See Arun Ulagaratchagan's blog post to read the full storyMicrosoft Fabric Preview Announcement.
In today's data-driven world, a data warehouse is an essential part of any business strategy. Its goal is to help companies efficiently manage and analyze large amounts of data, enabling them to make informed decisions and drive growth. However, as the volume of data collected continues to increase, so do the challenges associated with its management, which is compounded by the age of artificial intelligence. Traditional data warehouse solutions have become complex and costly, often resulting in data duplication, vendor lock-in and governance issues.
We are excited to announce the preview of Synapse Data Warehouse in Microsoft Fabric! Synapse Data Warehouse is the next generation data warehouse in Microsoft Fabric, it is the first transactional data warehouse that natively supports open data formats, enabling IT teams, data engineers and business users to seamlessly collaborate and extract actionable data from their data insights, all without compromising enterprise security or governance. Just like the previous generation of data warehouses, SQL offers multi-table ACID transactional guarantees. It builds on the proven SQL Server query optimizer and distributed query processing engine, but is supported by the following key enhancements, adding significant new value to the enterprise -
- fully managed: This new data warehouse is a fully managed SaaS solution that effortlessly extends modern data architecture to both professional developers who love to code and citizen enthusiasts without coding skills. Jobs that previously took months for businesses to complete can now be efficiently completed in minutes.
- No need to configure and manage resources:Instead of provisioning a dedicated cluster, it is based on a completely serverless computing infrastructure where resources are provisioned in milliseconds as job requests come in. Businesses benefit from resource efficiency and only pay for the resources they use.
- Separation of storage and computing:The compute nodes used are independent of the storage, enabling enterprises to scale and pay for either one independently.
- Open Data Standards:Data is not locked in a proprietary SQL Server format, but is stored in the Delta-Parquet open data standard in Microsoft OneLake, providing interoperability not only with all workloads in Fabric, but also with the Spark ecosystem performance without any data movement.
- Cross query:Thanks to the support of open data standards, data in the lake can be queried and cross-joined without making any copies of the data, whether processed by Fabric workloads or any other compute engine.
- Automatic scaling:It automatically scales up resources instantly as query and usage demands increase, and scales down when those resources are no longer needed, all without user intervention.
- self-optimization: It automatically detects and isolates workloads to provide predictable performance. Optimal performance is based on automatic activity-based and multi-tier caching. The resulting query plan is optimal. No need to hire highly skilled engineers to manage workload groups or tune data warehouses.
- fully integrated: It is fully integrated with all Fabric workloads out of the box for all developers. Users can continue to benefit from the rich functionality of the SQL engine using the T-SQL language or a simple user interface. All of this comes with the continued strength of the SQL ecosystem.
Let's dive into its features.
Analytics is the set of activities that requires the ability to ingest, prepare and analyze data to create business semantics, machine learning models and BI reports. It requires collaboration between IT, data engineers, business analysts, and data scientists across the organization. As data is shared or discovered, it needs to be secured and managed. Synapse Data Warehouse makes this easy with the following key features.
Fabric has a dedicated data warehouse home page where a new Warehouse can be created with just a name and sensitivity label. No configuration or setup is required. The user interface is the familiar relational database experience. Warehouse Explorer exposes schemas, tables, stored procedures and all other database objects. Anyone new to warehousing can start with the Warehouse sample!
Data can be loaded into the warehouse by writing T-SQL queries using the COPY command. It is also available from the Warehouse Editor using the Data Factory pipelines available now or the upcoming Dataflow Gen2. Pipelines provide connections to multiple data sources, subselect tables, and the ability to preview data. Tables are created automatically, and column data types are automatically mapped from source types to Parquet. Data pulled into the warehouse is stored in OneLake. Table transactions are guaranteed by the SQL calculation engine, and Delta logs are published periodically. Delta-Parquet in OneLake can be viewed using OneLake Explorer and easily accessed using Notebooks.
Professional developers can go on to write T-SQL code to query and analyze data. Citizen developers can use the Visual Query Editor, a drag-and-drop user interface for compiling queries and even performing complex joins and groupings. T-SQL is automatically generated and can also be edited.
Traditionally, when users want to merge data from data warehouses and data lakes, they face the cumbersome process of creating pipelines, transferring data, and copying data. In Microsoft Fabric, users can create virtual warehouses that contain data from any source in Fabric, be it Warehouse or Lakehouse, across any storage or any cloud. As long as the data is in the Delta table, you can create a shortcut to it and query or cross-join it using the T-SQL three-part naming convention or the visual query editor.
