Fannie  Zemlak

Fannie Zemlak

1597608000

Microsoft Provides Sneak Peek at Azure SQL Data Warehouse

Fresh from last week’s Build developer conference in San Francisco, Microsoft executives appeared at the company’s first-ever Ignite conference in Chicago and provided more details about the company’s new Azure SQL Data Warehouse.

The company yesterday demoed the first “sneak peek” at the new “elastic data warehouse in the cloud,” and today exec Tiffany Wissner penned a blog post to highlight specific functionalities.

Wissner explained how Azure SQL Data Warehouse expands upon the company’s flagship relational database management system (RDBMS), SQL Server.

“Azure SQL Data Warehouse is a combination of enterprise-grade SQL Server augmented with the massively parallel processing architecture of the Analytics Platform System (APS), which allows the SQL Data Warehouse service to scale across very large datasets,” Wissner said. “It integrates with existing Azure data tools including Power BI for data visualization, Azure Machine Learning for advanced analytics, Azure Data Factory for data orchestration and movement as well as Azure HDInsight, our 100 percent Apache Hadoop service for Big Data processing.”

Working with Azure SQL Data Warehouse

[Click on image for larger view.]

Working with Azure SQL Data Warehouse (source: Microsoft)

Just over a year ago, Microsoft introduced the APS as a physical appliance wedding its SQL Server Parallel Data Warehouse (PDW) with HDInsight. APS is described by Microsoft as “the evolution of the PDW product that now supports the ability to query across the traditional relational data warehouse and data stored in a Hadoop region – either in the appliance or in a separate Hadoop cluster.”

Wissner touted the pervasiveness of SQL Server as a selling point of the new solution, as enterprises can leverage developer skills and knowledge acquired from its years of everyday use.

“The SQL Data Warehouse extends the T-SQL constructs you’re already familiar with to create indexes, partitions, functions and procedures which allows you to easily migrate to the cloud,” Wissner said. “With native integrations to Azure Data Factory, Azure Machine Learning and Power BI, customers are able to quickly ingest data, utilize learning algorithms, and visualize data born either in the cloud or on-premises.”

PolyBase in the cloud is another attractive feature, Wissner said. PolyBase was introduced with PDW in 2013 to integrate data stored in the Hadoop Distributed File System (HDFS) with SQL Server, one of many emerging SQL-on-Hadoop solutions.

“SQL Data Warehouse can query unstructured and semi-structured data stored in Azure Storage, Hortonworks Data Platform, or Cloudera using familiar T-SQL skills making it easy to combine data sets no matter where it is stored,” Wissner said. “Other vendors follow the traditional data warehouse model that requires data to be moved into the instance to be accessible.”

Although Wissner didn’t identify any of those “other vendors,” Microsoft took pains to position Azure SQL Data Warehouse as an improvement upon the Redshift cloud database offered by Amazon Web Services Inc. (AWS), which Microsoft is challenging for public cloud supremacy.

One of the advantages Microsoft sees in its product over Redshift is the ability to save costs by pausing cloud compute instances. This was mentioned at Build and echoed today by Wissner.

“Dynamic pause enables customers to optimize the utilization of the compute infrastructure by ramping down compute while persisting the data and eliminating the need to backup and restore,” Wissner said. “With other cloud vendors, customers are required to back up the data, delete the existing cluster, and, upon resume, generate a new cluster and restore data. This is both time consuming and complex for scenarios such as data marts or departmental data warehouses that need variable compute power.”

Again parroting the Build message, Wissner also discussed Azure SQL Data Warehouse’s ability to separate compute and storage services, scaling them independently up and down immediately as needed.

“With SQL Data Warehouse you are able to quickly move to the cloud without having to move all of your infrastructure along with it,” Wissner concluded. “With the Analytics Platform System, Microsoft Azure and Azure SQL Data Warehouse, you can have the data warehouse solution you need on-premises, in the cloud or a hybrid solution.”

Users interested in trying out the new offering, expected to hit general availability this summer, can sign up to be notified when that happens.

#coding #azure #microsoft #sneak peek

What is GEEK

Buddha Community

Microsoft Provides Sneak Peek at Azure SQL Data Warehouse
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language

Ruthie  Bugala

Ruthie Bugala

1620435660

How to set up Azure Data Sync between Azure SQL databases and on-premises SQL Server

In this article, you learn how to set up Azure Data Sync services. In addition, you will also learn how to create and set up a data sync group between Azure SQL database and on-premises SQL Server.

In this article, you will see:

  • Overview of Azure SQL Data Sync feature
  • Discuss key components
  • Comparison between Azure SQL Data sync with the other Azure Data option
  • Setup Azure SQL Data Sync
  • More…

Azure Data Sync

Azure Data Sync —a synchronization service set up on an Azure SQL Database. This service synchronizes the data across multiple SQL databases. You can set up bi-directional data synchronization where data ingest and egest process happens between the SQL databases—It can be between Azure SQL database and on-premises and/or within the cloud Azure SQL database. At this moment, the only limitation is that it will not support Azure SQL Managed Instance.

#azure #sql azure #azure sql #azure data sync #azure sql #sql server

Top Microsoft big data solutions Companies | Best Microsoft big data Developers

An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.

#microsoft big data solutions development companies #microsoft big data analytics and solution #microsoft big data consultants #microsoft big data developers #microsoft big data #microsoft big data solution providers

Creating and Cataloging SQL pools in Azure SQL Server

This article will walk you through creating a new SQL pool within an existing Azure SQL Server as well as catalog the same using the Azure Purview service.

Introduction

Data is generated by transactional systems and typically stored in relational data repositories. This data is generally used by live applications and for operational reporting. As this data volume grows, this data is often required by other analytical repositories and data warehouses where it can be used for referential purposes and adding more context to other data from across the organization. Transactional systems (also known as Online Transaction Processing (OLTP) systems) usually need a relational database engine, while analytical systems (also known as Online Analytical Processing (OLAP) systems) usually need analytical data processing engines. On Azure cloud, it is usually known that for OLTP requirements, SQL Server or Azure SQL Database can be employed, and for analytical data processing needs, Azure Synapse and other similar services can be employed. SQL Pools in Azure Synapse host the data on an SQL Server environment that can process the data in a massively parallel processing model, and the address of this environment is generally the name of the Azure Synapse workspace environment. At times, when one has already an Azure SQL Server in production or in use, the need is to have these SQL Pools on an existing Azure SQL Server instance, so data in these SQL pools can be processed per the requirements on an OLAP system as well as the data can be co-located with data generated by OLTP systems. This can be done by creating SQL Pools within the Azure SQL Server instance itself. In this article, we will learn to create a new SQL Pool within an existing Azure SQL Server followed by cataloging the same using the Azure Purview service.

Pre-requisite

As we intend to create a new SQL Pool in an existing Azure SQL Server instance, we need to have an instance of Azure SQL in place. Navigate to Azure Portal, search for Azure SQL and create a new instance of it. We can create an instance with the most basic configuration for demonstration purposes. Once the instance is created, we can navigate to the dashboard page of the instance and it would look as shown below.

As we are going to catalog the data in the dedicated SQL Pool hosted on Azure SQL instance, we also need to create an instance of Azure Purview. We would be using the Azure Purview studio from the dashboard of this instance, tonregister this SQL Pool as the source and catalog the instance.

#azure #sql azure #azure sql server #sql #sql #azure

Sid  Schuppe

Sid Schuppe

1618404240

Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses