Azure SQL Data Warehouse was launched by Microsoft when they decided to make use of the cloud. The company turned its Azure services into enterprise-grade cloud-based solutions that integrate cutting-edge features like data management and analytics at its core.
One of the services that Microsoft is pushing with a lot of energy, is the Azure SQL Data Warehouse. It is a scalable database management service, blended with elastic data warehouse features.
Initially, Microsoft released the Azure SQL Data Warehouse for a short preview in July 2015, subsequently following it up with a public preview in September 2015. They have released several updates on the service since then.
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The Azure SQL Data Warehouse is one of Microsoft's latest offerings. It is a scalable, fully-managed cloud service with enterprise-class features. Microsoft SQL Data Warehouse is a distributed database management system compatible with a variety of existing Microsoft products and SQL Server tools, as well as with top Azure features like Data Factory and Machine Learning. It is offered as Platform-as-a-Service(PaaS) and is built on MPP (Massively Parallel Processing) architecture.
The Azure SQL Data Warehouse, as revealed by Microsoft, can support a massive amount of data, which can range in petabytes. This big data can be scaled easily by users, who are looking to manage costs and control operations in an efficient manner.
In addition, Microsoft's cloud-based SQL database management model allows users the convenience of a 'pay-as-you-go' pricing structure, which enables cost-effective options like paying for services only when it is required.
The Azure SQL Data Warehouse serves as a fundamental component in retrieving, processing, storing, and analyzing big sets of data.
This enables the deployment of machine learning and analytics, which leverage business intelligence - a 'must-have' feature in new-generation business environments.
The enterprise-ready database service, based in the cloud, provides global businesses with an end-to-end solution that incorporates a control node, an increased number of compute nodes, and highly-reliable storage, which helps enterprises manage the exponential influx of Big Data dominating today's computing ecosystem.
The integrated control node of Azure SQL Data Warehouse accomplishes data movement processes and compute operations that are spread across the entire computing network.
Right at the centre of these operations, is Azure SQL Data Warehouse's Massively Parallel Processing (MPP) engine, which splits convoluted queries into coordinates and parallel processes that enable processing across several compute nodes.
The Azure compute nodes, driven by the SQL Database, perform the heavy task, especially when big data is loaded into the warehouse.
When a user makes a data query, the MPP divides the query and distributes it across numerous compute nodes, which instantly start working with its respective chunk, processing the assigned data and adding it to the storage.
After the nodes complete the processing of a query, they deliver the results to Azure SQL's control node that consequently aggregates the processed data.
The Azure SQL's compute nodes and the control node are also dependent on the Microsoft Data Movement Services (DMS) as well, which facilitates seamless data movement and communication between the compute nodes and the control node, making the implementation of aggregations and joins possible across various nodes.
Microsoft's Azure SQL Data Warehouse deploys a high-performance, enterprise-grade storage, known as the Azure Blob Storage. The compute nodes read and write directly from this Storage.
Enterprise data is safe and secure with Azure Blob, as the state-of-the-art storage is not impacted by abrupt pausing of computing services, restoring or backing up of data to Azure SQL's warehouse, or the scaling of data warehouse and storage services.
As per Microsoft, the Azure SQL Data Warehouse integrates high-class techniques and algorithms that support the Data Movement Services in transferring data securely and efficiently.
In addition, the top-of-the-line database service also utilizes a high-level query optimizer and complex statistics, which combinedly generate query programs and optimize the distribution of data.
1. Ease of Scalability: The best feature of the Azure SQL Data Warehouse is its elastic nature. It enables scalability with possibilities to manage the compute and storage resources separately. This creates a win-win platform, where users pay for only what they need and when they need it.
The computing side of Azure SQL Data Warehouse is founded on the principle of Data Warehouse Unit (DWU), which keeps track of computational resources like storage I/O, memory, and CPUs used across participating compute nodes.
2. Flexibility: Another key advantage of Microsoft Azure SQL Data Warehouse is its 'pause' feature, which allows users to pause the computing services when they don't require it.
The leading cloud database service also provides users with the flexibility to cancel any running queries, or any assigned DWUs to the data warehouse, which enables the curtailment of unnecessary fees when services are not being used. User data, however, remains safe and secure with Azure Blob Storage.
3. Seamless Compatibility: Azure SQL Data Warehouse seamlessly integrates with Azure Platform Services and other analytics solutions. It is compatible with Power BI, Azure Machine Learning, HDInsight, Azure Data Factory, and also with other third party products. It easily integrates with development tools making it useful for creating end-to-end analytics solutions.
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4. Security: Azure SQL Data Warehouse has a connection security feature. It allows you to restrict access using firewall rules to restrict to certain IP or IP range. Integrating with Azure Active Directory (AAD) authentication allows connecting to Azure SQL Data Warehouse using identities in Azure AD. The multi-layer protection offers encryption at rest, in motion, or in use to ensure your data is not misused. Additional built-in tools help in auditing and monitoring data for maintaining regulatory compliance and identifying suspected security violations.
Microsoft's Azure SQL Data Warehouse, when combined with other Microsoft tools and services, offers an unprecedented performance which becomes a key differentiator when comparing it with other similar services in the market.
To design and maintain an Azure SQL Data Warehouse you must use the following best practices:
Azure Data Warehouse and Azure Data Lake are both used for storing big data but they serve different purposes. Depending upon the organization’s storage requirement one might be more beneficial than the other but they cannot be used interchangeably.
The main differences are:
Azure Data Warehouse vs Azure Data Lake
Stores processed and refined data which is purposeful.
Stores raw, unprocessed data which is ideal for machine learning
Data is stored for specific future use.
Data is less organized and is not stored for a specified purpose.
Data is stored in a secure structured format and cannot be manipulated easily.
Data is stored in a flexible unstructured format making it easy to change.
Using the data for analytical purposes is easy for employees and management.
Using the data requires special tools and knowledge. Data scientists are required to make it available for business use.
Azure SQL Data Warehouse is a powerful data warehouse service but it has the following limitations:
Azure SQL Data Warehouse, is the ideal solution for enterprises that require a distributed processing system that integrates with their existing resources. Unlike the on-premises equivalent (APS), Azure SQL DW is easily and quickly scalable and highly accessible for a workload using the familiar T-SQL language.
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