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Category: Computing & data (Page 2 of 35)

Topics include computing platforms, analytics, data modeling and databases, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing, and machine translation.

WP Engine available in AWS Marketplace

WP Engine, a WordPress technology company, announced availability in AWS Marketplace. WP Engine is committed to being a catalyst for digital experiences on WordPress by combining proprietary technology with a modern tech stack. By leveraging AWS, WP Engine enables brands to quickly build and launch fast, secure digital experiences with insights to maximize consumer engagement.

WP Engine’s collaboration with AWS began in 2015 and spans from the core platform infrastructure to WordPress ecosystem products like Amazon Polly integration, AWS Digital Customer Experience Competency status, and now availability in AWS Marketplace. WP Engine offers a range of enterprise-grade, high-resiliency, high-availability solutions on WordPress-optimized AWS architecture. With AWS’s global regions and multi-zone redundancy across all traffic-serving layers, customers benefit from the best uptime protection and risk mitigation with the elimination of single points of failure on the WP Engine WordPress digital experience platform.

Patra and partner on natural language understanding for insurance

Patra, a provider of technology-enabled services for the insurance industry, and, an artificial intelligence solution for natural language understanding and natural language processing (NLU/NLP), announced a partnership that brings efficiencies to a variety of insurance processes. This partnership delivers AI-powered policy checking to the insurance market. By combing’s technology and expertise along with the market power of the InsurConneXtions Alliance members, additional complex solutions are currently being developed for the industry. enables global organizations to leverage its AI-based natural language (NL) platform to automate the reading, understanding, and extraction of meaningful data from structured and unstructured text to augment and expand insights for every process that involves language. By integrating’s AI capabilities, Patra will improve quality, reduce friction, and drive out inefficiencies in the process of manually reviewing and cross-validating dozens to hundreds of pages of text for any given policy. These capabilities will facilitate a deeper understanding of data, enabling previously out-of-reach insights due to the vast and complex nature of language semantics. With close to 80% of the information within the insurance industry being unstructured data, intelligent automation based on human-like understanding is a critical factor for increasing capacity, and reducing inefficiencies and high-risk vulnerabilities.,

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Franz announces Allegro NFS Server 7.0 for Windows

Franz Inc., supplier of graph database technology for knowledge graph solutions, announced Allegro NFS Server 7.0 for Windows with 64-bit performance and support for all current versions of the Windows operating system. Allegro NFS Server 7.0 delivers a high performance, easy-to-install solution for small and large enterprise-wide deployments. Allegro NFS was originally developed for Franz’s internal purposes due to dissatisfaction with free and commercial NFS Servers available on the market and the incredible technical difficulties faced in configuring them on Windows. Since 2002, Allegro NFS has been adopted by many Fortune 500 companies who want reliable and easy-to-configure access to Windows from NFS clients.

Allegro NFS Server 7.0, runs on all the current versions of the Windows operating system including Vista, Server 2003, Server 2008, Server 2012, Windows 7 (32 and 64-bit), Windows 8 (32 and 64-bit), and Windows 10 (32 and 64-bit). Allegro NFS also has version that to run on Windows XP. To evaluate or purchase Allegro NFS Server 7.0, go to,

Apache announces Apache Gobblin as a Top-Level Project

The Apache Software Foundation (ASF) announced Apache Gobblin as a Top-Level Project (TLP). Apache Gobblin is a distributed Big Data integration framework used in both streaming and batch data ecosystems. The project originated at LinkedIn in 2014, was open-sourced in 2015, and entered the Apache Incubator in February 2017. Apache Gobblin is used to integrate hundreds of terabytes and thousands of datasets per day by simplifying the ingestion, replication, organization, and lifecycle management processes across numerous execution environments, data velocities, scale, connectors, and more.

As a scalable data management solution for structured and byte-oriented data in heterogeneous data ecosystems, Apache Gobblin makes the task of creating and maintaining a modern data lake easy. It supports the three main capabilities required by every data team:

  • Ingestion and export of data from a variety of sources and sinks into and out of the data lake while supporting simple transformations.
  • Data Organization within the lake (e.g. compaction, partitioning, deduplication).
  • Lifecycle and Compliance Management of data within the lake (e.g. data retention, fine-grain data deletions) driven by metadata.

Apache Gobblin software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project’s day-to-day operations, including community development and product releases.

