It’s not just the cross-platform expansion which is new in 2017. New features have also been added to the platform. It should be noted that Microsoft has continued its cloud-first approach. Many of the new features in SQL Server 2017 have been available in Azure SQL. Let’s explore some of the new features of SQL Server 2017.
Comments are now available for reports including Power BI reports. This allows users to add perspective to reports and collaborate with others. Users can also include attachments with their comments. Being able to comment or collaborate on a report provides the next level of self-service reporting making it easier to share information or provide assistance is extremely valuable.
Using Report Builder and SQL Server Data Tools, all report authors can create native DAX queries against supported SQL Server Analysis Services tabular data models by dragging and dropping desired fields in the query designers.
Reporting Services also supports a fully OpenAPI compliant RESTful API.
Graph databases have been widely used in social network and similar type systems allowing users to analyze interconnected data and identify non-obvious connections and materialize new information from existing data. For example, search for restaurants near you, preschools for your child other parents near you rated highly, product recommendations based on purchase history of same products you and others have previously purchased, etc.
A graph database allows you to easily store relationships as many-to-many. Traditionally relational database relationships are typically one-to-many. Although you can create many-to-many relationships in a traditional relational database or schema, the extensions to support querying a graph database or schema provides significant advantages from performance to ease of querying.
A graph schema or database is a collection of node and edge tables where a node represents an entity such as a person or an organization and an edge represents a relationship between the two nodes it connects. SQL Server’s query language has also been extended to support graph queries such as a multi-hop navigation and join free pattern matching. Queries can also look up against existing SQL database tables as well as graph nodes and edges.
Power BI Report Server
It is an on-premises server that enables users to publish Power BI reports and distribute them across the enterprise. Users now have the flexibility to publish their Power BI reports to the cloud with the Power BI service, or manage them on-premises with Power BI Report Server.
Python support added in Machine Learning Services
Machine Learning Services was previously named R Services in SQL Server 2016. Users can use Machine Learning Services (In-Database) to run R or Python scripts in SQL Server. Microsoft Machine Learning Server (Standalone) can be installed for users to deploy and consume R and Python models that don’t require SQL Server.
Automatic Database Tuning
Self-tuning features leverage the Query Store (introduced in SQL Server 2016) which tracks query execution plans and runtime statistics. This allows the database engine to identify queries that have regressed in performance and changed execution plans. If the engine determines that a change in plan has occurred, and the query has regressed in performance, the engine will revert to a previous plan.
SQL Graph and report commenting features are perhaps the most exciting for me. I am looking forward to leveraging these and many more features in the near future.
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