What are the Benefits of Modern Data Warehousing using Azure PaaS?

Marketing Team
Published on April 13, 2022

It is important for companies to become data-driven to adopt the process of digital transformation. Monetizing data is a sensible method of staying ahead of the competition. However, before you can use modern technologies like AI or Machine Learning, you must first prepare this data. Thus a Modern Data Warehouse is a need for virtually any company. This is due to the fact that it provides a central area where organizations can keep all of their data in one place and have a single dashboard. However, firms that employ a hybrid enterprise data warehouse design confront limits that they can't escape. Given these constraints, we intended to migrate to a cloud-based PaaS (Platform as a Service) architecture for improved data transformation and reporting capabilities.

Let's convert the customer's hybrid architecture to a full PaaS design using the Microsoft Azure cloud, traversing the various hurdles, performance enhancements, and cost-effectiveness, as well as the components and resources, employed. You can scale quickly, reduce downtime, and use this new modern data warehouse for future needs such as data science thanks to the Azure platform's PaaS resources. You can consolidate data from several operational systems into a single location, standardize it, do quality checks, and offer it to C–level executives quickly.

What is a Modern Data Warehouse?

A data warehouse is a centralized data management system that stores and consolidates data from multiple sources within an organization in order to support BI operations such as data analytics, reporting, data mining, machine learning, and many more. The data warehousing system collects, processes, and organizes data so that it can be analyzed quickly and easily by anybody in the business, making your work far easier. Despite the fact that data warehouses have been there since the 1980s, they have evolved significantly in recent years as a result of the rise of big data. Advanced analytics and data visualization techniques are increasingly available in data warehouses.

Modern Data Warehousing helps in structuring big data, and unstructured and semi-structured data through public data services. It can operate with a much larger volume and can handle various complex form of data types to generate detailed insights for you. The Microsoft concept of a Modern Data Warehouse is based on multiple Azure cloud services:

  • Azure Data Factory
  • Azure Data Lake Storage
  • Azure Databricks
  • Azure Synapse Analytics
  • Azure Analysis Services

Why do you need a Data Warehouse modernization?

Every firm needs reporting and/or dashboarding in some form. It also typically employs a variety of systems to carry out its daily activities, necessitating the acquisition, cleansing, transformation, and integration of data. The Enterprise Data Warehouse, which is at the heart of data-driven decision support systems, is likewise rapidly evolving. The EDW environment will be able to handle constantly changing business objectives and technological obstacles, as well as swiftly iterate new solutions to support future data analytics workloads at any scale, thanks to data warehouse modernization.

Why is PaaS the right choice?

PaaS comes with very less maintenance obligations, PaaS computing requires less administration and supervision from infrastructure staff (version updates, patching, etc.)

  • PaaS provides greater scalability, as well as simple and quick installation and configuration.
  • The features of high availability (HA) and disaster recovery (DR) are included out of the box.
  • The public cloud has a minimal entry fee and a pay-as-you-go concept from a financial standpoint.

Azure SQL Database, a PaaS service supplied by Azure cloud, was used to build the enterprise data warehouse. Azure SQL Database is a cloud-based database solution that provides the most comprehensive SQL Server engine. The staging database and EDW were built using Azure SQL, which is a highly available, performant, and scalable database. This supports massive amounts of data and uses Stored Procedure scripts to process the staged data. After that, the information is recorded in dimension and fact tables.

Benefits of using a modern data warehouse

There are several benefits of moving to a modern data warehouse, which influenced the decision to implement this solution:

  1. 50-70% improvement in ETL workload performance

When it comes to performing workloads, this design outperformed all others. On a daily basis, there are eight separate workloads running. These jobs load data from on-premise sources into staging, then transform and populate the Fact and Dimension tables in the EDW. The ADF and Azure Warehouse have decreased processing time and data migration by around 50% in the new modern data warehouse architecture. On average, we witnessed a 50–70 percent improvement in performance.

  1. Resource and cost management:

Using the Azure Portal, this new platform has enabled centralized auditing of all resources and their health. The customer was able to set up metric alerts on all of the resources and receive notifications for bottlenecked services or increased loads as a result of this. We can receive a more thorough record of all running programs and resource performance using log analytics. These PaaS resources can be scaled up or down as needed, or they can be turned off to save money on billing.

  1. Reduced network failures or infrastructural Issues:

In the previous environment, you could have expected 3-7 failures each month on average. This new, more modernized platform has been shown to be highly reliable and available. The workloads have been running in the production subscription instance for two months, and the client has not experienced any difficulties or task failures such as network failure, connection issues, processing deadlocks, or being out of memory.

  1. Optimized performance of Azure Analysis Service:

When two or more data models were refreshed at the same time on the old platform, we had frequent bottlenecks at the SSAS server end. Such issues are now uncommon with the new PaaS system, and the time it takes to refresh data models has decreased considerably.