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Created Service Type Note Context Reference
8/13/2019 Portal New Features New Azure reservations features can help you save more on your Azure costs, easily manage reservations, and create internal reports. Based on your feedback, we’ve added the following features to reservations: Azure Databricks pre-purchase plan App Service Isolated Stamp Fee reservations Ability to automatically renew reservations Ability to scope reservations to resource group Enhanced usage data to help with charge back, savings, and utilization API to get prices and purchase reservations Link Link Details
8/6/2019 Databricks Region Update Azure Databricks is now generally available in additional regions—South Africa and South Korea. These additional locations bring the product worldwide availability count to 26 regions backed by a 99.95 percent SLA. Link Link Details
8/6/2019 Databricks Pricing Update Today, with the Azure Databricks Unit pre-purchase plan, you can start unlocking the benefits of Azure Databricks at significantly reduced costs when you pre-pay for Databricks compute for a one or three-year term. With this new pricing option, you can achieve savings of up to 37 percent compared to pay-as-you-go pricing. Link Link Details
2/8/2019 Data Factory New Features Today, we are excited to announce the release of a set of new ADF connectors which enable more scenarios and possibilities for your analytic workloads. For example, you can now: Ingest data from Google Cloud Storage into Azure Data Lake Gen2, and process using Azure Databricks jointly with data coming from other sources. Bring data from any S3-compatible data storage that you may consume from third party data vendors into Azure. Copy data from MongoDB and others to Azure Cosmos DB's API for MongoDB for application consumption. Retrieve data from any RESTful endpoint as an extensible point to reach hundreds of SaaS applications. Link Link Details
2/8/2019 Stream Analytics New Features Azure Stream Analytics users can now partition output to Azure Blob storage based on custom date and time formats. This feature greatly improves downstream data-processing workflows by allowing fine-grained control over the blob output especially for scenarios such as dashboarding and reporting. Additionally, partition by custom date and time formats enables stronger alignment with downstream Hive-supported formats and conventions when consumed by services such as Azure HDInsight or Azure Databricks. Link Link Details
12/7/2018 Data Lake Store Preview Features Today, we are very pleased to announce significant updates to the ADLS Gen2 preview that will allow an even greater experience for customers. These updates include integration with Databricks and HDI, Azure Storage Explorer support and more. Link Link Details
12/4/2018 Time Series Insights New Features Time Series Insights is releasing a new set of IoT capabilities in preview that enable you to harness the value of IoT data to generate business-critical insights. The new features include: A scalable, performance-optimized and cost-optimized time series data store that enables a cloud-based IoT solution to trend years’ worth of time series data in seconds. Semantic model support to describe the domain and metadata associated with the derived and non-derived signals from assets and devices. A significantly enhanced user experience that combines asset-based data insights with rich, ad-hoc data analytics to drive business and operational intelligence. Integration with advanced machine learning and analytics tools like Databricks, Spark, Jupyter notebooks, and more to help you tackle time series data challenges in new ways. Link Link Details
9/25/2018 SQL Data Warehouse New Feature Today, we are excited to announce near real-time analytical capabilities in Azure SQL Data Warehouse. This architecture is made possible through the public preview of Streaming Ingestion into SQL DW from Azure Databricks Streaming Dataframes. Link Link Details
9/24/2018 Databricks Preview Feature We are happy to announce Azure Databricks users can directly stream data into Azure SQL Data Warehouse using the Structured Streams. This enables customers to visualize and report on near real-time data in SQL DW backed by real time streaming pipelines built with Structured Streams, resulting in faster decision making across the enterprise. Link Link Details
9/24/2018 Databricks Preview Feature Azure Databricks comes built in with the ability to connect to Azure Data Lake Storage, Cosmos DB, SQL DW, Event Hubs, IoT Hubs, and several other services. We now have the ability to allow customers to store connection strings or secrets in the Azure Key Vault.Azure Key Vault can help you securely store and manage application secrets reducing the chances of accidental loss of security information by centralizing the storage of secrets.When using Key Vault with Azure Databricks to create secret scopes, data scientists and developers no longer need to store security information such as SAS tokens or connections strings in their notebooks. Access to a key vault requires proper authentication and authorization before a user can get access. Authentication establishes the identity of the user, while authorization determines the operations that they are allowed to perform. Link Link Details

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