Modern Data Warehousing Concepts- All You Need to Know About


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Do you still adhere to conventional data warehousing approaches? Don’t you get troubled in maintaining cloud-born data or ever-rising structured or unstructured data?

Well, if you are using conventional ways, then assuredly you get troubled in these sort of circumstances so often. But not now! Today, you will get a simple yet smart solution to all these issues with Modern Data Warehousing Concept.

Multidimensional data processing, real-time data virtualization, and many other privileges are offered by logical warehouses. Usually, new concepts face a strong backlash from organizations, but this time you need to remodel your strategies for not just better but for the best outcomes.

Why Modern Data Warehouse has become a prerequisite?

The conventional warehousing focused on transaction processing instead of the values and laid down the attention on data sources, applications, infrastructure, and analytics. On its opposite, modern data warehousing focuses on table storage, object storage, programming languages, and computation & processing. So, overall its focus lies in value processing instead of transactions, which means more favorable outcomes.

Okay, as you got why the modern approach is best, now the question arises that why should you take this data warehousing initiative?

Well, to know the need, you need to first ask yourself these given questions. It will navigate you clearly further that, what should you exactly do.

● Do, IT holds the capacity to harness data integration and data virtualization?
● Can IT grasp data coming from all the scattered sources?
● Do you have the multi-platform architecture to hike up your performance and scalability levels? If yes, then can
it handle high-velocity data analysis of real-time?
● Do you have any mechanism for improved agility, automated orchestration etc.?
● Does your organization support the business intelligent model?
● Can IT tackle the data flowing via sensors and several machines?

Just ask these questions, and your answers will help you envisioning engineering solutions in the best possible way. You will be able to make a proper data strategy to handle it out.

What should be your criteria for the successful implementation of data warehousing?

●Check out Data Storage
Your first step should be to check the storage options that you have. There wouldn’t be just one option, of course. So, you need to first see what options do you have and how that all are benefitting you? Evaluate these options and check the formats in relation to applications, so that you can understand their smooth or interrupted working.

●Find Multi-tenancy Support
Why should you look for multi-tenancy support? Well, there are numerous benefits you can behold, after having it. With single software stack, you will be able to serve Innumerable customers as well as partners besides customizations and quick upgrades. So, for a smart business environment or one can say, BI (Business Intelligent) surroundings, you need to necessarily get multi-tenancy support.

●Review Schema Objects
You must know the nature of databases that you are continually storing. For that, do a proper evaluation, including, verification and analysis of data loaded for optimization of schema objects.

●Metadata Management
For data warehousing initiatives, you also need to ensure metadata management. If you successfully manage it, then you can easily capture the required info to build and interpret the data used in warehousing.


For data warehousing projects, enterprise-class B2B data integration helps a ton. With cloud-based integration, you will get the best results for framing the right strategies for your business unit and data warehousing. It’s time to halt the interruptions by embracing enterprise-class integration solutions and modern warehousing concepts.

Chandra Shekhar
Chandra Shekhar is a product marketing enthusiast who likes to talk about business integration and how enterprises can gain a competitive edge by better customer data exchange. He has 8 years of experience in product marketing for SaaS companies.


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