Metadata Driven Approach Meets Data Warehouse Automation – A Match Made in Heaven

By |2021-05-14T11:45:31+00:00March 9th, 2021|

In the previous part, we shed light on why data warehouse automation technology should be an integral part of your data warehousing strategy.

Here, we’ll talk about metadata and why a metadata-driven approach and DWA are like the peanut butter and jelly for agile data warehouse development. In this blog, we will discuss the definition of metadata, example of metadata and the three categories of metadata. Further, it explains what is metadata in a data warehouse and its importance.

What is Metadata?

Metadata is defined as the data that acts as a directory for other data. It helps to understand data. A daily-life example to understand the concept of metadata is a book’s index. An index is a metadata that includes all the information about a book’s contents.

What is Metadata in Data Warehouse?

In a data warehouse, metadata can be many things, like data types, data formats, source and destination database tables, entity relationships, SCD patterns, and ETL mappings and transformations, and more.

As such, a metadata-driven architecture allows you to bring source database schema into a data model, customize its structure based on your business requirements, and make the data model available for subsequent processes.

When the metadata-driven approach is coupled with DWA, they become the perfect partners that streamline design, development, and deployment, leading to a robust data warehouse implementation. Such combination provides IT teams with everything they need to formulate agile and sustainable processes that help deliver high-quality outputs consistently.

Metadata answers the 5 Ws (and an H) of your business data that is essentially stored in your data warehouse.

Think of metadata as atoms. Just like atoms are the fundamental units of matter and define the structure and properties of chemical elements, metadata serves as the building blocks of your data warehouse. It provides you with the context, characteristics, and lineage of your business data down to an atomic level, allowing you to see its current and historical information.

Three Major Types of Metadata in Data Warehouse

There are three major types of metadata in data warehouse:

  1. Operational Metadata: Operational metadata provides information about the history and status of data. Examples of operational metadata would include data archive and retention rules, error logs, and data sharing rules.
  2. Technical Metadata: Technical metadata gives knowledge about the format and structure of the data. Examples of technical metadata would be column names, database system names, and data models.
  3. Business Metadata: Business metadata focuses on data governance and helps non-technical business users to understand a datawarehouse in simpler everyday language.
Types of metadata in data warehouse

Categories of Metadata In Data Warehouse

Why is Metadata in Data Warehouse Important?

The role of metadata in datawarehouse is crucial. Let’s explore what do business stakeholders and IT teams get out of the marriage of these two technologies:

Powers up the Iterative Development Culture

With a project as big as a data warehouse, it is always recommended to work in smaller, more manageable cycles rather than a big bang approach. Else, you’ll easily lose sight of the real purpose of your data warehouse: to provide trusted insights to help users answer business questions and empower data-driven decision-making.

As such, applying an iterative model is only possible when your data warehouse team is equipped with the right gear to deliver updates to your under-construction or existing data warehouse in an agile manner.

Metadata approach in data warehouse automation tools, like Astera DW Builder, enable your team to rapidly build prototypes around your proposed business logic, ensuring the reliability and accurateness of your data warehousing processes. Once you have successfully created, tested, and implemented the prototype of one of your reporting flows, you can create a repeatable process for other analytics projects. This is because Astera DW Builder heavily automates repetitive tasks and allows you to repurpose existing models and flows for faster development.

Futureproofs Your Data Warehouse Deployment

Data Warehouse Deployment

Data Warehouse Deployment (Credits: MotionPoint)

Data warehouses should be designed as ever-expanding systems that can easily welcome and embrace changes as they occur. Business users are continually discovering new requirements that need to be reflected in reporting dashboards to base their analysis and predictions on the most recent data and conditions.

With a metadata-driven architecture, IT teams don’t have to worry about keeping up with upstream and downstream dependencies. Developers can rest assured that updating the existing infrastructure with the new requirements won’t result in a ripple effect that might disrupt your data warehouse implementation’s integrity and usability.

Astera DW Builder captures changes on the metadata level, saving you from manually coding them in separately in various areas, such as dimensional models, ETL flows, and staging tables. Since it boasts logical development, all you have to do is update your data models and redeploy them to reflect the changes across multiple development environments and, consequently, to your data warehouse fueling your analytics projects.

Gives the Confidence to Move to the Cloud

Data Warehouse Cloud

Now let’s look at the metadata and data warehouse automation wedlock from the cloud perspective.

Businesses are moving away from the on-premise infrastructure, at least most of their data ecosystem, if not all, to the cloud. That’s primarily because of the world of options the cloud providers offer to store and manage data. There are one-click scalability options, unlimited compute power, zero hardware requirements for storing petabytes, fast and easy access to information for business users, improved query performance, and the list goes on.

Since metadata holds all the contextual information about your enterprise data ecosystem, it is basically agnostic to the platform it is used to build the data warehouse. This means you can easily switch and shift your data warehouse to a more suited DW architecture to meet your changing business needs.

The role of metadata in data warehouse automation tools here is that they take the underlying code and automatically transform it to work in the target cloud platform, saving your developers from going back to the drawing board to rewrite the code. With this, you can select Snowflake, Azure, Oracle, Redshift, or any other cloud provider to build or migrate your data warehouse.

How Does Astera DW Builder Empower Metadata-Driven Data Warehousing?

Astera DW Builder simplifies and automates data warehouse development end-to-end, using the agile metadata-driven approach. The product fetches metadata directly from source databases and allows you to utilize it in the design, development, and deployment phases of your data warehouse. Once implemented, introducing changes to the design is easy as the captured metadata allows you to propagate changes across the board while ensuring the integrity of existing models, integration flows, and deployments.

Want to see these two technologies in action together? Request a live product demonstration today for your use case or talk to our experts to see the value Astera DW Builder can bring to your data warehousing initiatives.