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Enterprise Data Management

Guide to Data Catalog Architecture | Components and Work Process

Chandan Gaur | 04 December 2023

 Enterprise Data Catalog Architecture

What is a Data Catalog?

The Data Catalog provides a single self-service environment, helping users find, understand, and trust the data source. It also helps users discover new data sources, if there are any. Discovering and understanding data sources are the initial steps for registering the sources. Users search for the Data Catalog Tools and filter the appropriate results based on their needs. In enterprises, Data Lake is needed for Business Intelligence, Data Scientists, and ETL Developers where the right data is needed. The users use catalog discovery to find the data that fits their needs. The key capabilities of this approach are as follows:

i. Search and discovery

ii. Data Intelligence
ii. Manage business glossary
iv. Empowering Confidence in Data

Discover further insights into the Top 5 Use Cases of Data Catalog in Enterprises.

What is Metadata?

Metadata is a thread that connects all other building materials, including ways for ingestion to be aware of sources, refinement to be connected to ingestion, and so on. Every component of the architecture contributes to the development and use of metadata.

Explore about Azure Data Catalog - Enabling Greater Value of Enterprise Data Assets

Data catalog Architecture

Data catalog architecture refers to the components that gather, manage, and organize data and its associated information to help users discover, understand, interpret, and use data. The key components of a data catalog architecture include:
Data Assets: These are the data sets that users can discover and access for analysis and decision-making


1. Data Metadata

Metadata provides information about the data assets, including their origin, lineage, format, and usage. It helps users understand the context and quality of the data

2. Data Catalog

The data catalog serves as a central repository for data assets and their associated information, enabling users to search, discover, and understand the data available to them

3. Data Governance

Data governance frameworks ensure the accuracy, reliability, and security of the data catalog, including data quality, privacy, and compliance

4. Data Lineage

Data lineage tracking helps users trace the history and provenance of data sets, providing insights into the origins and transformations of the data

5. Data Usage

Information about how data sets are used, including the queries, algorithms, and models that rely on them

6. Data Sharing and Collaboration

Data catalogs can be shared across the organization, enabling user collaboration and discovery.

7. AI/ML Capabilities

Modern data catalogs may include AI and machine learning capabilities to discover and extract metadata, minimizing manual effort for data capture.

8. Integration with Data Sources and Downstream Uses

The data catalog should support integration with various data sources and downstream uses, including known uses and extendable options for unknown requirements

9. Governance Framework

A robust governance framework is essential for data catalogs to ensure security, privacy, and compliance, especially in a conglomerate of users
Organizations can design and implement a data catalog that meets their specific data management and discovery needs by understanding the key components of a data catalog architecture.

Deep dive into the Top Enterprise Data Catalog Tools

How to Build an Effective Data Catalog?

All data catalogs are not created equal. It's critical to filter players based on key capabilities when selecting a data catalog. As a result, many data catalogs, including Talend Data Catalog, depend on critical components that will ensure your data strategy's effectiveness. Let's take a look at some of the essential features:

1. Connectors and easy-to-curation tools to build your single place of trust

The data catalog's ability to map physical datasets in your dataset, regardless of their origin or source, is enhanced by having many connectors. Using powerful capabilities, you can extract metadata from business intelligence software, data integration tools, SQL queries, enterprise apps like Salesforce or SAP, or data modeling tools, allowing you to onboard people to verify and certify your datasets for extended use.

2. Automation to gain speed and agility

Thanks to improved automation, data stewards won't waste time manually linking data sources. They'll then concentrate on what matters most: fixing and curating data quality problems for the company's good. Of course, you'll need the support of stewards to complement automation – to enrich and curate datasets over time.

3. Powerful search to quickly explore large datasets.

The quest should be multifaceted as the primary component of a catalog, allowing you to assign various criteria to perform an advanced search. Search parameters include names, height, time, owner, and format.

4. To conduct root cause analysis, use Lineage.

Lineage allows you to link a dashboard to the data it displays. Understanding the relationship between various forms and data sources relies heavily on lineage and relationship exploration. So, if your dashboard shows erroneous data, a steward may use the lineage to determine where the issue is.

6. Glossary to add business context to your data

The ability to federate people around the data is essential for governance. To do so, they must have a shared understanding of words, definitions, and how to relate them to the data. As a result, the glossary is helpful. Look for PII in a data catalog. You'll find the following data sources: It's especially useful in the context of GDPR (General Data Protection Regulation), where you need to take stock of all the data you have.

7. Profiling to avoid polluting your data lake

When linking multiple data sources, data profiling is essential for determining your data quality in completeness, accuracy, timeliness, and consistency. It will save time and enable you to spot inaccuracies quickly, allowing you to warn stewards before polluting the data lake.


