Deploying a Data Catalog for Your Databases: Driving Successful Business Outcomes

Last Updated on: May 12th, 2023, Published on: May 12th, 2023

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A data catalog is the backbone of modern data management, enabling organizations to find, understand, trust, and use their data effectively.

Now, before we move ahead to understand how to deploy a data catalog for databases, it is important to understand a few basic concepts. That includes knowing what are the purposes, structures, and uses cases of a data warehouse and a database. Learn the difference between a data warehouse and a database.

Table of contents

  1. Data warehouse vs database: A quick primer
  2. Why deploying a data catalog for databases is crucial for your business
  3. 5 Ways a data catalog interacts with metadata in your database
  4. How to set up a data catalog for a database
  5. Challenges to watch out for
  6. Rounding it all up
  7. Data catalog for databases: Related reads

Data warehouse vs database: A quick primer

A data warehouse and a database are both systems for storing and managing data, but they differ in purpose, structure, and use cases which we will briefly understand in this section.

Data warehouse: Purpose, structure, and use cases


Purpose

A data warehouse is designed for large-scale data storage, analysis, and reporting. It focuses on the efficient storage, retrieval, and processing of vast amounts of structured and semi-structured data from various sources.

Structure

Data warehouses use a different schema design (often star or snowflake schema) that optimizes data for analytical queries and reporting. They also employ techniques like data indexing, partitioning, and materialized views to improve query performance.

Use cases

Data warehouses are best suited for business intelligence, analytics, reporting, and data mining. They are designed for handling large volumes of historical data and complex queries to support decision-making.

Database: Purpose, structure, and use cases


Purpose

A database is designed for storing, managing, and retrieving data for individual applications or systems. It focuses on efficient data storage and real-time transaction processing.

Structure

Databases typically use a relational schema design (tables, columns, and rows) that optimizes data for transactional processing. They also use normalization techniques to minimize data redundancy and maintain data integrity.

Use cases

Databases are best suited for transactional systems, such as web applications, e-commerce systems, and other online services that require real-time data processing and storage.

Now that we have understood the functions of a data warehouse and a database, let us explore how to start deploying a data catalog for your databases.

Why deploying a data catalog for databases is crucial for your business

Deploying a data catalog for your databases can be beneficial in various ways, as it serves as a centralized repository for metadata, making it easier to discover and understand the data stored across your databases.

Here are some benefits of deploying a data catalog:

  1. Enhanced data discovery
  2. Improved data understanding
  3. Better collaboration
  4. Data governance
  5. Increased productivity

Let us look into each of these benefits in detail:

1. Enhanced data discovery


A data catalog makes it easier for users to find relevant data assets by providing searchable metadata, tags, and descriptions. This speeds up the process of locating the required information and promotes data reuse.

2. Improved data understanding


Data catalogs offer detailed information about the data, such as data lineage, data relationships, data profiling, and data quality metrics. This helps users better understand the data and its context, increasing trust in the data.

3. Better collaboration


A data catalog enables collaboration among data users, allowing them to share knowledge, add comments, and annotate data assets. This fosters a data-driven culture and encourages knowledge sharing across teams.

4. Data governance


Data catalogs support data governance initiatives by providing a clear view of data ownership, lineage, and quality. This aids in compliance with data protection regulations and promotes responsible data management.

5. Increased productivity


With a data catalog, users can quickly find and understand the data they need, reducing time spent on manual data discovery and improving the overall efficiency of data-driven processes.

By deploying a data catalog for your databases, you can drive better business outcomes through increased data discovery, understanding, and collaboration. This enables users to make more informed decisions and leverage data assets effectively, ultimately contributing to the organization’s success.

5 Ways a data catalog interacts with metadata in your database

A data catalog interacts with metadata in databases to provide a comprehensive view of an organization’s data assets. Metadata is data about data, and it provides crucial information about the structure, meaning relationships, and quality of the data in your databases. The data catalog collects, organizes, and presents this metadata in a user-friendly and searchable manner.

