This article was published as a part of the Data Science Blogathon.
Introduction
Data is compelling and critical for businesses to generate actionable and valuable insights only when used correctly. In addition, it is also essential to analyze and organize it well. However, only a few business data are analyzed and stored appropriately.
.png)
Cloud storage and computing enterprise data performance require efficient data storage solutions. It has become a one-time investment in implementing a data warehouse system that can be used instantly rather than as a significant capital expenditure. This way, your business can access structured data sources where you can collect, discover and query statistics.
Meanwhile, Microsoft brought a cloud data warehouse solution known as Azure’s SQL Data Warehouse. It is one of the effective and reliable products and platforms in the data platform environment.
If you are new to cloud data warehousing in Microsoft Azure, continue reading to understand better.
Cloud Data Warehousing In Microsoft Azure
SQL Data Warehouse MS Azure is a cloud platform as a service. It is also called massively parallel processing (MPP) relational database technology. It is a critical component of a multi-platform Modern Data Warehouse architecture.
Because Azure SQL Data Warehouse is an MPP system with a shared-type architecture, you can use it for large-scale analytic workloads and take advantage of parallelism. This cloud data warehouse solution enables the separation of storage and computation. As a result, you can achieve scalability and independent billing.
Source: https://docs.microsoft.com/en-us/azure/architecture
In addition, Azure SQL Data Warehouse is an integral part of the MS SQL Server product lines, including SQL Server and Azure SQL Database. Experience and knowledge are therefore effectively transferred to Azure SQL Data Warehouse.
However, there is one exception. The MPP architecture is unique from the SMP architecture of Azure SQL Server and SQL Database. It also requires special design techniques to exploit the full capacity of the MPP architecture.
What can do with Azure Cloud Data Warehousing?
Azure SQL Data Warehouse is known for its elastic cloud service& high scalability. It offers compatibility with other Azure offerings such as Machine Learning and Data Factory and various MS products and SQL Server tools.
This data warehouse solution can process massive amounts of data using parallel processing. As a distributed database management system, the SQL data warehouse system has overcome most of the shortcomings of traditional data storage systems.
Because Azure SQL Data Warehouse can quickly spread data between different processing and storage units, it becomes more suitable for batch loading, bulk data provisioning, and transformation. This built-in Azure feature offers the same consistency and scalability as other Azure services.
Source: https://docs.microsoft.com/en-us/azure/architecture
How Azure SQL Data Warehousing overcomes the disadvantages
Data warehouses traditionally consist of symmetric multiprocessor (SMP) machines and two or more identical processors. They offer full access to I/O devices because they are connected to shared memory.
A single OS controls them equally. However, the need for scalability has skyrocketed due to growing business demands. This makes SQL Data Warehouse Azure even more important for any organization.
Azure cloud data warehouses satisfy all requirements through a shared architecture. In addition, storing data in multiple locations makes it possible to process large volumes of data in parallel.
Notable features of cloud data warehouse in Microsoft Azure
Are you still using an open data warehouse? Then maybe now you should switch to MS Azure SQL Data Warehouse. This solution from Microsoft allows you to create a data warehouse in the cloud.
Here are the notable features of the Azure cloud data warehouse solution:
Excellent combination of Azure cloud scaling capabilities and SQL Server relational database;
Continuously compute separately from storage;
Includes the use of T-SQL and tools;
Scales up and down and pauses and resumes calculations;
What are the common rationales for implementing Azure SQL Data Warehouse?
Organizations new to cloud data warehousing may consider implementing MS Azure SQL Data Warehouse. To help you decide, below are common implementation rationales:
Consolidation and session of multiple disparate data sources
When data is integrated from different sources, it becomes more valuable. For example, a customer’s 360-degree view can reconcile customer master data, support requests, open claims, and sales for easy analysis.