How Data Catalog Works?

Building a Catalog starts with collecting the metadata of the sources. After obtaining the metadata, the metadata entities need to categorize and assign different tags. ML (Machine Learning) and NLP (Natural Language Processing) are used to automate these processes. Metadata entities auto-assign a tag according to the name of the data entity with the Machine Learning model help. Ultimately, the data steward reviews things and adds value to the Data Catalog.

A data lake is a centralized location created for the purpose of processing, storing, and protecting huge amounts of organized, semistructured, and unstructured data.5 Best Practices To Keep in Mind While Building Data Lake

What are the benefits of a Data Catalog?

The below highlighted are the benefits of Data Catalog:

1. Spend more time using the data not found

As per Forrester Forbes report, data scientists spent more than 75% of their time understanding and finding the data. And more than 75% of them don't like that part of their job—this is due to the questions which they have before working on the queries. The main reason for this problem in an organization is the poor mechanism of handling and tracking all the data. A good Catalog helps the Data Scientist or Business Analyst understand the data and answer their questions.

2. To implement Access Control

When an organization grows, role-based policies are needed; we don't want everybody to modify the data. Access Control should be implemented while building the Data Lake. Particular roles are assigned to the users, and according to those roles, Data Access should be controlled. In the Hadoop ecosystem, implement using Apache Ranger. For the sensitive data in the Data Lake, use encryption for the Data Protection.

3. To Reduce Cost by Eliminating Data Redundancies

A suitable Catalog Tool helped us find the data redundancies and eliminate them. This can help us to save storage costs and data management costs.

4. To follow Laws

There are different protection laws per the data, such as GDPR, BASEL, GDSN, HIPAA, and many more. These laws must be followed while dealing with any data. But these laws stand for different use cases and don't imply every data set. To understand that, we need to know about the data set. A good Catalog helps us ensure that Data Compliance is followed by giving a view of Data Lineage and using Access Control.

AWS Data Catalog is a prospering metadata management service nowadays. Click to explore about, AWS Data Catalog

Why does Data Catalog Matter?

1. Helps in Understanding the data

A good Catalog helps the user understand the data. A catalog makes it easier for the user to find and know the relevant data; it also gives information about the data, such as where it is being used and generated.

2. Allow users to work with multiple data sources

The catalog consists of one or more data sources. It helps users to find quality data sources and to gain better knowledge about multiple data sources.

3. To Follow Regulatory compliance

There are several data-related laws like GDPR, BASEL, and HIPAA. These laws are driven by different perspectives and use cases, but ultimately, they all come down to better data governance with a focus on Data Lineage and Access Control.

How to Adopt a Data Catalog?

Building a Catalog is a multi-step process that includes -

1. Metadata Extraction

It is the very first step of building the catalog. In this step, the metadata of a defined source is collected and stored in the metadata store. It helps in understanding the defined data asset.

2. Data Sampling

It is used to understand the schema, tables, and databases.

3. Auto-Titling (ML and NLP)

Every organization has a naming convention of using abbreviations to define the schema. Natural language processes model to assign that abbreviation a common name understood by the users who are using Catalog.

4. Query Log Ingestion

It collects additional information about the data sets and gives a complete picture of each data set, just like Data Lineage Data Usability.

5. Crowd Sourcing & Expert Sourcing

Up to this layer, the Catalog is ready; it needs to add more values to the Data Catalog. The NLP model has corrected the names of the data assets collected from the data sources, but the Computer-Human Collaboration is also necessary to verify things.

Data Catalog Best Practices: Unlocking the Power of Metadata

The best practices for Data catalog are listed below:

1. Assigning Ownership for the data set

Ownership of each data set must be defined. There must be a person to whom the user contacts in case of an issue. A good catalog must also tell about the owner of any particular data set.

2. Machine-Human Collaboration

After building a Catalog, the users must verify the data sets to make them more accurate.

3. Searchability

The Catalog should support searchability. It enables data asset discovery; data consumers can easily find assets that meet their needs.

4. Data Protection

Define Access policies to prevent unauthorized data access.

Top Data Catalog software tools

These are some top Data Catalog tools :

1. Alation Data Catalog

2. Alex Augmented Data Catalog

3. Ataccama Data Catalog

4. Atlan Data Discovery & Catalog

5. AWS Glue Data Catalog

6. Collibra Data Catalog

7. Erwin Data Catalog

Explore more in detail about the Data Catalog Tools

A Collaborative Effort in Building an Effective Data Catalog

Organizations can consolidate metadata in a single location by utilizing Data Catalogs, allowing for a comprehensive data view across databases and integrating valuable insights on its location and statistics. To implement this strategy effectively, we recommend following these recommended steps.