Here’s how a data catalog interacts with metadata in databases:

  1. Metadata extraction
  2. Metadata enrichment
  3. Metadata organization
  4. Metadata search and discovery
  5. Metadata management and governance

Let us look into each of the above aspects in detail:

1. Metadata extraction


The data catalog connects to various data sources, such as databases, data warehouses, and other data storage systems, and extracts metadata from these sources. This process may involve using APIs, connectors, or custom scripts to access and retrieve metadata, which includes table schemas, column names, data types, constraints, relationships, and more.

2. Metadata enrichment


Once the metadata is extracted, the data catalog enriches it by adding more information. This may include data lineage (tracking the data’s origin and transformations), data profiling (providing summary statistics and identifying data quality issues), and semantic tagging (adding business context and meaning to data assets). Data catalog users can also contribute to metadata enrichment by adding descriptions, comments, and annotations to data assets.

3. Metadata organization


The data catalog organizes the collected and enriched metadata into a structured and navigable format. It may use techniques like data classification, categorization, and taxonomy to help users quickly find and understand the data assets they need.

4. Metadata search and discovery


The data catalog provides a search interface that allows users to search for data assets based on metadata attributes, such as keywords, tags, descriptions, or data sources. The search functionality may incorporate advanced features like natural language processing or machine learning-based recommendations to make it easier for users to find the data they need.

5. Metadata management and governance


The data catalog supports metadata management and governance by providing tools for tracking data lineage, maintaining data quality, and monitoring data usage. It also helps enforce data policies, access controls, and regulatory compliance.

In summary, a data catalog interacts with metadata in databases by extracting, enriching, organizing, and presenting it in a user-friendly manner. This allows users to easily discover, understand, and collaborate on data assets, fostering a data-driven culture and improving decision-making within the organization.

How to set up a data catalog for a database: A step-by-step guide

Setting up a data catalog for a database such as MySQL involves several steps and considerations. Here’s a high-level overview of the process:

  1. Choose a data catalog solution
  2. Establish connectivity
  3. Plan metadata extraction
  4. Prepare for metadata enrichment
  5. Implement data catalog
  6. Extract and load metadata
  7. Validate and refine
  8. Train users and promote adoption
  9. Monitor and maintain

Let us look into each of the above steps in detail:

1. Choose a data catalog solution


There are several data catalog solutions available in the market, both open-source and commercial. Some popular options include Atlan, Amundsen, and Apache Atlas. Evaluate the features, compatibility, scalability, and cost of each solution to choose the one that best fits your organization’s needs.

2. Establish connectivity


Ensure that the chosen data catalog solution can connect to your MySQL database. This may involve setting up appropriate drivers, connectors, or APIs to facilitate data extraction and metadata retrieval. You might also need to configure access credentials and permissions to allow the data catalog to access the database.

3. Plan metadata extraction


Determine the scope and frequency of metadata extraction from your MySQL database. This could involve deciding which tables, schemas, or databases to include in the data catalog, and how often the metadata should be updated to keep the catalog current. Consider any performance implications of the extraction process on your database, especially during peak usage periods.

4. Prepare for metadata enrichment


Identify any additional sources of metadata or business context that can be used to enrich the metadata extracted from your MySQL database. This could include data dictionaries, data lineage information, or user-generated annotations. Plan the process for incorporating this information into the data catalog.

5. Implement data catalog


Install and configure the chosen data catalog solution, following the vendor’s guidelines and best practices. This may involve setting up the necessary infrastructure, such as servers, storage, and networking components, as well as configuring the software, security settings, and user access controls.

6. Extract and load metadata


Initiate the metadata extraction process to populate the data catalog with information from your MySQL database. Monitor the extraction and loading process to ensure that it completes successfully and troubleshoot any issues that arise.

7. Validate and refine


Once the data catalog is populated, validate the accuracy and completeness of the metadata. Engage users to test the data catalog, provide feedback, and contribute additional context or annotations as needed. Refine the catalog based on user feedback and continuously improve its usability and value.

