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Category: Database (Page 1 of 3)

Databricks introduces Delta Engine and acquires Redash

Databricks announced the availability of Delta Engine and the acquisition of Redash. These new capabilities make it faster and easier for data teams to use its Unified Data Analytics platform for data science, machine learning, and a broad range of data analytics use cases. Delta Engine is a high-performance query engine on cloud data lakes and Redash is an open-source dashboarding and visualization service for data scientists and analysts to do data exploration.

Delta Engine is tailored for use with Delta Lake, the open-source structured transaction layer for data lakes. Organizations can now build curated data lakes that include structured and semi-structured data and run all their analytics on high quality, fresh data in the cloud. Databricks acquired Redash, the company behind the successful Redash open source project, to provide easy-to-use dashboarding and visualization capabilities on these curated data lakes. With Redash, data scientists and SQL analysts can eliminate the complexity of moving data into other systems for analysis.

The open source Redash project was created to help data teams make sense of their data. Data scientists and SQL analysts can easily gather a wide variety of data sources, including operational databases, data lakes, and Delta Lake, into thematic dashboards. The results can be visualized in a wide variety of formats like charts, cohorts, and funnels, and are easily shareable across an organization or with external users. Redash can be used with Databricks today using a free connector, and Redash will be fully integrated into Databricks’ Unified Data Analytics Platform and the Databricks workspace in the coming months and take advantage of capabilities like Delta Engine.

MariaDB releases MariaDB Community Server 10.5

MariaDB Corporation announced the general availability of MariaDB Community Server 10.5, a major release that brings high-performance analytics to the open source database. In a push to mainstream analytics and to make it as popular as MariaDB’s transactional engine, the company added a new, native columnar storage engine to the community database server and a new, native MariaDB Python Connector and Microsoft Power BI integration. All new analytical capabilities in MariaDB Community Server 10.5 are available for free with unrestricted use to broaden adoption of hybrid transactional and analytical processing, and modern analytical approaches.

MariaDB Server is compatible with the widely used MySQL database protocol and therefore supports native integrations with BI and data analysis tools and frameworks. This compatibility also enables access to data in any MariaDB storage engine, including ColumnStore. In addition, MariaDB released two new, native connectors to make data analysis with MariaDB easier and faster. MariaDB Community Server 10.5 including ColumnStore is available immediately for free direct download on the MariaDB website now and through Docker Hub by the end of June. MariaDB Connector/Python and MariaDB Power BI adapter can be downloaded from For customers interested in MariaDB for demanding production environments with built-in high availability and massively parallel processing (MPP), please contact MariaDB for early access to MariaDB Enterprise Server 10.5.

Semantic Web Company and Ontotext partner to advance enterprise knowledge graphs

Ontotext (OT) and Semantic Web Company (SWC) announced a strategic partnership to meet the requirements of enterprise architects such as deployment, monitoring, resilience, and interoperability with other enterprise IT systems and security. Users will be able to work with a feature-rich toolset to manage a graph composed of billions of edges that is hosted in data centers around the world. The companies have implemented an integration of the PoolParty Semantic SuiteTM v.8 with the GraphDB and Ontotext Platform, which offers benefits for numerous use cases:

  • GraphDB powering PoolParty: Most of the knowledge graph management tools out there bundle open-source solutions that are good at managing thousands of concepts, whereas PoolParty bundled with GraphDB manages millions of concepts and entities—without extra deployment overheads.
  • PoolParty linked to high-availability GraphDB cluster: GraphDB can now be used as an external store for PoolParty, which offers a combination of performance, scalability and resilience. This is particularly relevant for organizations intent on developing tailor-made knowledge graph platforms integrated into their existing data and content management infrastructure.
  • Dynamic text analysis using big knowledge graphs: PoolParty can be used to edit big knowledge graphs in order to tune the behavior of Ontotext’s text analysis pipelines, which employ vast amounts of domain knowledge to boost precision. This way the power and comprehensiveness of generic off-the-shelf natural language processing (NLP) pipelines can be custom-tailored to an enterprise.
  • GraphQL benefits for PoolParty: Application developers can now access the knowledge graph via GraphQL to build end-user applications or integrate knowledge graph services with the functionality of existing systems. Ontotext Platform uses semantic business objects, defined by subject matter experts and business analysts, to generate GraphQL interfaces and transform them into SPARQL.,

DataStax unveils Vector: AIOps for Apache Cassandra

DataStax announced the private beta of Vector, an AIOps service for Apache Cassandra. Vector continually assesses the behavior of a Cassandra cluster to provide developers and operators with automated diagnostics and advice, helping them be consistently successful with Cassandra and DataStax Enterprise (DSE) clusters. Vector provides recommendations with detailed background knowledge and offers multiple ways to fix a problem. With this embedded knowledge base, Vector is able to analyze individual nodes, compare behavior to other nodes in the cluster, and serve up recommendations, such as: Cassandra and operating system configuration, schema design, and Cassandra performance and query techniques. Vector features:

  • Automated expert advice – Proactively identifies current and potential issues to help developers and operators solve problems quickly. Automated advice provides contextual learning with background knowledge to build skills.
  • Continuous updates – Rules and advice are continuously updated, deployed to SaaS and on-premises applications, and automatically applied to clusters.
  • Hands-off management – Advanced visualizations of system usage with insightful charting to understand tables, keyspaces, and nodes. Vector helps developers and operators see and understand how the cluster is performing and its configuration without having to log into Cassandra nodes.
  • Cassandra skills development – Helps strengthen Cassandra skills and knowledge by providing detailed advice and recommendations. Vector helps to reduce unexpected and unplanned items.

MongoDB releases MongoDB Cloud platform 4.4 with Atlas Data Lake, Search, and MongoDB Realm

MongoDB, Inc. announced a series of products that comprise the MongoDB Cloud platform that give developers a better way to work with data, wherever it resides. The launch of MongoDB 4.4, general availability of Atlas Data Lake and Atlas Search, and the general availability of MongoDB Realm offers organizations an escape from data silos and fragmented APIs as MongoDB Cloud delivers a developer-optimized, cloud-to-mobile platform. With MongoDB’s document data model, developers can structure data any way the application requires – from rich, hierarchical objects to simple key-value pairs and tables to connected graphs – and then query it with a single API. This gives developers a consistent and productive experience across the broadest set of workloads.

The addition of Atlas Data Lake and Atlas Search to the MongoDB Cloud platform simplifies modern data infrastructure, extends applications with rich search experiences and unlocks analytics for data archived in a data lake. Using the same MongoDB Query Language (MQL) and data model, with Atlas Data Lake a user can run a query and have the data brought back to them: whether it is real-time transactional data in the global Atlas global cloud database or a relevance-based search query with Atlas Search or a long-running analytical query on data in object storage. Using MongoDB Cloud, developers no longer need to deal with the cognitive burden of flipping back and forth between multiple technologies, query languages and data models. MongoDB’s Realm Sync enables bi-directional data synchronization between Realm’s mobile client on the front end and Atlas on the backend. This allows for data to be seamlessly shared between devices and with the backing database without complex conflict resolution and integration code.

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