Historical analysis
The data warehouse is also reliable when analyzing historical data using predictive analytics consulting and reporting techniques, including slowly changing dimensions and periodic snapshots. For example, a department was created this quarter, or your customer sales representative is now moving to a new division. So the report will be flexible in terms of “as is” or “as it was,” offering critical value and often unavailable from traditional source systems.
Azure Analytics and integration in the Microsoft environment
- Reduce silos
Is a business-driven analytics solution critical to running your business? Then it’s a sign that you need a solution that a centralized system provides. This way, your business will be supported more efficiently and integrated with other important data with a larger user base.
With Azure cloud data warehouse, you can see the best results of your business efforts while continuously gaining value and maturity. You can also reduce silos.
- User-friendly data structure
Structuring the data to be a user-friendly dimensional model is critical because it helps the capabilities of the core user base. Additional techniques such as useful measures (such as YTD, QT, and MTD) and familiar names for columns, tables, and derived attributes contribute to ease of use. Data analysts are encouraged to use a data warehouse to ensure consistent results and save time, money, and effort.
- Existing investments
Once you notice that your current data warehouse can no longer provide value for specific use cases, it is not economically feasible to migrate all your data to another architecture or shut down. Instead, take advantage of a cross-platform architecture where data is one critical component.
What are the different components of Azure Data Warehousing?
Here are the various components of MS Azure SQL Data Warehouse to help you get to know it better:
- Control node
Applications and connections communicate with the system’s front-end control node. The master node is responsible for coordinating everything needed to run parallel queries, from data movement to computation. This is possible by transforming each query to run in parallel on different compute nodes.
- Compute node
After the computing nodes receive the queries, they are processed and stored. Attention, parallel processing of queries takes place with different computing nodes. Then the results are returned to the control nodes after the process is complete. The results are then collected and returned to the final result.
- Storage space
Large amounts of unstructured data are quickly stored with Azure Blob storage. Compute nodes can read and write directly from this storage, so the data interact. Azure data storage is fault-tolerant and scales transparently. In addition, the storage restores data and provides powerful backups.
- Data Movement Service (DMS)
Windows offers a DMS that runs alongside SQL databases on every type of node. It helps move data between nodes and then forms the core of the entire process to ensure that it fulfills its critical role in moving data to complete parallel processing.
What are the key benefits of cloud data warehousing in Microsoft Azure?
Below are the main benefits you should know are Azure data warehouses:
- Flexibility
Azure SQL Data Warehouse provides excellent elasticity because the storage and compute components are separated. You can even scale the calculation independently. Even when the query runs, elimination and addition of resources are allowed. - V12 portability
Looking to upgrade from SQL Server to Azure SQL or vice versa? Microsoft Aure Data Warehouse contains all the tools and services per your requirements. - Focused on safety
One of the best things about Azure SQL is that it provides various security components, including auditing, encryption, data masking, row-level security, etc. Be aware that cloud data is not exempt from cyber threats. - Polybase
With Azure Data warehousing, you can query non-relational resources through Polybase. - High scalability
According to consulting experts, Azure offers high scalability. In the case of Azure Data Warehouse, it can quickly scale up and down based on demand.
Conclusion
Azure SQL Data Warehouse is an MPP system with a shared-type architecture. You can use it for large-scale analytic workloads and take advantage of parallelism. This cloud data warehouse solution enables the separation of storage and computation. As a result, you can achieve scalability and independent billing.
- SQL Data Warehouse MS Azure is a cloud platform as a service. It is also called massively parallel processing (MPP) relational database technology. It is a critical component of a multi-platform Modern Data Warehouse architecture.
- This data warehouse solution can process massive amounts of data using parallel processing. As a distributed database management system, the SQL data warehouse system has overcome most of the shortcomings of traditional data storage systems.
- A single OS controls them equally. However, the need for scalability has skyrocketed due to growing business demands. This makes SQL Data Warehouse Azure even more important for any organization. Azure cloud data warehouses satisfy all requirements through a shared architecture.
- The main benefits you should know are Azure data warehouses: -Flexibility, V12 portability, Focus on safety, Polybase, High scalability
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.