Synapse Data Warehouse is fully integrated with Power BI in Microsoft Fabric. Power BI datasets are automatically generated and kept in sync with the data in the warehouse. Users don't have to worry about Power BI schemas and make trade-offs as their data volume grows or needs to meet performance or security requirements. The experience of creating relationships between tables, adding Power BI semantics called measures is all in the Warehouse Editor. The new Power BI reports are a one-click experience!
The data warehouse supports traditional T-SQL security constructs. GRANTS, REVOKE, or DENY can be used to protect objects through Warehouse. Object-level security allows granular access to control collaboration and consumption.
Data governance is critical to business. As with other Microsoft Fabric experiences, sensitivity labels can be applied to a warehouse, which can be passed down to any project downstream. You can also view end-to-end lineage information.
Data Warehouse in Microsoft Fabric is currently in preview. The focus of this preview release is to provide a rich set of SaaS features and functionality to suit all skill levels. The preview delivers on the promise of a simplified experience through an open data format based on a single copy of the data. While this release does not focus on performance, concurrency, and scale, additional capabilities to handle complex workloads and deliver industry-leading performance will be implemented as we move toward general availability of the data warehouse in Microsoft Fabric.
Existing Azure Synapse Dedicated SQL Pools will continue to provide a robust enterprise-grade PaaS solution. Synapse Data Warehouse in Microsoft Fabric is an evolution in the form of a simplified SaaS solution that can connect to existing PaaS offerings. Customers will be able to upgrade from current products to Fabric at their own pace.
In addition to preview releases, feature-rich features are released each month, with details updated through monthly blog posts. Here are just a few features to give you a sneak peek:
- automatic statistics: Automatically calculate statistics in the warehouse when executing queries, ensuring users get the best performance.
- Zero copy table clone:Users can create zero-copy table clones using T-SQL commands.
- The data warehouse in the deployment pipeline:Users can use repositories in deployment pipelines and deploy to development, test, and production workspaces. They can compare schemas, roll back changes, and automate by using the REST API.
- Data Warehouse Git Integration: Users can connect to Git repositories, develop their repository SQL scripts and code, manage versions, commits and pull requests, and download SQL projects.
- Data Warehouse REST API: Users can use public REST APIs to automatically create, manage, and manage their data warehouses.
- Warehouse integration with Microsoft Fabric Monitoring Hub: Users can use Monitoring Center to view query details end-to-end, monitor and resolve performance issues of solutions.
- Data Flow Gen2: Users can use Dataflows Gen2 with the familiar Power Query experience to transform and load data into Warehouse.
Microsoft Fabric is currently in preview. Sign up for a free trial to try everything Fabric has to offer—no credit card information required. Everyone who signs up gets a fixed trial volume of Fabric that can be used for any feature or functionality from integrating data to creating machine learning models. Existing Power BI Premium customers can simply enable Fabric through the Power BI admin portal. After July 1, 2023, Fabric will be enabled for all Power BI tenants.
