Microsoft’s SQL Server 2017 is a significant step towards making SQL Server the preferred platform. SQL Server 2017 offers a wide range of data types and development languages. It also allows us to run SQL Server on Linux, Linux Docker Containers, or Windows. These new features can have a significant impact on your data.
What to Expect in SQL Server 2017
Python Integration

SQL Server 2017 has one of the greatest advantages: R integration into the SQL Server database engine. Users can use the “sp_execute_external_script” stored procedure to run the R code to take advantage of parallelism in the database engine. An experienced user may have noticed that @language is the first parameter in this stored procedure. Microsoft designed this stored procedure as an open-ended function and has added Python as the second supported languages. Python is a powerful scripting language that can be read easily and is widely used by IT administrators, developers, data scientists, and data analysts. Python can also leverage external statistical packages for data manipulations and statistical analyses. This capability is enhanced by Transact-SQL, which allows you to combine it with T-SQL.
Azure PaaS: Simplified Application Migration

The Cloud has seen major changes, and compatibility has improved over time. There are still some gaps, such as cross-database query and SQL CLR, that need to be addressed. Azure Managed Services is the solution to this problem. Azure Managed Services is a hybrid Platform as a Server (PaaS)/Infrastructure as a Service (IaaS) and is a part of a new feature called Cross-Database Query Capability. In an on-premise environment, multiple databases commonly exist on the same instance, and a single query can reference separate databases by using the “database.schema.table” notation. SQL Database does not allow you to reference multiple databases in a single query. This has resulted in many migrations to the platform being limited by the code that must be rewritten. Cross-database queries are now possible in Managed Instances, which simplifies the process for migrating applications to Azure PaaS offerings. It also increases the number ISV applications that can run on PaaS.
Azure SQL Database Threat Detection Improved

The Azure SQL Database Threat Identification detects SQL Injection and identifies potential SQL Injection vulnerability. It also provides login monitoring. This can be activated at the SQL Database level by activating auditing and configuring notifications. Administrators are notified if the threat detection engine detects unusual behavior.
Graph Database
The Graph Database is now part of SQL Server 2017’s core database engine. Relational databases, despite their name, struggle to manage relationships between data objects. This includes hierarchy management. The graph database introduces the concepts of Nodes and Edges. Edges are relationships between nodes. Nodes represent entities. Data properties can be associated with both Edges and Nodes. SQL Server 2017 also uses extensions within the T-SQL language for join-less queries that use matching and return related values.
System Performance Management Improved with Adaptive query Optimization
The biggest challenge for a DBA in managing system performance is The query optimizer generates new execution plan as data changes occur. These plans may sometimes be less than optimal. SQL Server 2017’s adaptive query optimization can evaluate the execution time of a query and compare it to its history. This technology builds on the technology introduced in SQL Server 2016. Adaptive Query optimization can then improve the execution plan for the next query.
Sub-Title Suggestion: On-Premise Support for Cloud-Averse Organizations
SQL Server is more than just a database engine for Business Intelligence specialists. Reporting Services and Analysis Services are a fundamental part of SQL Server. Reporting Services received a major overhaul in SQL Server 2016. More improvements were made in SQL Server 2017, which includes on-premise storage for Power BI reports in an SSRS instance. This is a big deal for organizations that are cloud-averse due to a variety of reasons. SQL Server 2017 also supports Power Query data sources in SSAS Tabular Models to expand. This allows tabular models to store data from a wider range of data sources than it currently supports, including Azure Blob Storage and webpage data.

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