Metadata

Metadata is information about data. It describes the structure, contents, and context of data. In a data warehouse, metadata is particularly important because it helps users understand the data structure, context, and meaning of the data and ensures that the data is being used effectively and efficiently.

As it’s a critical component of any data warehousing system and relevant in the past, future, and current stages of the data warehouse, investing in a robust metadata management system can make better use of an organization’s data and derive greater insights from it.

There are several types of metadata that are commonly used in data warehousing, as follows:

•     Technical metadata: This type of metadata describes the physical structure of the data, including tables, columns, indexes, and relationships between tables.

•     Business metadata: This type of metadata describes the meaning and context of the data. It includes information about the source of the data, the business rules that apply to the data, and how the data should be used.

•     Operational metadata: This type of metadata describes how the data is being used in real-time. It includes information about who is accessing the data, how often it is being accessed, and what queries are being run.

•     Usage metadata: This type of metadata describes how users are interacting with the data. It includes information about which reports are being run, which queries are being executed, and which data is being exported.

Metadata is typically stored in a metadata repository, which is a centralized database that contains information about the data warehouse. This repository can be used to manage metadata across different tools and applications, and to ensure that all users are working with consistent and accurate information.

With the large amount of data that data warehouses store, managing metadata can be a challenging task. This is where metadata management tools come in. These tools help automate metadata management, making it easier to maintain accurate and up-to-date metadata.

Let’s consider the need for metadata management with a use case of a large retail organization with a complex data warehousing environment.

The company has several databases, data marts, and data warehouses, each with different data structures and business rules. It is challenging for users to find the data they need, and there is a high risk of data inconsistencies and errors. The company decided to implement a metadata management tool to streamline metadata management.

Example: The company decides to use the Informatica Metadata Manager. They start by configuring the tool to connect to their databases, data marts, and data warehouses. The tool automatically discovers and documents metadata from these sources, providing a comprehensive view of the organization’s data assets. Users can now search for data assets across different systems, view the relationships between assets, and see the history of changes to assets.

The company also uses the tool to create a business glossary, which provides a common language for describing data. They define terms such as customer, product, and sales and link these terms to the relevant data assets. This makes it easier for users tounderstand the context and meaning of the data they’re working with.

As a result of implementing the metadata management tool, the company can improve the accuracy and consistency of its data, reduce the risk of errors, and make it easier for users to find and understand the data they need.

Following are some of the most popular metadata management software solutions for data engineering:

•     IBM Infosphere Information Server: IBM Infosphere Information Server is a comprehensive metadata management solution that provides data lineage, impact analysis, and governance capabilities.

•     Collibra: Collibra is a cloud-based metadata management platform that provides a data governance framework. The platform includes features such as data discovery, business glossary, and data lineage.

•     Alation: Alation is a cloud-based metadata management solution that provides a collaborative data catalog. It includes features such as data discovery, data cataloging, and data lineage.

•     Talend Metadata Manager: Talend Metadata Manager is a metadata management solution that provides data lineage, data mapping, and impact analysis capabilities. It can be used with the Talend Data Integration platform.

•     Informatica Metadata Manager: Informatica Metadata Manager is a metadata management solution that provides a centralized view of metadata across the organization. It includes features such as data discovery, data lineage, and impact analysis.

Note  In response to the changing data landscape, metadata management software is becoming more advanced and incorporating new features. This applies to different types of data storage such as traditional data warehouses, data lakes, delta lakes, and data mesh. Organizations are using these software tools more effectively to optimize their data management.

The following modern tools with specific usage scopes are also frequently used in addition to the well-known and evolved tools:

•     Microsoft Purview: Microsoft Purview is a cloud-based metadata management solution that allows users to discover, classify, and manage data assets across the organization. It includes features such as data discovery, data cataloging, and data lineage.

•     Apache Atlas: Apache Atlas is an open-source metadata management solution that provides data governance capabilities for Hadoop and other Big Data platforms. It includes features such as data discovery, data classification, and data lineage.

•     Cloudera Navigator: Cloudera Navigator is a metadata management solution that provides data discovery, data lineage, and data governance capabilities for Hadoop and other Big Data platforms.

•     SAP Metadata Management: SAP Metadata Management is a metadata management solution that provides a centralized repository for metadata across the organization. It includes features such as data discovery, data lineage, and impact analysis.

•  MANTA: MANTA is a metadata management solution that provides automated data lineage and impact analysis for databases, data warehouses, and Big Data platforms.

•     AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that can be used to manage metadata for data stored in Amazon S3. It includes features such as data cataloging, data lineage, and data discovery.

•     Databricks Delta: Databricks Delta is a unified data management system that can be used to manage metadata for data stored in Databricks. Delta provides features such as schema enforcement, data versioning, and data lineage.

•     Databricks Unity Catalog: Databricks Unity Catalog is a metadata management solution that is built into the Databricks Unified Analytics Platform. It provides a unified metadata catalog for all data sources within the Databricks workspace, including data stored in Databricks Delta Lake, Apache Spark, and external data sources such as AWS S3 and Azure Data Lake Storage.