Learn more about Synapse Data Warehouse in Fabric and how customers are using it at the following Build 2023 sessions:
- Modernize your enterprise data warehouse and generate value from data
If you want to learn more about Microsoft Fabric, consider:
- Sign up for a free Microsoft Fabric trial
- Visit the Microsoft Fabric website
- Read the more in-depth Fabric Experience announcement blog:
- Data Factory experience in the Fabric blog
- Synapse Data Engineering Experience in the Fabric Blog
- Synapse Data Science Experience in the Fabric Blog
- The Synapse Real-Time Analytics Experience in the Fabric Blog
- Power BI announcement blog
- The Data Activator experience in the Fabric blog
- Management and Governance in the Fabric Blog
- OneLake in the Fabric blog
- Microsoft 365 Data Integration in the Fabric blog
- Dataverse and Microsoft Fabric Integration Blog
- Explore the Fabric technical documentation
- Read the free eBook on getting started with Fabric
- by exploring the fabricguided tour
- Watch the free FabricWebinar Series
- join infabric communityPost your questions, share your feedback, and learn from others
- to visitMicrosoft Architecture PhilosophySubmit improvement suggestions and vote on ideas from your peers
"Microsoft Azure Synapse Analytics is an expensive solution and there is a license needed to use it. My company has an enterprise license."Is Azure Synapse a data warehouse? ›
Azure Synapse Analytics (formerly Azure SQL Data Warehouse) is a cloud data warehouse by Microsoft, which provides a unified workspace for building end-to-end analytics solutions by bringing together enterprise data warehouse and big data analytics.Is Microsoft Synapse a database? ›
Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources—at scale.What is Microsoft Synapse used for? ›
The Synapse Studio provides a workspace for data prep, data management, data exploration, enterprise data warehousing, big data, and AI tasks. Data engineers can use a code-free visual environment for managing data pipelines. Database administrators can automate query optimization.Is Synapse better than Databricks? ›
Azure Synapse has built-in support for automating Machine Learning workflows. However, it does not offer complete Git support or a collaborative environment. Databricks, on the other hand, incorporates streamlined ML workflows that give GPU-enabled clusters and substantial version control through Git.Which is better Databricks or Synapse? ›
Azure Synapse utilizes a 3-component architecture; Data storage, processing, and visualization in a single, unified platform. On the other hand, Databricks utilizes a lakehouse architecture that enables the best data warehouse and data lake features into one continuous platform.What is the difference between Azure data warehouse and Synapse? ›
Weighing Your Data Needs
Azure SQL DB provides an easy-to-maintain data storage with predictable cost structures while Azure synapse provides control and features such as pausing computational tasks in order to efficiently manage costs.
Azure Synapse Analytics is specifically designed to handle large-scale analytical workloads, while Azure SQL Database is better suited for smaller analytical workloads. Azure Synapse Analytics provides built-in support for advanced analytics tools like Apache Spark and machine learning services.What is the difference between synapse and data warehouse? ›
Basically, Azure Synapse completes the whole data integration and ETL process and is much more than a normal data warehouse since it includes further stages of the process giving the users the possibility to also create reports and visualizations.Does Synapse use SQL? ›
Azure Synapse SQL is a big data analytic service that enables you to query and analyze your data using the T-SQL language. You can use standard ANSI-compliant dialect of SQL language used on SQL Server and Azure SQL Database for data analysis.
Is Azure Synapse a ETL tool? Azure Synapse uses ELT rather than ETL. ETL is a method of loading data in the pattern of Extract, Transform, and Load. Azure Synapse uses an Extract, Load, and Transform process since it does not require the resources for data transformation prior to loading.Is Synapse a PaaS or SAAS? ›
Azure Synapse is primarily a Platform as a Service (PaaS) solution with free Azure Synapse Workspace development environment on top of those resources.Does Synapse use Python? ›
Synapse notebooks support four Apache Spark languages: PySpark (Python)What is Synapse data warehouse? ›
Azure Synapse Analytics is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems. It brings together the best of SQL technologies used in enterprise data warehousing, Apache Spark technologies for big data, and Azure Data Explorer for log and time series analytics.What is the difference between snowflake and Synapse? ›
Snowflake is a serverless solution with fully independent storage and computation processing layers based on the ANSI SQL. Azure Synapse consists of built-in support for AzureML to handle machine learning workflows. A robust machine-learning environment is available at Databricks for the creation of various models.Why Snowflake is better than Synapse? ›
Because Snowflake and Synapse have extremely diverse performance capabilities, making a straight performance comparison is challenging. Snowflake typically outperforms Synapse significantly with no fine-tuning, as with every cloud data platform, because it is a SaaS service while Synapse is a PaaS-based solution.What is the difference between SQL Server and Synapse analytics? ›
While a traditional SQL database is dependent on the computational resources of a single machine, a Synapse SQL Pool can distribute the processing of tables across up to 60 compute nodes depending on the service level. The more DWU's you've assigned, the more compute nodes will be used.Why is Databricks so popular? ›
Why is Databricks popular? Databricks is a cloud-based data processing and analysis platform that enables users to easily work with large amounts of data. The platform offers a unified workspace for all users' data, making it easy to access and process data from a variety of sources.Should I use Azure data Factory or Synapse? ›
Synapse allows you to collect, transform, and analyze data from just one platform. However, Azure Data Factory is only suitable for data engineers to streamline data collection processes with in-built processes. If you only want to connect and transform data without writing code, you should embrace Azure Data Factory.What is Azure Synapse competitors? ›
Top 10 Alternatives to Azure Synapse Analytics
Google Cloud BigQuery. Databricks Lakehouse Platform. Snowflake. Amazon Redshift.
