Introduction

In the age of data-driven decision-making, managing vast amounts of data and ensuring its accessibility, quality, and governance are critical to business success. A data catalog is a powerful tool that helps organizations centralize their data assets, providing metadata, data lineage, and governance features for users to easily discover, understand, and trust their data.

This 5-day hands-on training course is designed to help professionals implement and manage data catalogs effectively within their organization. Participants will learn how to set up, maintain, and optimize a data catalog solution, explore its key features like data discovery, metadata management, and data governance, and understand its role in improving data quality and accelerating business intelligence and analytics.

Objectives

By the end of this course, participants will be able to:

• Understand the core concepts and benefits of implementing a data catalog.
• Learn how to select the right data catalog tools and integrate them with existing data systems.
• Gain expertise in managing metadata, data discovery, and data lineage within the catalog.
• Implement effective data governance policies using a data catalog.
• Enable self-service data discovery and empower business users to trust and access data.
• Explore integration with cloud-based data platforms, data lakes, and data warehouses.
• