Big Data and advanced analytics present a significant challenge. The most advanced Modern Data Warehouses are evolving to meet the needs of the Modern Data Enterprise. Hence, the volume increase is expected to continue. On the other hand, business velocity is influencing business operations and customer interactions.
Data has become more diverse and accessible than ever before. Big Data has a significant impact on business. And a data warehouse is an immediate need to tackle this data. Also, a data warehouse will be a great help in other aspects. Hence, this article will help you understand the function of a modern data warehouse and its major benefits to businesses.
What Is A Modern Data Warehouse?
A modern data warehouse comprises multiple programs that are inaccessible to the user. Polyglot persistence promotes the best data storage technology based on data. This “best-fit engineering” considers NoSQL solutions for JSON formats while aligning multi-structure data into data lakes.
Pursuing a polyglot persistence data strategy takes advantage of virtualization and different infrastructure. As a result, a data warehouse requires Petabytes of storage and more optimized techniques to run complex analytic queries. But, traditional methods are inefficient in meeting modern-day Data Warehousing needs.
There are numerous Cloud solutions for building data warehouses. These are performance-optimized, cost-effective, and support parallel query execution. It also incorporates Hadoop, a traditional data warehouse, and other data stores. And, it includes multiple repositories that may reside in different locations. It also contains data from mobile devices, sensors, the cloud, and the Internet of Things. Thus it is essential to have a modern cloud data warehouse for the data enterprises.
What are the advantages of using a modern data warehouse?
- Data integration into the environment is completed quickly.
- Integration has become more efficient, saving time, money, and effort.
- Possibility of enabling innovative new data models.
- Potential for new data insights that provide preventive and predictive analysis.
- Ability to analyze larger datasets as data collection and storage grows exponentially.
- Open-source software and commodity hardware have cost advantages.
What are the best data warehousing practices?
- Define the Data Storage and Compression Formats
There may be multiple options for data storage. Each data storage has its own set of merits and demerits. Data formats and storage must be evaluated to work smoothly with the applications.
- Keep an eye out for Support for Multiple Tenancies
Support for multi-tenancy is critical in the BI environment. It also provides the benefit of using a single software stack to serve thousands of partners and customers. Consequently, helping in upgrades and customizations.
- Examine the Schema
Examine the type of database storage. To optimize schema objects, check how it is loaded, processed, and analyzed.
- Maintain Metadata Management
Ensure complete Metadata Management for Data Warehouse projects. The success of Modern Data Warehousing projects is dependent on Metadata Management. It also collects the data required to build, use, and interpret the Data Warehouse elements.
Functions Of A Modern Data Warehouse
It has two functions: data processing and serving as a data store for analytics programs. This functionality is not available in siloed data stores.
Today’s data warehouses have the tools to analyze vast amounts of information to be used in data analytics. For companies, this is a crucial capability. More information is being created than ever before. Also, you can’t gain value from it if you can’t analyze it, and if you don’t evaluate information quickly, it loses value.
The second function is to act as a data repository for analytics programs. That is, analytics programs connect to the data warehouse and retrieve data for analysis. You can also use this feature to connect other analytics tools to your data warehouse. You are free to select the solutions you want.
There are three data analytics use cases for a modern data warehouse:
- Integrate data
- Advanced analytics
- Real-time analytics
Take a second to think about the information that your organization generates and consumes daily. You may generate sales reports, and your marketing team is almost certainly reviewing the outcomes of its social media or email campaigns. While all of that information is valuable, the value can sometimes increase dramatically.
When you combine data sources to gain new insights into your organization, one use case is integrating data into a data warehouse. This integration puts information in one place so that you can combine data sources for a different look into your operations.
Even businesses are unable to predict the future. However, advanced analytics can help you predict when something might happen. Modern data warehouses provide advanced analytics to help you make better business decisions. Many companies want to know when to expect customer churn.
Advanced analytics would identify customers who are most likely to switch to a competitor based on attributes such as purchase history and interactions. This data assists businesses in determining whether it is worthwhile to invest resources to retain these customers or whether they should focus on a more valuable segment.
Real-time analytics is another application. As the name implies, you can analyze data in real-time, eliminating the need to wait for data to load from record systems. Mobile device data is one application for real-time analytics.
Device usage in and of itself can be valuable for organizations; with a modern data warehouse. Consequently, you can connect to those data streams in real-time to understand how users are interacting with your products and services.
To capitalize on the enormous new opportunities presented by Big Data, the modern enterprise requires a modern data platform. Hence, Microsoft’s current data warehouse solution provides a platform, solutions, features, functionality, and benefits that enable the Modern Enterprise in three key areas:
- efficiently managing relational and non-relational data at all volumes and high performance,
- enjoying a consistent experience across on-premises and cloud, gaining insights from BI, and
- also applying Advanced Analytics across all data.
Thinklayer has been assisting customers in extracting value from their data. Contact us today to learn more about building a modern data warehouse for a greater return on investment for your company. Our solutions also cater to a wide range of industries, focusing on meeting ever-changing marketing requirements.