In a world where self-service and speed of delivery are key priorities in any reporting BI solution, the importance of creating a comprehensive and performant data semantic model layer is sometimes overlooked.
I have seen quite a few occurrences where the relational data store such as Azure SQL Database and Azure SQL Data Warehouse are well structured, the reporting tier is well presented whether that is SQL Server reporting services or Power BI, but still, the performance is not as expected.
Before we drill down to the data semantic model, I always advise that understanding your data and how you want to present and report on it is key. By creating a report that takes the end consumer through a data journey is the difference between a good and a bad report. Report designing should take into account who is consuming and what they want to achieve out of the report. For example, if you have a small number of consumers who need to view a lower level of hierarchical data with additional measures or KPIs, then it may not be suitable to visualize this on the first page. As the majority of consumers may want to view a more
Microsoft Azure Government regions give unparalleled flexibility for US government agencies and their partners. Today, we announce the general availability of industry-leading performance Azure SQL Data Warehouse Compute Optimized Gen2 tier for government customers in the US Gov Arizona and US Gov Texas regions. Azure SQL Data Warehouse is a fast, flexible, and secure analytics platform offering you a SQL-based view across data. It is elastic, enabling you to provision a cloud data warehouse in minutes and scale to terabytes in minutes.
With Azure Government only US federal, state, local, and tribal governments and their partners have access to this dedicated instance that only screened US citizens operate. Customers can choose from six government-only datacenter regions, including two regions granted an Impact Level 5 Provisional Authorization. Moreover, Azure Government offers the most compliance certifications of any cloud provider.
These new locations bring the product worldwide availability count for Compute Optimized Gen2 tier to 28 regions.
More performance, concurrency, and power
Azure SQL Data Warehouse Gen2 brings the best of Microsoft software and hardware innovations to dramatically improve query performance and concurrency. Our customers now get up to five times better performance on average for query workloads, four times more
Gaining insights rapidly from data is critical to competitiveness in today’s business world. Azure SQL Data Warehouse (SQL DW), Microsoft’s fully managed analytics platform leverages Massively Parallel Processing (MPP) to run complex interactive SQL queries at every level of scale.
Users today expect data within minutes, a departure from traditional analytics systems which used to operate on data latency of a single day or more. With the requirement for faster data, users need ways of moving data from source systems into their analytical stores in a simple, quick, and transparent fashion. In order to deliver on modern analytics strategies, it is necessary that users are acting on current information. This means that users must enable the continuous movement from enterprise data, from on-premise to cloud and everything in-between.
SQL Data Warehouse is happy to announce that Striim now fully supports SQL Data Warehouse as a target for Striim for Azure. Striim enables continuous non-intrusive performant ingestion of all your enterprise data from a variety of sources in real time. This means that users can use intelligent pipelines for change data capture from sources such as Oracle Exadata straight into SQL Data Warehouse. Striim can also be used to move fast
This week at Microsoft Ignite 2018, we are excited to announce eight new features in Azure Stream Analytics (ASA). These new features include
Support for query extensibility with C# custom code in ASA jobs running on Azure IoT Edge.Custom de-serializers in ASA jobs running on Azure IoT Edge.Live data Testing in Visual Studio.High throughput output to SQL.ML based Anomaly Detection on IoT Edge.Managed Identities for Azure Resources (formerly MSI) based authentication for egress to Azure Data Lake Storage Gen 1.Blob output partitioning by custom date/time formats.User defined custom re-partition count.
The features that are generally available and the ones in public preview will start rolling imminently. For early access to private preview features, please use our sign up form.
Also, if you are attending Microsoft Ignite conference this week, please attend our session BRK3199 to learn more about these features and see several of these in action.
General availability features Parallel write operations to Azure SQL
Azure Stream Analytics now supports high performance and efficient write operations to Azure SQL DB and Azure SQL Data Warehouse to help customers achieve four to five times higher throughput than what was previously possible. To achieve fully parallel topologies, ASA will transition SQL writes
As customer build their mission-critical analytics solutions using Azure SQL Data Warehouse (SQL DW), Microsoft’s fast, flexible and secure analytics platform, we want to ensure that we give them greater control over the management of their workloads.
Today we are excited to announce a preview of scheduled maintenance for Azure SQL DW. This new feature seamlessly integrates the Service Health Planned Maintenance Notifications, Resource Health Check Monitor and the Azure SQL Data Warehouse maintenance scheduling service. Customers can now choose a preferred maintenance window based on their business requirements, giving them the flexibility to select times when system maintenance is to be performed.
With the power of these combined services and the new functionality developed by the Azure SQL DW, the team provides a predictable and stable service upgrade and maintenance experience. Azure SQL Warehouse now gives you the tools you need to plan around this necessary service maintenance and minimize the impact on your day to day operations. You can learn more about how to use this function.
The new functionality can be accessed via your data warehouse overview blade and a new the resource menu option.
