Cross-platform compatibility is increasingly important in our customers’ diverse and rapidly changing data estates. SQL Server 2017 will be the first version of SQL Server compatible with Windows, Linux, and Linux-based container images for Docker. In addition to running on Windows Server, the new version will also run on Red Hat Enterprise Linux, SUSE Enterprise Linux Server, and Ubuntu. It can also run inside Docker containers on Linux or Mac, which can help your developers spend more time developing and less on DevOps. key feature enhancement in SQL Server 2017 is the ability to run the Python language in-database to scale and accelerate machine learning, predictive analytics and data science scripts. The new capability, called Microsoft Machine Learning Services, enables Python scripts to be run directly within the database server. Microsoft R Server 9.1, which takes the concept of bringing intelligence to where your data lives to Hadoop and Spark, as well as SQL Server. In addition to several advanced machine learning algorithms from Microsoft, R Server 9.1 introduces pretrained neural network models for sentiment analysis and image featurization, supports SparklyR, SparkETL, and SparkSQL, and GPU for deep neural networks. Integration of AI functions into databases, data lakes, and the cloud to simplify the deployment of intelligent applications. The second is the use of AI within our services to enhance performance and data security. The third is flexibility—the flexibility for developers to compose multiple cloud services into various design patterns for AI, and the flexibility to leverage Windows, Linux, Python, R, Spark, Hadoop, and other open source tools in building such systems. Process massive unstructured data, such as text/images, extract sentiment, age, and other cognitive features using Azure Data Lake, and query/analyze these by content. This enables what I call “Big Cognition—it’s not just extracting one piece of cognitive information at a time, and not just about understanding an emotion or whether there’s an object in an individual image, but rather it’s about integrating all the extracted cognitive data with other types of data, so you can perform powerful joins, analytics, and integrated AI. Powerful integration of data and AI is the seamless integration of DocumentDB with Spark to enable machine learning and advanced analytics on top of globally distributed data. SQL Server will run just as fast on Linux as on Windows, as you’ll see in the newly published 1TB TPC-H benchmark world record nonclustered data warehouse performance achieved with SQL Server 2017 on Red Hat Enterprise Linux and HPE ProLiant hardware. SQL Serve 2017 can store and analyzing graph data relationships. This includes full CRUD support to create nodes and edges and T-SQL query language extensions to provide multi-hop navigation using join-free pattern matching. In addition, SQL Server engine integration enables querying across SQL tables and graph data. And, you can use all of your existing SQL Server tools to work with graph data.