dwaas definition

DWaaS - Definition

What is DWaaS?

Data Warehouse as a Service (DWaaS) is an outsourcing model where the service provider takes care of, configures, and manages the hardware and software resources required, providing the full-featured capabilities a company needs without any or minimal administrative overheads. 

It has become very popular recently due to the data-centric approaches most companies have picked up. It is not difficult to understand why data has become critical for companies to operate. It does it all—providing actionable analysis, giving fresh insights, and fueling business processes that are transformed digitally. 

Unfortunately, on-premise data warehouses can be quite large and extremely costly to build and maintain. As a result, not every organization can afford to own one. This is where DWaaS comes to address the challenge. 

The core components are similar to on-premise warehouses, just that DWaaS is delivered as a cloud solution over the internet or as a solution over some private network. All the customer needs to do is provide data and pay for the managed service. 

Components related to DWaaS

  1. Source System Integration

A DWaaS involves a collection of connections and data feeds with source systems that allow a corporation to pull (or push) data into the warehouse from multiple end-users, apps, or any other media through which they generate their data. This also consists of a load manager that makes up the "front-component", performing all operations associated with loading all the received data into the warehouse to prepare it for the ETL processes.

  1. Actual Warehouse Database/s

After the data has been loaded to the warehouse, it needs to undergo processing to fit into a certain standardized form. This is because various applications or sources of data might possibly send varied forms or types of data. For example, a certain field might be numeric from one data source and a boolean from another. 

Thus, extract-transform-load (ETL) is performed on the data before it is stored in the database—the gold mine of the entire philosophy being DWaaS. This component implements actions such as data analysis to verify consistency, index and view building, denormalization and aggregate generation, source data transformation and merging, and archive and backup creation. 

The following image gives a clearer idea of the entire process. This database might or might not be the most simple-looking structure one can think of. Most companies use multiple databases to store the different types of data they come across: raw, freshly processed, or highly refined data.

  1. Administer Technical Configurations

Once we have ensured consistency in the data, it requires technical configurations such as summarization and consolidation so it can be easily used and managed. Additionally, a set of tools, including metadata (both technical and business) generators and query managers that make up the "backend" component, are used for efficient scheduling of requests for data to deliver almost delay-less services.

  1. Analytics and services

One of the most important parts of a DWaaS is the analytical capabilities it provides:

  • business intelligence
  • application development (interface supported)
  • data mining
  • statistical reporting
  • query system
  • ingestion
  • modern technology like machine learning and AI
  • other data integration tools

Benefits of DWaaS

There are many advantages to using data warehouses and DWaaS in particular. 

  • Consolidation: DWaaS consolidates data from multiple sources, acting as a single point of access for all data. It eliminates the need for users to connect to dozens, if not hundreds, of different systems, like marketing, sales, financing, and so on. The data warehouse delivers consistent data on a variety of cross-functional tasks. It also allows for ad-hoc reporting and querying. Additionally, the data warehouse assists in the integration of multiple data sources to alleviate stress on the production system.
  • High quality: Data quality, accuracy, and consistency are all critical factors. A data warehouse applies a set of semantics to data, such as naming conventions, codes for different product types, languages, currencies, and so on.
  • Reduced supervision: Moreover, a data warehouse aids in reducing user supervision. Your data warehouse makes it a point to show you discrepancies and rectify them before you enter data. This is especially useful for those who are careless or hurried when gathering data. 
  • Analysis and reporting: Utilizing a data warehouse can help speed up the analysis and reporting process. It is easier for the user to utilize data for reporting and analysis after restructuring and integrating it.
  • Time-saving: DWaaS allows users to access crucial data from a variety of sources in one place. As a result, the user saves time by not having to retrieve data from several sources. Even major decisions like scaling up happen in seconds for an infrastructure that has fluctuating demands.
  • Long-lasting: A vast volume of historical data is stored in a data warehouse. This allows users to compare and contrast different time periods and patterns to make predictions.

Challenges of DWaaS

There are also some disadvantages of using data warehouses that DWaaS is trying to address as it matures.

  • Third-Party Requirements: Using data warehouses can require the help of a third-party business intelligence team, depending on the current system. Due to the complexities of operating systems, software, and programs, it may be difficult for a business owner to figure out how to properly use their data warehouse.
  • Security: Association with third parties can lead to vulnerabilities as their policies may have exploitable points or loopholes. Attacks could expose sensitive and valuable user data. Any minor breach could result in loss of credibility and could lead to multiple lawsuit compensations depending on what was affected by the breach.
  • Performance:  Even though DWaaS separates analytical processing from traditional transactional databases, it is ultimately an online service. The service is dependant on stable connections so user experience can be highly degraded if a stable connection is not ensured. 
  • High Costs: According to International Data Corporation research, firms that invested in data warehousing produced more revenue and saved significant margins over any business model. Despite this, one should expect to spend more than their initial investment for the same. This includes regular system maintenance and upgrades, if one wants the latest technology at their fingertips for extracting the maximum value out of their data.

These are some apprehensions business owners still have about using data warehouses served to them by someone else. However as DWaaS matures like any other technology, these are likely to subside.