Data have become the strategic asset used to transform businesses to uncover new insights. Traditionally, data from transactional systems such as ERP, CRM, and LOB applications are cleansed – extracted, transformed, and loaded (ETL) – into the data warehouse. However, trends that include increasing data volumes from social apps, real-time data from connect devices (IoT), new sources and types of data, and the need to do advanced analytics is putting the traditional data warehouse model under pressure.
On the January 24 call for the Data Platform and Analytics Partner Community, we’ll talk about the data warehouse opportunity for partners, and take questions from attendees.
A traditional data warehouse is built by gathering data sources from transactional systems like ERP, CRM, and LOB. Today, social, mobile, and connected devices generate data in such large volumes and format that has driven the growth of big data storage technologies such as Hadoop. And end users are demanding real-time and predictive analytics to make use of that data, but their demands can’t be met using traditional data warehouse tools. Many customers that want to drive real insights based on their data use technologies like R.
Microsoft SQL Server 2016 provides the much-needed support for storing large volumes of data of all shapes and formats in a flexible deployment model for both cloud and on-premises.
New capabilities in SQL Server 2016 and Azure data warehouse services
In-database analytics at massive scale
R has emerged as the most popular software for mining data, uncovering new insights and making predictions. R Services (In-Database) is a feature of SQL Server 2016 and SQL Server vNext that supports enterprise-scale data science. It provides in-database R predictive analytics capabilities to gain instant access to standard statistical and data manipulation functions, graphics, and cutting-edge machine learning algorithms.
Integrated relational and non-relational Azure Data Lake Store
Azure Data Lake Store provides a single repository to store integrated (relational and non-relational) data of any size type and speed without forcing changes to your application as the data scales. Azure Data Lake Store includes the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed.
Azure HDInsight provides a cloud based Hadoop and Spark cluster solution that provides an enterprise-ready managed Hadoop service in the cloud for the customers with enterprise-grade security, reliability with ease of administration, and management. Microsoft introduced PolyBase to add the ability to query relational and non-relational data in Hadoop with a single, T-SQL-based query model.
Microsoft delivers a comprehensive offering to deploy data warehousing and big data that can span on-premises and cloud. Microsoft offers hybrid deployment for enterprises that want both on-premises and cloud, providing the benefits of control and flexibility of on-premises and elasticity and redundancy of the cloud. It also opens a wealth of cloud computing and advanced analytics capabilities that are accessible through Azure.
Advanced analytics and machine learning
Today, most advanced analytics applications use a primitive approach of moving data from data warehouses into the application tier to derive intelligence resulting in high latency and low scalability. Microsoft has revolutionized these integrated advanced analytics into the modern data warehouse by providing comprehensive set of advanced analytics and machine learning tools in Cortana Intelligence Suite, and R Server, which can be deployed on-premises with SQL Server 2016 and in the cloud with HDInsight.
Solutions for the modern data warehouse and corporate BI are a rapidly growing area of opportunity for IT companies, and these are often large deals. If your partner business offers services for data warehousing and business intelligence, look for opportunities to help your customers upgrade and migrate to Microsoft Azure and Microsoft SQL Server 2016.
- Modernize data warehousing
- Migrate customers from SAS to Microsoft R
- Modernize analytics
Training and resources
- Partner resources for SQL Server 2016
- Technical training: Introduction to Azure Data Lake
- Technical training: Design and Implement Big Data and Advanced Analytics Solutions
- Technical training: Big Data Analytics with Azure HDInsight
- Microsoft R Server 2016 Overview on Channel 9
Data Platform, Intelligence, and Analytics Partner Community
- Sign up for the January 24 partner call
- Community call schedule
- Yammer group
- Blog series
- Training and enablement