Synapse Spark, in terms of the Lakehouse pattern, allows you to develop code-first data engineering notebooks using the language of your choice (SQL, Scala, Pyspark, C#).What is the old name of Microsoft Synapse? ›
Understanding Azure Synapse Analytics (formerly SQL DW)Is Azure Synapse an ETL? ›
Traditional SMP dedicated SQL pools use an Extract, Transform, and Load (ETL) process for loading data. Synapse SQL, within Azure Synapse Analytics, uses distributed query processing architecture that takes advantage of the scalability and flexibility of compute and storage resources.Is Azure Synapse a relational database? ›
Simply put, Azure Synapse Analytics is an evolution of Azure SQL Data Warehouse. Azure SQL Data Warehouse was a massively parallel processing (MPP) cloud-based, scale-out, relational database, designed to process and store large volumes of data within the Microsoft Azure cloud platform.When should I use Azure Synapse? ›
We use this solution to analyze big data. We collect data from different sources then we analyze that data with Synapse. I mainly use Synapse Analytics to gather data, do aggregations, and generate reports. Our primary use case for Azure Synapse is as a data warehouse, for creation of data pipelines.Is Azure Synapse a NoSQL database? ›
Azure Synapse Analytics (Azure SQL Data Warehouse) Microsoft Azure Cosmos DB is Microsoft's Big Data analysis platform. It is a NoSQL database service and is a replacement for the earlier DocumentDB NoSQL database.What is the difference between Synapse serverless and SQL pool? ›
The basic differences between Synapse Serverless and the Dedicated pool are obvious: While the serverless doesn't store data, only access data from storage accounts and scale the MPP environment automatically, the dedicate SQL Pool keeps a static number of servers according to the service level we choose and a constant ...What are the 3 types of data warehouse schema? ›
A database uses relational model, while a data warehouse uses Star, Snowflake, and Fact Constellation schema.Can Synapse store unstructured data? ›
Structured and unstructured data stored in your Synapse workspace can also be used to build knowledge mining solutions and use AI to uncover valuable business insights across different document types and formats including from Office documents, PDFs, images, audio, forms and web pages.Does Synapse include data factory? ›
Azure Data Factory and its equivalent pipelines feature within Azure Synapse itself provide a fully managed cloud-based data integration service. You can use the service to populate an Azure Synapse Analytics with data from your existing system and save time when building your analytics solutions.
Azure Synapse is ideal for OLAP workloads with clearly defined read and write tasks. This approach accelerates large workloads and complex queries by decoupling and parallelizing complex tasks. In this case, data is usually stored in a denormalized form using a schema.Is Synapse cloud based? ›
Azure Synapse Analytics is a scalable and cloud-based data warehousing solution from Microsoft.What are the 3 components of Synapse analytics? ›
What are the main components of Azure Synapse Analytics? An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics.How to extract data from Synapse? ›
- Create all required Connections.
- Create a data exchange Format.
- Create a Flow to load data in Synapse Analytics.
- Set Synapse Analytics Connection.
- Set the source and the destination.
- Set data exchange Format configured in step 2.
- Set the optional parameters.
Apache Synapse is free and open source software distributed under the Apache Software License 2.0.What API does Synapse use? ›
The API clients available to interface and access data within Synapse are: command line, python, and R.What is the size of database in Synapse? ›
Azure Synapse is a good fit for workloads and data sizes of 1TB and more. It also provides a maximum of 240 TB database limit for the row store and unlimited storage for column store tables.Is Synapse a lifetime? ›
Excitatory synapses have a wide range of PSD95 lifetimes extending from hours to several months, with distinct spatial distributions in dendrites, neurons, and brain regions.Does Synapse work on Windows 10? ›
Note: Razer Synapse 3 is only compatible with Windows 10 64-bit or Windows 11. Go to Razer Synapse 3 download page and click "Download Now". Run the installer. Select "RAZER SYNAPSE".Is Synapse based on spark? ›
Apache Spark in Azure Synapse Analytics is one of Microsoft's implementations of Apache Spark in the cloud. Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage.
- Open SQL Server Management Studio (SSMS).
- In the Connect to Server dialog box, fill in the fields, and then select Connect: Server name: Enter the server name previously identified. ...
- Expand your Azure SQL Server in Object Explorer.
