Principal Program Manager, Microsoft, USA
Sunil Agarwal is a Principal Program Manager in SQL Server Storage Engine Group since 2002. He had worked on the in-memory OLTP as part of SQL Server 2014. Sunil now owns the column store in-memory technology for DW workloads. Earlier, Sunil has driven many features in SQL Server such as AlwaysON, Data Compression, Snapshot Isolation, Vardecimal Storage Format, Enhancements to TempDB, Bulk Import/Export, and Supportability. Sunil has co-authored two books and many white papers on SQL Server and he contributes to popular SQL Server Storage Engine Blog.
|Session Title & Code||Abstract||Level||Track|
|ColumnStore Index: SQL Server 2014 and Beyond – (DEV-05A)||The updateable clustered columnstore in SQL Server 2014 offers a leading solution for your data warehouse workload with order of magnitude better data compression and query performance over traditional btree-based schemas. This session describes columnstore index internals with deep insight into data compression methodology for achieving high query performance including improvements in column store investments for SQL Server 2016||Advanced||DEV/DBA|
|In-Memory OLTP: SQL Server 2014 and Beyond – (DEV-06A)||SQL Server 2014 introduced in-memory technology for optimizing OLTP workloads. This talk provides an overview of the in-memory technology using common use cases. Additionally, we will take a deep dive into how in-memory tables and indexes are managed in memory and on storage. You will also learn how to provision and control memory usage, how durability and high availability is achieved and how these constructs and operations are integrated together with disk-based tables to provide you a seamless experience||Advanced||DEV/DBA|
|Operational Analytics in SQL Server – (BIA-10I)||SQL Server 16 enables customers can run analytic queries on in-memory and disk-based OLTP tables with minimal impact on business critical OLTP workloads, requiring no application changes. This session covers various configurations and best practices for achieving significant performance gains with Operational Analytics.||Intermediate||BIA|
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