New England SQL Server User Group

Many companies are migrating their data to the cloud. Files are typically classified as containing unstructured, semi structured, and structured data. A data warehouse is best suited to working with structured data while a data lake can store all three. What if

you can use one product to work with all

three types of data?

Azure Databricks SQL Warehouse is an

interface that makes the spark cluster look

like a SQL database. In general, the syntax

is compliant with the core ANSI SQL

language. However, additional features are

available for working with remote storage,

loading files into delta tables, and managing

the hive catalog. Last but not least, SQL

queries can be visualized and combined

into dashboards.

At the end of this talk, you will have a good

understanding of the Azure Databricks SQL

Warehouse.

Have you ever needed to learn a new database design and don’t know where to begin? Or are trying to find out why a query doesn’t perform well? Or need to provide security information to auditors or your security team? SQL Server has a number of metadata facilities available to help you with these tasks, and more. Functions, dynamic management views, and system stored procedures can illuminate details from the smallest column up through an entire SQL Server instance and everything in-between. We will demonstrate metadata techniques to help you: Document your database schema objects such as procedures, functions, tables, columns and indexes Investigate performance and look for bottlenecks Discover metadata to administer your databases backups, index maintenance, and security Apply your own metadata using extended properties

Abstract to follow