This session will take a look at better Unicode support, query processing improvements for row store tables, secure enclaves, and other neat things you'll find useful as a modern database administrator or developer.
Azure offers a comprehensive set of big-data solutions that help you gather, store, process, analyse and visualise data of any variety, volume or velocity, so you can discover new opportunities and take quick action. In this overview session, we’ll look at the various components within Azure that make up the Modern Data Warehouse, enable Real-Time Analytics, and support Advanced Analytics scenarios. You should leave with a high level understanding of the capabilities and limitations of each of the products within the Azure Analytics portfolio.
We will review this new feature of ADFv2, do deep dive to understand the mentioned techniques, compare them to SSIS and/or T-SQL and learn how modelled data flow runs Scala behind the scenes.
Azure Databricks support both Classic and Deep Learning ML Algorithms to analyse Large DataSets at scale. The Integrated Notebook experience gives the Data Scientists and Data Engineers to do exploratory Data Analysis, also feels like native to Jupyter notebook users. In this session we will extract intelligence from Higgs Dataset (Particle Physics) by running Classic and Deep Learning models using Azure Databricks. We will also peek into AMl service's integration with Azure Databricks for managing the end-to-end machine learning lifecycle.
Power BI Premium enables you to build comprehensive, enterprise-scale analytic solutions that deliver actionable insights through Microsoft Power BI. The session will focus on performance, scalability, and application lifecycle management (ALM).
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data. Azure Databricks is a fast, easy and collaborative Apache Spark-based analytics service that can be used for big data analytics and artificial intelligence (AI) solutions. This session will cover the architecture patterns for using the two in synergy.
Database Partitioning has been around for a while but is always being updated and enhanced. In today big data volume world, we to think how we are going to support these large tables.
Come to this session to learn how to enable collaboration and solution design for business users and IT specialists to build solutions that enable an origanization to harness the power of their big data.  This session will enable you to collaborate across business and IT, and how we can extend intelligence beyond Power BI into Azure Data Services. Once Power BI has landed in an organization, attaching and extending into Azure can be achieved using common use cases and modernization plays.
In this session Buck Woody explains how Microsoft has implemented the SQL Server 2019 relational database engine in a big data cluster leverages an elastically scalable storage layer that integrates SQL Server and HDFS to scale to petabytes of data storage. You’ll see the three ways you can interact with massive amounts of data: Data Virtualization, Data Marts, and working with a complete Kubernetes Cluster in SQL Server. You’ll also learn common use case scenarios that leverage big data and the SQL server 2019 Big Data Cluster on-premises, in the cloud, and in a hybrid architecture.
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