Data Catalog as a Metadata Hub
The data catalog is a metadata hub, connecting all of your data assets to each other and to the people who need them. By making it easy to find and understand data, the data catalog enables better decision-making across the organization.
The Data Catalog of the Future
The data catalog of the future will be powered by active metadata.
The new data quality system lets the company manage all aspects of identifying, tracking, and fixing problems with their data without overwhelming anyone.
It will be able to automatically generate metadata and make it available through APIs, enabling intelligent data to use cases like observability, cost management, quality control, and more.
Active Metadata: is a key enabler for intelligent data. It is the missing link between data and insights.
There are many possible use cases for active metadata, but they all have one thing in common: they use metadata to take action on data.
But why metadata?
The idea of metadata is not new. In fact, it has been around for centuries. The term itself was first introduced in the late 18th century, and it has been used in a variety of different fields ever since. However, it is only recently that metadata has begun to gain traction as a tool for data management. There are a few reasons for this.
First, the volume and variety of data that organizations must deal with have exploded in recent years. This has made it increasingly difficult for humans to keep track of all their data assets, let alone understand what they contain. Metadata can help with both of these problems by providing a way to organize and structure data so that it can be more easily discovered and understood.
Second, the rise of big data and artificial intelligence has created a need for more intelligent data management. Organizations are now looking for ways to automate the management of their data assets, and metadata is a key component of this.
Importance of Active Metadata
Organizations today are struggling to manage their data effectively. They have too much data, and it is spread across too many silos.
They lack visibility into their data, and they don’t have the tools or the expertise to effectively govern it.
As a result, their data management processes are inefficient
There are a few key things that need to happen to make this a reality:
1. Developers need to start thinking about metadata as first-class citizens.
This means that every time you create or update a piece of data, you should also be creating or updating the metadata associated with it.
2. We need to start storing metadata in a format that can be easily queried and analyzed
By storing metadata in a structured format like Apache Hive, we can start to run queries and perform analytics on it just like we would with any other data.
3. We need to build APIs and tooling that makes it easy to access and work with metadata.
4. We need to start using metadata to drive intelligent data pipelines. By using metadata to drive these pipelines, we can make them much more intelligent and efficient.
Metadata as the “north star” for data-driven organizations
The most important thing that metadata can do is to help data-driven organizations align around a common understanding of their data.
This is what I call the “north star” use case for metadata.
The north star use case for metadata is to help data-driven organizations align around a common understanding of their data. This shared understanding is what I call the “data model.”
The data model is a representation of the data that is shared by all members of the organization. It includes things like the names of tables and columns, the data types, and the relationships between tables.
The data model is the cornerstone of the data-driven organization. It is the foundation upon which all decisions are made.
Getting all of these advantages requires excellent metadata, which is delivered through data cleaning and preparation utilizing AI and ML technologies.