The modern business landscape has become more competitive than ever as enterprises continue to deploy the latest technologies to gain a leading edge over their competition. Although concrete decision making weighs a lot in attaining that edge, its timeline plays an even greater role in a business’ success. 

Making well-timed decisions is a challenge for most businesses since gaining insights from traditional data warehouses can take a while. Often there are several coding requirements, and developers have to process the acquired data before making it available for analysis. This results in severe delays in the decision-making process. As a result, business growth suffers.

To tackle this challenge, enterprises are now opting for what we call a metadata-driven data warehouse. 

What is Metadata-Driven Data Warehousing?

Metadata is ‘data about data.’ Therefore, a metadata-driven warehouse points towards a DW where a separate repository contains the information about all the data present within that warehouse. Think of it as an index for your DW, a set of data points governing your data; enabling you to swiftly access the required information to make crucial business decisions without delay. 

These metadata repositories record almost everything about the data available in the data warehouse, from current and historical records to track information in source systems and the changes that appear in the process, to the structure of the data stored. These repositories are often called data dictionaries.

 Benefits of a Metadata Driven Data Warehouse

A metadata-driven data warehouse is the most efficient way of finding data through queries. Here are some of its benefits.

Meta Driven Data Warehouses Boost Productivity 

Extracting data from a traditional data warehouse requires coding at multiple levels, especially if the data needs to be transformed before extraction. A single transformation can trigger the need to refactor the code several times. This can consume several hours and occupy the developer’s time. In addition, such low-level developments are also prone to human error. 

Metadata-driven data warehouses solve these concerns by allowing businesses to automate data mapping without writing a single line of code. Since the schema is imported, modified, and deployed automatically, business owners are able to access and understand data from any DW layer with ease, making the ETL process a lot simpler. 

Meta Driven Data Warehouses are Reliable

Each developer has their own style of writing ETL code. It can be challenging for any other developers to imitate that code, which is why replacing a resource can turn into a hassle for the enterprise. 

This concern is resolved with an automated metadata-driven data warehouse since it is based on a particular design rather than coded scripts. It enables consistency and reliability of data across the warehouse (regardless of a changing workforce) because the DW architecture remains the same. 

Meta Driven Data Warehouses are Not Technology Dependent

At the pace IT is progressing, we have new codes every few months. In a traditional DW setting, you would have to rewrite the entire code in order to jump to a newer, better platform with the latest technology.

Metadata-driven DWs eliminate the need to spend all that time redoing ETL code. You simply retarget to the newer platform and start making the most of its updated properties while your ETL patterns remain unchanged. It allows enterprises to retain their competitive edge in the market by staying on top of modern technologies and contemporary platforms. 

Meta Driven Data Warehouses Saves Monitoring Costs

Data warehouses are composed of complex interlinked data layers, which means that a single problem can trickle down errors in multiple other parts of the DW. Unfortunately, manually tracking the problem source can be challenging and costly. 

Since metadata carries information regarding on-going as well as historical developments, allowing for a more systematic way to get to the root cause of the problem. The quicker a problem is identified, the faster it can be resolved.  

In fact, DWs built with a metadata-driven approach for ETL allow users to modify rows without altering the entire set of integrated values, which can save monitoring costs associated with manually changing all the codes.

Meta Driven Data Warehouses are Easily Scalable

Building a thorough architecture for your warehouse is crucial to sustain data over long periods of time, which is why the building blocks (codes in this instance) is tested for multiple variances of commands like insert, delete, etc. Such architecture should handle changes in the DW – updating rows and columns, maintaining history, and more – seamlessly.

Ensuring all this and more can be challenging in a traditional warehouse. On the other hand, a metadata-driven data warehouse resolves these concerns through template generation – a robust template built to keep data quality intact despite complex alterations or transformations in the DW. This template also makes scalability easier for an MDW. 

Meta Driven Data Warehouses vs. Traditional Data Warehouses

In a nutshell, a traditional data warehouse is complex and time-consuming since it requires developers at every phase of the data warehousing, from designing to deployment, to write lines and lines of SQL.

On the other hand, a metadata-driven data warehouse is more practical since the concept entails using a tool to build a robust template that doesn’t require extensive coding (neither during the execution phase nor for any transformations).

This easy-to-execute and contemporary data warehousing method allows enterprises to scale easily without compromising on data quality or performance. Furthermore, business owners rely less on developers to pull data from a DW’s depths (as all data is easily accessible through the operational and historic metadata stored in the DW). In essence, a Metadata driven data model makes the entire process efficient, translating into well-informed business decisions.

All in all, it is safe to state that metadata-driven data warehousing offers more accurate decision making and therefore better business growth. 

How Astera Helps with Data Warehousing?

There are various challenges in executing a robust metadata-driven data warehouse, like integrating metadata in the system or unifying the metadata formats across different databases. 

Astera offers an end-to-end data warehousing solution – Astera DW Builder (DWB) – to remove all the hassles tied to switching from a traditional warehousing approach to a metadata-driven solution. It automates repetitive tasks like dimensional data modeling and ETL generation, which expedites the entire process of building a data warehouse from scratch.

This reduction in time and costs makes it an ideal solution for small and medium-sized businesses as well as enterprises looking for data warehouse automation tools. 

Learn more about various Astera Data Warehouse Builder (DWB) use cases.

Author Bio:

Iqbal Ahmed Alvi is a technical content writing expert at Astera, a data warehousing solution provider. He likes to write about data and its ever-growing scope, and in his spare time, he works on exciting data-focused projects.