Author Archives: Amit Banerjee

Data Platform Summit 2017

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PrintI will be speaking at the Data Platform Summit happening at Bangalore from Aug 17-19, 2017. I will be delivering sessions on achieving a million predictions/sec using SQL Server and introduce our latest entrants into the Azure Database family: MySQL and PostgreSQL.

Session: CLOUD/IoT/NoSQL_C13 – Running Applications On Azure Using Postgres/MySQL
Date: 17th Aug, 2017
Time: 11:30am – 12 noon
Session Type: Chalk Talk
Room: Kilimanjaro
Abstract: A chalk talk on Azure’s managed database offering for PostgreSQL and MySQL and how we make it easy to run your existing apps using PostgreSQL and MySQL on Azure.

Session: BIA_B02 – Advanced – Building 1 Million Predictions Per Second Using SQL-R
Date: 17th Aug, 2016
Time: 1:45PM – 3PM
Session Type: Breakout Session
Room: Hydra
Abstract: Using the power of OLTP and data transformation in SQL 2016 and advanced analytics in Microsoft R Server, various industries that really push the boundary of processing higher number of transaction per second (tps) for different use cases. In this talk, we will walk through the use case of predicting loan charge off (loan default) rate, architecture configuration that enable this use case, and rich visual dashboard that allow customer to do what-if analysis. Attend this session to find out how SQL + R allows you to build an “intelligent datawarehouse”.

In this session, I will show you the actual demo where we hit 1 million transactions/sec.

Session: CLOUD/IoT/NoSQL_B40 – Basic – Azure Database for MySQL and PostgreSQL
Date: 18th Aug, 2016
Time: 11:45AM – 1PM
Session Type: Breakout Session
Room: Sphinx
Abstract: Azure Database for MySQL and Azure Database for PostgreSQL are managed database services built for developers using the community edition of MySQL PostgreSQL. Learn how you can leverage these new choices of managed database services to build scalable, secure and intelligent apps. Using insights from current customer scenarios and through live demos, we walk through the service management capabilities, best practices to move your databases to the service, and also focus on how the Microsoft Azure ecosystem of app and data services is unlocking the potential of MySQL and PostgreSQL in the Azure cloud platform.

There is a surprise element in this session as well! And if I let the cat out of the bag now, it wouldn’t remain a surprise would it? Come to the session to find out what it is.

We also have other members from the SQL Server Tiger, SQL CAT, Data Migration and our field teams delivering sessions about various different data related topics. My colleague, Ajay Jagannathan, published a post on the upcoming Tiger sessions at the conference. There is a great list of accomplished speakers that will be delivering sessions at the conference. Apart from a great lineup, it always feels great to connect with customers and the community in general to learn about how they are using the products that we are building and gather feedback from the horse’s mouth!

Follow  SQLServerGeeks and the #DPS2017 hashtag on Twitter for new and exciting updates about the conference. We hope to meet you at the conference.

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SQL Saturday 613: Building 1 million predictions per second with R-services and SQL Server 2016

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Last Saturday, I presented a session on how to use R-Services with SQL Server to build an analytical workflow for banking solutions. I talked about how our customer, Jack Henry & Associates, an S&P 400 company that supports more than 11,300 financial institutions with core processing services, is leveraging the power of SQL Server and R to make drive intelligent insights into their data warehousing software. Below you will find a link on how you can setup the complete solution that you can deploy on our Data Science Virtual Machine on Azure.

Our Corporate Vice President, Joseph Sirosh, had demonstrated this solution along with Jack Henry & Associates at Ignite. In this session, I talked about the nuts and bolts on how to build a scalable predictive engine with SQL Server and using the enhancements shipped in SQL Server 2016. After this session, you will be able to build your very own scalable predictive engine on SQL Server 2016!

As always, it’s always great to meet my friends and the community at SQL Saturday events!

The slide deck used for my presentation can be found on SlideShare. The PowerBI dashboard and the demo scripts can be downloaded from the tigertoolbox repo on GitHub.

Book on Azure and SQL Server

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My last contribution to a book was in 2012. With the advent of the cloud and my continuing work with SQL Server, I jumped at the opportunity when my friends and colleagues, Pranab Mazumdar [t] and Sourabh Agarwal [t], talked to me about contributing to a book on running SQL Server on Azure.

The book “Pro SQL Server on Microsoft Azure” attempts to teach the basics of Microsoft Azure and see how SQL Server on Azure VMs (Infrastructure-as-a-Service) and Azure SQL Databases (Platform-as-a-Service) work. This book will show you how to deploy, operate, and maintain your data using any one or more combinations of these offerings along with your on-premise environments. You will also find some architecture details which are very important for an end user to know in order to run operations using Azure.

The book is available on Apress and Amazon.

We would love to hear any feedback about the book. It could be good, bad or ugly. You will find the resources available for download on the site.

Upgrading a Replication Topology to SQL Server 2016

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If you are looking to upgrade your SQL Server replication topology, then you can look at the post that I published on the Tiger blog.

Introducing VDC_Complete for Backup and Restore applications using SQLVDI

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I published a blog post on the Tiger blog on a recent change that was introduced for SQLVID. You can also use the SQL Server Backup Simulator which is available on our tigertoolbox GitHub repository for checking backup/restore behavior using SQLVDI APIs. The updated SQLVDI header files required to use VDC_Complete is available on the Microsoft SQL Server Samples GitHub repository.