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PASS Summit 2014 Roundup - Microsoft Unveiled Major Enhancement to Azure SQL Database


Nov 10, 2014

Introduction

PASS Summit 2014 was an incredible community event. The PASS summit which brings together a large number of professionals from the SQL Server background was held in Seattle, WA from 4-7th Nov 2014.

PASS Summit opened with keynote addresses from Microsoft’T.K. "Ranga" Rengarajan, James Phillips, and Joseph Sirosh. There were 200+sessions and 17 full day pre-conference seminars it was attended by 5,000+ SQL Server and BI Professionals from over 50 countries and 2,000 companies to take your SQL Server skills to the next level.

Microsoft announced that it is opening up Azure Machine Learning to free access in an attempt to make advanced analytics more accessible to more people. This is very good news for DBAs and developers because Azure machine learning is now available to them at no cost.

As expected much of the focus this time was on different Azure offerings. Some of the key enhancements included are:-

  • Using Power Query now connect to SQL Server Analysis Services Cube. For more information, see Power Query November 2014 update - Connect to Analysis Services, Salesforce and more!
  • Azure DocumentDB - Microsoft Azure DocumentDB is a NoSQL document database service designed for modern mobile and web applications. DocumentDB delivers consistently fast reads and writes, schema flexibility and the ability to easily scale a database up and down on demand. DocumentDB enables complex ad hoc queries using the SQL dialect, supports well defined consistency levels, and offers JavaScript language integrated, multi-document transaction processing using the familiar programming model of stored procedures, triggers and UDFs. For more information, see Introduction to Microsoft Azure DocumentDB.
  • Azure Data Factory Service – The Azure Data Factory service is a fully managed service for composing data storage, processing, and movement services into streamlined, scalable, and reliable data production pipelines. Developers can use Data Factory to transform semi-structured, unstructured and structured data from on-premises and cloud sources into trusted information. For more information, see Introduction to Azure Data Factory Service.


  • Azure Data Factory Service An Overview

    Image Source: Azure Documentation

  •  Analytics Platform System brings Microsoft’s massively parallel processing (MPP) data warehouse technology–the SQL Server Parallel Data Warehouse (PDW)–together with HDInsight, Microsoft’s 100% Apache Hadoop distribution, and delivers it as a turnkey appliance. To integrate data from SQL Server PDW with data from Hadoop, APS offers the PolyBase data querying technology. For more information, see Breakthrough performance for big data analytics at an industry leading price.
  • Azure SQL Database – Later this year Microsoft will preview a newer version of Azure SQL Database which will that represents will make it easier to extend and migrate applications to the cloud, include support for larger databases with online indexing and parallel queries, improved T-SQL support with common language runtime and XML index, and monitoring and troubleshooting with extended events. Moreover, the preview will also unlock in-memory columnstore indexes, which will deliver greater performance for data marts.
  • Microsoft Azure Machine Learning is a fully managed cloud service for building predictive analytics solutions, and helps overcome the challenges most businesses face in deploying and using machine learning. It is easier now for anyone to try Azure Machine Learning, as the service is now available to test free of charge without a subscription or credit card. DBAs, Developers, Business Intelligence Professionals can now experiment with Azure Machine Learning at no cost. What are you waiting for Signup Now!
  • Stretch Table will be an interesting feature. This is nothing but the ability to take a table in SQL Server and stretch it into Azure SQL Database. In this scenario the table will be remain locally and in cloud i.e. current data will be stored locally and the historic used data will be stored in Azure SQL Database. In this scenario the TSQL queries which need to access the current data will fetch it from local tables and the rarely used data will be fetched from cloud.

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