Virtual Symposium Recordings

In this session you'll learn topics ranging from the newest technical features in SQL Server to its implementation in all the platforms it runs on. You'll learn not only specific technologies, but how to assemble them into a complete solution based on customer needs and requests. We'll explain the latest improvements in SQL Server, how to work with the Big Data Clusters feature, and then learn about the ways you can leverage SQL in Microsoft Azure - all with a focus on how to extrapolate what you have learned to create other solutions for your organization. You'll also get an introduction to "What to Use When" - explaining how to create your own solutions.

SQL Server databases are growing larger and larger by the day, often becoming multi-TB monsters. Restores of these databases have become more and more complicated. Not only do the "old ways" no longer work for timely restores, but even the "new ways" fall short. In this session, we will demonstrate how it is possible to perform a restore of a SQL Server database irrespective of its size in a matter of minutes. We will demonstrate the actual copy of data back to the server's disks while the database being restored is online and available for all operations. The world's leading brands use Actifio for rapid database cloning and recovery, so can you.

Are you thinking about running SQL Server in Kubernetes and don’t know where to start…are you wondering what you really need to know? If so, then this is the session for you! When deploying SQL Server In Kubernetes key considerations include data persistency, Pod configuration, resource management, and high availability/disaster recovery scenarios. In this session, we’ll look closely at each of these elements and learn how to run SQL Server in Kubernetes.

"The intelligent query processing (IQP) feature family includes features with broad impact that improve the performance of existing workloads with minimal implementation effort to adopt. In this session, Satya Ramesh walks us through couple IQP features in SQL Server 2019. 1. Adaptive Joins (Batch Mode) 2. Batch Mode on Rowstore This is part 2 of IQP session. Part 1 was delivered by Amit Bansal "

To steal a quote from Python that perfectly applies here, Azure SQL is “batteries included.” It’s got everything you need. Lots of things have changed from when SQL Server was released to the market decades ago, and even more from when Azure SQL was made available in the cloud. In this session we'll see what Azure SQL offers to the modern developer and how each of the following features can help in reducing application complexity and increasing development efficiency. Along with relational features, an amazing number of post-relational features like JSON and Graph support, columnar storage and in-memory lock-free structures are available to developers to leave to the database the burden to efficiently manipulate and crunch data, so that they can focus on design architectures, implementing the business logic in the most clean and elegant way possible. Having all such features all available for one scalable database is unique in the market. Come learn how to take advantage of everything Azure SQL can offer to a developer

SSG YouTube Channel https://www.youtube.com/user/SQLServerGeeks/ SSG [...]

SSG YouTube Channel https://www.youtube.com/user/SQLServerGeeks/ SSG [...]

SSG YouTube Channel https://www.youtube.com/user/SQLServerGeeks/ SSG [...]

Data is all we do. It’s all we’ve ever done, and we’re pretty darn good at it. So, we developed erwin Data Intelligence, comprised of erwin Data Catalog (erwin DC) and erwin Data Literacy (erwin DL) with built-in automation, to give organizations an accurate picture of their metadata landscapes. Such visibility reduces friction in data accessibility and utility, improves overall quality, underpins governance and accelerates digital transformation as more individuals become data literate and intelligent. With erwin Data Intelligence, you can automate the discovery and assessment of enterprise data assets. In fact, metadata management is key to wringing all the value possible from data assets because it answers a lot of important questions: • What data do we have? • Where did it come from? • Where is it now? • How has it changed since it was originally created or captured? • Who is authorized to use it and how? • Is it sensitive or are there any risks associated with it? When approached manually, metadata management is expensive, time-consuming, error-prone and can’t keep pace with a dynamic enterprise data management infrastructure. Learn how erwin data intelligence will help to move about manual to automation and document data assets in a centralized repository.