiled additional AI (artificial intelligence) models and capabilities in its Recommend Spring Release 2022. Algolia Recommend introduces AI models powered by behavioral insights. When coupled with Algolia’s fast indexing capabilities, customers are able to immediately put their most relevant and up to date content into motion for end-users. From a single dashboard, merchandisers, digital content managers, or digital business leaders can choose the model that is right for them, deploy it, and track the results. The release includes:

  • Popular Trends – AI models that detects emerging trends based on users’ behavioral data as they interact with various brands, categories of products and content, and topics of interest.
  • Business Rules – Low-code/no-code functionality for controlling AI and activating unique business strategies. This provides greater flexibility for category merchandisers, online retail strategists, and content specialists to generate new recommendations.
  • Hybrid Recommend Engine – This is a combination of collaborative filtering algorithms and content-based filtering algorithms to increase the relevancy and accuracy of recommendations. Recommendations can be presented to users as the content-based data is indexed. Availability of behavioral information either at this initial stage or later can further help fine-tune and enrich the quality of recommendations.

https://www.algolia.com/about/news/new-ai-based-recommendation-models-overcome-cold-start-challenge/

Stardog updates enterprise knowledge graph platform

Stardog, an Enterprise Knowledge Graph platform provider, unveiled Stardog 8.0, with new innovations to streamline data exploration and discovery for all citizen data users.

The new Advanced Query tool in Stardog Explorer empowers citizen data users to ask complex business questions via the semantic layer more easily. By removing the need to learn a graph query language, users can self-serve from across their enterprise data landscape. Also new in Explorer is the ability to see what’s in your Stardog database by browsing data source and virtual graph metadata in the new Stardog Data Catalog graph. Additional updates include:

  • Project resources (imported CSV files and virtual graphs) can be previewed and refreshed to see updates in the data.
  • Enhanced support for project collaboration through exporting, importing, and duplicating projects.
  • Our new query profiler is available and shows you the query plan, allows you to interrupt slow queries, and can show you partial results.
  • Error notifications now stay displayed and the error text can be copied for troubleshooting.
  • A new Stardog Data Catalog graph is built from the metadata about data sources and virtual graphs within a Stardog database.
  • Improved performance for querying multiple virtual graphs and SPARQL update queries.

https://www.stardog.com/blog/introducing-stardog-8.0/

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