DataRobot announces Feature Discovery integration with Snowflake

DataRobot announced the latest integration with Snowflake. Building off of DataRobot’s expanded partnership and existing integration with Snowflake, the new Feature Discovery pushdown integration improves the speed and accuracy of developing models, unlocking new use cases. DataRobot’s Feature Discovery, which has been a part of the DataRobot enterprise AI platform since 2019, automatically discovers, tests, and creates hundreds of valuable new features for machine learning models. This improves models’ accuracy, increasing an organization’s ability to make accurate predictions.

The new Feature Discovery integration with Snowflake delivers this capability to Snowflake users, pushing down data preparation operations into Snowflake to minimize data movement resulting in faster performance and lower operating costs. This allows users to obtain more accurate DataRobot models by accessing more data from Snowflake and leveraging the power of Snowflake’s Data Cloud. With Feature Discovery, the joining, aggregating, and creation of derived features from datasets is done automatically using data science best practices. This lets users build better machine learning models in less time and drive more innovation with AI.

ThoughtSpot and Microsoft Partner on search and AI-driven analytics

ThoughtSpot and Microsoft announced a new agreement to help Azure Synapse customers tap into their cloud data through augmented analytics. ThoughtSpot Cloud will be available on Microsoft Azure, giving customers a means to bring analytics and insights from their data in Azure Synapse Analytics and other cloud data warehouses to their entire organization through search and AI. Customers can equip anyone with the ability to analyze data, find insights, and make informed decisions. Customers can also buy ThoughtSpot through the Azure Marketplace. Highlights of the agreement include:

  • ThoughtSpot Cloud on Microsoft Azure. ThoughtSpot Cloud, the new SaaS platform for search and AI-driven analytics, will be available on Microsoft Azure. Customers will be able to leverage their data in Azure Synapse Analytics, Azure Databricks and other cloud data warehouses.
  • Enhanced support for Azure Synapse Analytics. Deeper collaboration between Microsoft and ThoughtSpot will bring new support for Azure Synapse.
  • Seamless purchasing experience. Customers will be able to buy ThoughtSpot directly in the Azure Marketplace using Azure credits.
  • Product co-development. Ongoing co-development of solutions will enable joint customers to take advantage of the value of their data in Azure Synapse Analytics with ThoughtSpot.

Elastic adds new capabilities across solutions

Elastic announced new capabilities and updates across its Elastic Enterprise Search, Observability and Security solutions. With searchable snapshots, users can retain and search their data on low-cost object stores such as AWS S3, Microsoft Azure Storage, and Google Cloud Storage, which can reduce storage costs. Searchable snapshots support a new cold tier capability, which is now generally available and also available in Elastic Cloud.

Expanded capabilities in Elastic Enterprise Search include a new web crawler for Elastic App Search and support for Box as a content source inside Elastic Workplace Search. The web crawler retrieves information from publicly accessible websites to make that content easily searchable in App Search engines, and the schema is inferred upon ingestion and can be updated in near real time with one click.

New in Elastic 7.11, the beta of schema on read with runtime fields gives users the ability to define the schema for their index at query time. Users can choose between flexibility and cost efficiency with schema on read or fast performance with schema on write. Elastic Observability introduces new topline views for Elastic APM and Elastic Metrics, making it easy for users to quickly spot and triage application and infrastructure performance issues.

Franz announces AllegroGraph 7.1

Franz Inc., supplier of Graph Database technology for AI knowledge graph solutions, announced AllegroGraph 7.1, which provides optimizations for complex queries across FedShard deployments faster. AllegroGraph with FedShard allows infinite data integration through unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution for Enterprise scale analytics. Big Data predictive analytics requires a data model approach that unifies typical enterprise data with knowledge bases such as taxonomies, ontologies, industry terms and other domain knowledge. The Entity-Event Data Model utilized by AllegroGraph puts core ‘entities’ such as customers, patients, students or people of interest at the center and then collects several layers of knowledge related to the entity as ‘events’. The events represent activities that transpire in a temporal context.

The AllegroGraph 7.1 release accelerates complex reasoning across enterprise-scale data by providing users with additional query options. Franz’s Research and Development team discovered an approach that can significantly improve certain SPARQL Path Expression queries across database shards. AllegroGraph’s advanced caching methods and merge join operations provide optimizations to the scalable, parallel distributed query approach that is offered via FedShard. The new release includes support for the RDF* and SPARQL* extensions and extended support for SHACL (SHApe Constraint Language).

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