This all-day workshop on SQL Server will provide coverage of the full range of the platform's capabilities and optimal usage of them, from core features like security and T-SQL language improvements; to new innovations like running SQL Server in containers and stretching its storage to Azure; to the latest in operational analytics, machine learning (ML) and Big Data. You'll learn about critical features that have been in the product for some time, as well as many of the latest features, delivered in SQL Server 2019. That's the tl;dr. For details, read on!
We'll kick things off with the most important features added to the relational engine, including broad multi-platform support across Windows, Linux, and Docker. Then you'll explore all the latest T-SQL enhancements, including more convenient DDL statements, new string split, join, and aggregate functions, partition truncation, and other language improvements.
Next, we'll examine the latest security features, including the new strict CLR security model, dynamic data masking (DDM), row level security (RLS), and Always Encrypted. You'll also get a look at the new "secure enclaves" feature in SQL Server 2019 that enables rich query and range operations on always encrypted data. We'll also have coverage of Stretch DB, which lets you leverage Azure to archive old data without taking it offline, and we'll explore temporal tables – versioned tables that enable instant point-in-time queries over your data.
But data platforms aren't just about operational databases anymore, and neither is SQL Server. So we'll next move on to its analytics capabilities, starting with columnstore indexes: what they are; how to create them; how they benefit massive data warehouse-style queries; and tips for monitoring how they're used in query plans.
From there we'll look at PolyBase, including its connectivity to data lakes on Hadoop clusters and cloud storage as well new connectivity to databases like Oracle, Teradata, MongoDB and even additional SQL Server instances. Next, we'll look at SQL Server's Big Data clusters and their incorporation of technologies like Apache Spark, the Hadoop Distributed File System (HDFS) and data stored in the Apache Parquet columnar format.
We'll start to wind down our analytics tour with a look at SQL Server's machine learning capabilities, including in-database features made possible by native hosting of the Python and R programming languages. And as the last stop on our analytics tour, we'll take in how to use Azure Data Studio to query SQL Server and the platforms it integrates with, as well as leverage SQL Server's data science features.
Our workshop concludes with a look at the latest "beyond relational" capabilities added to the relational database engine. Learn how to exploit the integrated JSON support – whether you are projecting or consuming JSON content, or you are implementing a hybrid relational/JSON solution. Then we'll wrap up with the latest Graph DB support, where (ironically) this capability delivers a superior relationship-focused data model compared to traditional relational tables.
Attend this workshop, designed especially for developers, and get up-to-speed on both the latest relevant SQL Server features and the product's classic critical capabilities!
You will learn:
- How to accelerate your database development process by running cross-platform SQL Server inside Docker containers
- The latest T-SQL language enhancements, plus DDM, RLS, Always Encrypted, Stretch DB, temporal tables, JSON, and Graph DB
- How to use SQL Server for data warehouse, big data and machine learning workloads
- The basics of new SQL Server-integrated open source technologies, including Spark, HDFS, Python, R and Parquet