To create a dataset with the Synapse Studio, select the Data tab, and then the plus sign icon, to choose Integration dataset. You'll see the new integration dataset window to choose any of the connectors available in Azure Synapse, to set up an existing or new linked service.Is Snowflake better than SQL? ›
Scalability: While SQL Server is known for being reliable, it can struggle when it comes to scaling, especially for larger data sets. Snowflake, on the other hand, is designed for scalability and can handle petabytes of data with ease.Why Snowflake is better than SQL? ›
SQL Server gives you complete control over the database backup schedule, high data availability and disaster recovery, encryption used, amount of logging, etc. With the Enterprise Edition, Snowflake assures total data security with customer-managed encryption keys and has HIPAA and PCI compliance.How do I reduce the cost of Azure synapse? ›
Other ways to manage and reduce costs for Azure Synapse
Choose the node sizes appropriately to match your preference for performance vs. cost. Use autoscale to re-size pools when needed.
The architect category was the most expensive cloud resource in every geography except for Canada and the Middle East/Africa (MEA), and architects were second in both of those geographies. In the United States, the median cost of an architect was $125,000.Which is the most expensive data storage type Azure? ›
Azure Storage access tiers include: Hot tier - An online tier optimized for storing data that is accessed or modified frequently. The hot tier has the highest storage costs, but the lowest access costs.Is Snowflake better than Azure Synapse? ›
Because Snowflake and Synapse have extremely diverse performance capabilities, making a straight performance comparison is challenging. Snowflake typically outperforms Synapse significantly with no fine-tuning, as with every cloud data platform, because it is a SaaS service while Synapse is a PaaS-based solution.What is the difference between snowflake and synapse? ›
Snowflake is a serverless solution with fully independent storage and computation processing layers based on the ANSI SQL. Azure Synapse consists of built-in support for AzureML to handle machine learning workflows. A robust machine-learning environment is available at Databricks for the creation of various models.What is the difference between Azure Synapse and data Factory? ›
Synapse allows you to collect, transform, and analyze data from just one platform. However, Azure Data Factory is only suitable for data engineers to streamline data collection processes with in-built processes. If you only want to connect and transform data without writing code, you should embrace Azure Data Factory.
- Data Warehouse: Ability to integrate with various data platforms and services.
- Descriptive/Diagnostic Analytics: Use T-SQL queries against the Synapse database to perform data exploration and discovery.
The data stored in Azure is encrypted by default. Azure also allows companies to generate multiple data replicas and store them in any data centre to provide multi-layered security. Moreover, Security Development Lifecycle (SDL) is a leading security process on which Azure has been designed.Is Amazon cheaper than Azure? ›
The cost of running Windows instances on AWS can be up to 5X more expensive than using your existing Windows SQL Server and SQL licenses with Azure. The reason is that you have to purchase Windows licenses separately when using AWS. This discounting model is also available for SUSE Linux and RedHat subscriptions.Which Azure database is cheapest? ›
In Azure VM, you can have the SQL Enterprise Edition, SQL Standard Edition and SQL Web Edition. The Enterprise edition is the most expensive and it includes several features not included in the Standard or Web edition. The Web edition is the cheapest option.What are the 5 types of storage in Azure? ›
- Azure Blob Storage. Blob is one of the most common Azure storage types. ...
- Azure Files. Azure Files is Microsoft's managed file storage in the cloud. ...
- Azure Queue Storage. ...
- Azure Table. ...
- Azure Managed Disks.
Azure ultra disks are the highest-performing storage option for Azure virtual machines (VMs). You can change the performance parameters of an ultra disk without having to restart your VMs. Ultra disks are suited for data-intensive workloads such as SAP HANA, top-tier databases, and transaction-heavy workloads.Which is the absolute cheapest way to store data in Azure? ›
Consider Azure Blob Storage Block Blobs instead of storing binary image data in Azure SQL Database. Blob storage is cheaper than Azure SQL Database.Is Synapse a PaaS or SaaS? ›
Azure Synapse is primarily a Platform as a Service (PaaS) solution with free Azure Synapse Workspace development environment on top of those resources.Is Azure Synapse a ETL tool? ›
Traditional SMP dedicated SQL pools use an Extract, Transform, and Load (ETL) process for loading data. Synapse SQL, within Azure Synapse Analytics, uses distributed query processing architecture that takes advantage of the scalability and flexibility of compute and storage resources.