This blog is a continuation of a series of blog posts to share best practices for improving performance and scale when using Azure Database for PostgreSQL service.
With Azure DevOps Projects we want to make it is easy for you to set up a fully functional DevOps pipeline tailored to the development language and application platform you want to leverage.
As Julia White mentioned in her blog today, we’re pleased to announce the general availability of Azure Data Lake Storage Gen2 and Azure Data Explorer. We also announced the preview of Azure Data Factory Mapping Data Flow.
Always On availability groups (AG) provide high availability and disaster recovery capabilities to your SQL Server database, whether on-premises, in the cloud, or a combination of both.
Today, we are excited to announce three new Azure Reserved VM Instances’ (RI) features to provide our customers with additional savings and purchase controls.
Today, Microsoft is announcing new intelligent cloud and intelligent edge capabilities for U.S. government customers.
Azure Data Factory adds new connectors for data ingestion into Azure to empower mordern data warehouse solutions and data-driven SaaS apps: Cosmos DB MongoDB API, Google Cloud Storage, Amazon S3, MongoDB, REST, and more.
Generally available: Lsv2-series Azure Virtual Machines; Generally available: Query Store for Azure SQL Data Warehouse; The Things Network and Azure IoT connect LoRaWAN devices; and so much more.
We are excited to announce public preview availability of Read Replicas for Azure Database for PostgreSQL.
Today, we announced the acquisition of Citus Data, an innovative open source extension to scale out PostgreSQL databases without the need to re-architect existing applications. Citus Data delivers unparalleled performance and scalability by intelligently distributing data and queries across multiple nodes, which makes sharding simple.
Healthcare organizations depend on data-driven decisions. To enable better decisions and better health outcomes, healthcare organizations are moving to the cloud. There, the latest advances in artificial intelligence, machine learning, and analytics can be more easily tested and implemented.
Data integration is complex and helps organizations combine data and business processes in hybrid data environments. The increase in volume, variety, and velocity of data has led to delays in monitoring and reacting to issues.