Azure SQL Data Warehouse maintenance scheduling is now available in the
Azure SQL Data Warehouse (SQL DW) is a fast, flexible, and secure analytics platform that helps our customers transform data into actionable insights. Starting today, Azure SQL DW customers can seamlessly get rich insights and build customizable dashboard widgets to better manage and tune their workloads.
This experience is made real by the general availability of Azure Data Studio, a tool that provides a Transact-SQL (T-SQL) code editor with IntelliSense, an integrated terminal for common command line tools, and customizable database widgets encapsulated through extensions. The Azure SQL DW insights extension can be leveraged with Azure Data Studio to automatically render dashboard widgets to help surface tuning insights into your data warehouse. These dashboard views are automatically refreshed to help you seamlessly apply best practices and ensure that your workload is optimized for performance within a single tool.
Currently, the following dashboard widgets are immediately available when downloading the extension.
Data distribution across your data warehouse
You can visualize the data distribution across your data warehouse to immediately detect whether your data warehouse is suffering from physical data skew which can impact query performance.
Data is more critical than ever in all aspects of modern business. As more organizations embark on digital transformation and embrace a data-driven culture, a common IT challenge is building the definitive source of truth for trusted data, breaking infrastructure and functional silos and bringing all data types together. Thousands of customers are now using Azure SQL Data Warehouse (SQL DW) to take advantage of the fast, flexible, and secure analytics platform to gain deeper insights and make better decisions.
Azure SQL DW has been engineered to deliver lighting fast query performance in a secure, cost-effective cloud solution. With the release of Compute Optimized Gen2 tier earlier this year, Azure SQL DW has set new industry standards for fastest query performance and highest query concurrency. We have been recognized as a leader in price/performance through TPC-H benchmarks for cloud data warehousing. To make this high-performance analytics platform more accessible, today we are announcing multiple service usability and security enhancements to allow our customers to build and manage an enterprise-grade data warehouse with ease.
We are also excited to share our recognition as the leader in a recent TPC-DS performance benchmark research. Fivetran, a data integration vendor, recently
Gaining insights rapidly from data is critical to being competitive in today’s business world. With a modern data warehouse, customers can bring together all their data at any scale into a single source of truth for use cases such as business intelligence and advanced analytics.
A key component of successful data warehousing is replicating data from diverse data sources into the canonical data warehousing database. Ensuring that data arrives in your data warehouse consistently and reliably is crucial for success. Data integration tools ensure that users can successfully connect to their critical data sources while moving data between source systems and their data warehouse in a timely yet reliable fashion.
We’re excited to announce that Fivetran has certified their zero maintenance, zero configuration, data pipelines product for Azure SQL Data Warehouse. Fivetran is a simple to use system that enables customers to load data from applications, files stores, databases, and more into Azure SQL Data Warehouse.
– George Fraser, CEO and Co-Founder at Fivetran
We’re also pleased
Today, we announce the broader regional availability of the industry-leading performance provided by Azure SQL Data Warehouse (Azure SQL DW). Azure SQL DW is a fast, flexible, and secure analytics platform offering you a SQL-based view across data. It is elastic, enabling you to provision a cloud data warehouse and scale to terabytes in minutes.
Compute Optimized Gen2 tier is now rolled out to three additional Azure regions— Australia Central, Australia Center 2, and France Central. These new locations bring the product worldwide availability count for Compute Optimized Gen2 tier to 22 regions.
Azure SQL DW Gen2 brings the best of Microsoft software and hardware innovations to dramatically improve query performance and concurrency. Our customers now get up to 5 times better performance, on average, for query workloads, 4 times more concurrency, and 5 times higher computing power compared to the previous generation. Azure SQL DW can also serve 128 concurrent queries from a single cluster, the maximum for any cloud data warehousing service.
Begin today and experience the speed, scale, elasticity, security, and ease of use of a cloud-based data warehouse for yourself. You can see this blog post for more info on the capabilities and features
We are pleased to announce general availability of the expanded Blueprint for Federal Financial Institution Examination Council (FFIEC) regulated workloads. As more financial services customers moving to the Azure cloud platform, we wanted to expand the Blueprint to explain how to deploy four different reference architectures in a secure and compliant way. It also takes the guesswork out of figuring out what security controls Microsoft implements on your behalf when you build on Azure, and how to implement the customer-responsible security controls.
We have built an end-to-end solution for moving compliant workloads to Azure, and reducing the time required to do so, leveraging Microsoft’s experience in working with banks in the U.S. and around the globe. Why start from scratch when you don’t have to? This Blueprint provides guidance for the deployment of PaaS Web Applications, IaaS Web Applications, Data Analytics, and Data Warehouse architecture in Azure suitable for the collection, storage, and retrieval of sensitive financial data regulated by the FFIEC.
The FFIEC Blueprint now consists of: Four reference architectures, with supporting deployment guidance Threat models to understand the points of potential risk Security control implementation mappings which describes how the reference architecture supports each control Customer responsibility matrix