Monat: April 2019

Want to evaluate your cloud analytics provider? Here are the three questions to ask.

We all want the truth. To properly assess your cloud analytics provider, ask them about the only three things that matter:

Independent benchmark results
Company-wide access to insights
Security and privacy

What are their results on independent, industry-standard benchmarks? 

Perhaps you’ve heard from other providers that benchmarks are irrelevant. If that’s what you’re hearing, maybe you should be asking yourself why? Independent, industry-standard benchmarks are important because they help you measure price and performance on both common and complex analytics workloads. They are essential indicators of value because as data volumes grow, it is vital to get the best performance you can at the lowest price possible.

In February, an independent study by GigaOm compared Azure SQL Data Warehouse, Amazon Redshift, and Google BigQuery using the highly recognized TPC-H benchmark. They found that Azure SQL Data Warehouse is up to 14x faster and costs 94 percent less than other cloud providers. And today, we are pleased to announce that in GigaOm’s second benchmark report, this time with the equally important TPC-DS benchmark, Azure SQL Data Warehouse is again the industry leader. Not Amazon Redshift. Not Google BigQuery. These results prove that Azure is the best place for all your analytics.

This is why customers like Columbia Sportswear choose Azure.

“Azure SQL Data Warehouse instantly gave us equal or better performance as our current system, which has been incrementally tuned over the last 6.5 years for our demanding performance requirements.”

Lara Minor, Sr. Enterprise Data Manager, Columbia Sportswear

 

Can they easily deliver powerful insights across your organization?

Insights from your analytics must be accessible to everyone in your organization. While other providers may say they can deliver this, the end result is often catered to specific workgroups versus being an enterprise-wide solution. Data can become quickly siloed in these situations, making it difficult to deliver insights across all users.

With Azure, employees can get their insights in seconds from all enterprise data. Data can seamlessly flow from your SQL Data Warehouse to Power BI. And without limitations on concurrency, Power BI can be used across teams to create the most beautiful visualizations that deliver powerful insights. This combination of powerful analytics with easy-to-use BI is quite unique. In fact, if you look at the Gartner 2019 Magic Quadrant for Analytics and Business Intelligence Platforms and the Gartner 2019 Magic Quadrant for Data Management Solutions for Analytics below, you’ll see that Microsoft is a Leader.

 

 

Our leadership position in BI, coupled with our undisputed performance in analytics means that customers can truly provide business-critical insights to all. As the TPC-DS benchmark demonstrates, Azure SQL Data Warehouse provides unmatched performance on complex analytics workloads that mimic the realities of your business. This means that Power BI users can effortlessly gain granular-level insights across all their data.

The TPC-DS industry benchmark I mentioned above is particularly useful for organizations that run intense analytics workloads because it uses demanding queries to test actual performance. For instance, one of the queries used in the TPC-DS benchmark report calculates the number of orders, time window for the orders, and filters by state on non-returned orders shipped from a single warehouse. This type of complex query, which spans across billions of rows and multiple tables, is a real-world example of how companies use a data warehouse for business insights. And with Power BI, users can perform intense queries like this by easily integrating with SQL Data Warehouse for fast, industry-leading performance.

How robust is their security?

Everyone is a target. When it comes to data, privacy and security are non-negotiable. No matter how cautious you are, there is always a threat lurking around the corner. Your analytics system contains the most valuable business data and must have both stringent security and privacy capabilities.

Azure has you covered. As illustrated by Donald Farmer, a well-respected thought leader in the analytics space, analytics in Azure has the most advanced security and privacy features in the market. From proactive threat detection to providing custom recommendations that enhance security, Azure SQL Data Warehouse uses machine learning and AI to secure your data. It also enables you to encrypt your data, both in flight and at rest. You can provide users with appropriate levels of access, from a single source, using row and column level security. This not only secures your data, but also helps you meet stringent privacy requirements.

“It was immediately clear to us that with Azure, particularly Azure Key Vault, we would be able to meet our own rigorous requirements for data protection and security.”

Guido Vetter, Head of Corporate Center of Excellence Advanced Analytics & Big Data, Daimler

Azure’s leading security and data privacy features not only make it the most trusted cloud in the market, but also complements its leadership in other areas, such as price-performance, making it simply unmatched.

Get started today

To learn more about Azure’s industry-leading price-performance and security, get started today!

 

 

Gartner Magic Quadrant for Analytics and Business Intelligence Platforms Cindi Howson, James Richardson, Rita Sallam, Austin Kronz, 11 February 2019.

Gartner Magic Quadrant for Data Management Solutions for Analytics, Adam Ronthal, Roxane Edjlali, Rick Greenwald, 21 January 2019.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Quelle: Azure

Smarter, faster, safer: Azure SQL Data Warehouse is simply unmatched

Today, we want to call attention to the exciting news that Azure SQL Data Warehouse has again outperformed other cloud providers in the most recent GigaOm benchmark report.