8. Train users and promote adoption


Provide training and support to help users understand and utilize the data catalog effectively. Encourage a data-driven culture by promoting the benefits of the data catalog and showcasing successful use cases.

9. Monitor and maintain


Regularly monitor the data catalog’s performance, usage, and metadata freshness. Address any issues that arise and plan for ongoing maintenance, updates, and enhancements.

By following these steps, you can set up a data catalog for your MySQL database that helps users discover, understand, and collaborate on your data assets, ultimately driving better decision-making and business outcomes.

Deploying a data catalog for a database or multiple databases: Challenges to watch out for

Deploying a data catalog for a single database or multiple databases can present several challenges. Some of these challenges include:

  1. Data source diversity
  2. Metadata quality
  3. Scalability
  4. Data security and compliance
  5. User adoption
  6. Data governance integration
  7. Change management
  8. Resource constraints

Let us look into each of the above challenges in detail:

1. Data source diversity


Organizations often have multiple databases and data storage systems, each with its own data structure, format, and access methods. Integrating these diverse data sources into a single data catalog can be complex and may require custom connectors, drivers, or scripts.

2. Metadata quality


The quality of the metadata extracted from databases is crucial for the usefulness of the data catalog. Incomplete, inconsistent, or inaccurate metadata can lead to confusion and mistrust among users. Ensuring the quality of metadata is a continuous challenge that requires ongoing maintenance and refinement.

3. Scalability


As the number of databases and data assets in an organization grows, the data catalog must scale to accommodate the increasing volume of metadata. Managing this growth and ensuring the catalog remains performant and responsive can be challenging.

4. Data security and compliance


Ensuring data security and compliance with data protection regulations is a critical concern when deploying a data catalog. This involves managing access controls, encryption, and data masking, as well as tracking data lineage and maintaining audit logs.

5. User adoption


For a data catalog to be successful, users must understand its benefits and adopt it in their daily workflows. Overcoming resistance to change and promoting a data-driven culture can be a significant challenge.

6. Data governance integration


Integrating the data catalog with existing data governance processes and tools can be difficult. Organizations need to ensure that the data catalog supports their data governance objectives and helps maintain data quality, consistency, and compliance.

7. Change management


Databases and data sources may undergo changes over time, such as schema updates, data migrations, or decommissioning. Keeping the data catalog up-to-date and accurately reflecting these changes can be challenging.

8. Resource constraints


Deploying a data catalog requires an investment in infrastructure, software, and human resources. Organizations may face budgetary or resource constraints that make it difficult to fully implement and maintain a data catalog.

To overcome these challenges, organizations should carefully plan their data catalog deployment, invest in training and user support, and establish processes for ongoing maintenance, updates, and improvements. By addressing these challenges, organizations can successfully deploy a data catalog that enhances data discovery, understanding, and collaboration, leading to better decision-making and business outcomes.

Rounding it all up

In this blog, we learnt the importance of data catalogs in managing data effectively and driving business benefits. Deploying data catalogs for databases can drive better business outcomes through enhanced data discovery, understanding, collaboration, governance, and increased productivity.

Deploying a data catalog starts the seeding process of data democratization and data enablement in your organization. It says that your organization is serious about maximizing the value of data. It also recognizes that we can extract much more from data when we create an even playing field for the diverse data users in an organization. A data catalog is a starting point for that inclusive initiative.

Are you looking for a data catalog for your organization — you might want to check out Atlan.

Here’s why:

  • The latest Forrester report named Atlan a leader in Enterprise Data Catalog for DataOps, giving the highest possible score in 17 evaluation criteria including Product Vision, Market Approach, Innovation Roadmap, Performance, Connectivity, Interoperability, and Portability.
  • Atlan enjoys deep integrations and partnerships with best-of-breed solutions across the modern data stack. Check out our partners here.
  • Atlan already enjoys the love and confidence of some of the best data teams in the world including WeWork, Postman, Monster, Plaid, and Ralph Lauren — to name but a few. Check out what our customers have to say about us here.

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