This is the result of relentless innovation and laser-focused execution on providing new features our customers need, all while reducing prices so customers get industry-leading performance at the best possible value. In just the past year, SQL Data Warehouse has released 130+ features focused on providing customers with enhanced speed, flexibility, and security. And today we are excited to announce three additional enhancements that continue to make SQL Data Warehouse the industry leader:

Unparalleled query performance
Intelligent workload management
Unmatched security and privacy

In this blog, we’ll take a closer look at the technical capabilities of these new features and, most importantly, how you can start using them today.

Unparalleled query performance

In our March 2019 release, a collection of newly available features improved workload performance by up to 22x compared to previous versions of Azure SQL Data Warehouse, which contributed to our leadership position in both the TPC-H and TPC-DS benchmark reports.

This didn’t just happen overnight. With decades of experience building industry-leading database systems, like SQL Server, Azure SQL Data Warehouse is built on top of the world’s largest cloud architectures.

Key innovations that have improved query performance include:

Query Optimizer enhancements
Instant Data Movement
Additional advanced analytic functions

Query Optimizer enhancements

Query Optimizer is one of the most critical components in any database. Making optimal choices on how to best execute a query can and does yield significant improvement. When executing complex analytical queries, the number of operations to be executed in a distributed environment matters. Every opportunity to eliminate redundant computation, such as repeated subqueries, has a direct impact to query performance. For instance, the following query is reduced from 13 down to 5 operations using the latest Query Optimizer enhancements.

Instant Data Movement

For a distributed database system, having the most efficient data movement mechanism is also a critical ingredient in achieving great performance. Instant Data Movement was introduced with the launch of the second generation of Azure SQL Data Warehouse. To improve instant data movement performance, broadcast and partition data movement operations were added. In addition, performance optimizations around how strings are processed during the data movement operations yielded improvements of up to 2x.

Advanced analytic functions

Having a rich set of analytic functions simplifies how you can write SQL across multiple dimensions that not only streamlines the query, but improves its performance. A set of such functions is GROUP BY ROLLUP, GROUPING(), GROUPING_ID(). See the example of a GROUP BY query from the online documentation below:

SELECT Country
,Region
,SUM(Sales) AS TotalSales
FROM Sales
GROUP BY ROLLUP(Country, Region)
ORDER BY Country
,Region

Intelligent workload management

The new workload importance feature in Azure SQL Data Warehouse enables prioritization over workloads that need to be executed on the data warehouse system. Workload importance provides administrators the ability to prioritize workloads based on business requirements (e.g., executive dashboard queries, ELT executions).

Workload classification

It all starts with workload classification. SQL Data Warehouse classifies a request based on a set of criteria, which administrators can define. In the absence of a matching classifier, the default classifier is chosen. SQL Data Warehouse supports classification at different levels including at the SQL query level, a database user, database role, Azure Active Directory login, or Azure Active Directory group, and maps the request to a system defined workload group classification.

Workload importance

Each workload classification can be assigned one of five levels of importance: low, below_normal, normal, above_normal, and high. Access to resources during compilation, lock acquisition, and execution are prioritized based on the associated importance of a request.

The diagram below illustrates the workload classification and importance function:

Classifying requests with importance

Classifying requests is done with the new CREATE WORKLOAD CLASSIFIER syntax. Below is an example that maps the login for the ExecutiveReports role to ABOVE_NORMAL importance and the AdhocUsers role to BELOW_NORMAL importance. With this configuration, members of the ExecutiveReports role have their queries complete sooner because they get access to resources before members of the AdhocUsers role.

CREATE WORKLOAD CLASSIFIER ExecReportsClassifier
   WITH (WORKLOAD_GROUP = 'mediumrc'
        ,MEMBERNAME     = 'ExecutiveReports'
        ,IMPORTANCE     =  above_normal);
 
CREATE WORKLOAD CLASSIFIER AdhocClassifier
    WITH (WORKLOAD_GROUP = 'smallrc'
         ,MEMBERNAME     = 'AdhocUsers'
         ,IMPORTANCE     =  below_normal);

For more information on workload importance, refer to the classification importance and CREATE WORKLOAD CLASSIFIER documents.

Unmatched security and privacy

When using a data warehouse, customers often have questions regarding security and privacy. As illustrated by Donald Farmer, a well-respected thought leader in the analytics space, Azure SQL Data Warehouse has the most advanced security and privacy features in the market. This wasn’t achieved by chance. In fact, SQL Server, the core technology of SQL Data Warehouse, has been the least vulnerable database over the last eight years in the NIST vulnerabilities database.

One of our newest security and privacy features in SQL Data Warehouse is Data Discovery and Classification. This feature enables automated discovery of columns potentially containing sensitive data, recommends metadata tags to associate with the columns, and can persistently attach those tags to your tables.

These tags will appear in the Audit log for queries against sensitive data, in addition to being included alongside the query results for clients which support this feature.

The Azure SQL Database Data Discovery & Classification article walks you through enabling the feature via the Azure portal. While the article was written for Azure SQL Database, it is now equally applicable to SQL Data Warehouse.

Next steps

Visit the Azure SQL Data Warehouse page to learn more.
Get started with a free Azure SQL Data Warehouse account.
Discover the seven essential security and privacy principles for your cloud data warehouse.

Azure is the best place for data analytics

Azure continues to be the best cloud for analytics. Learn more why analytics in Azure is simply unmatched.
Quelle: Azure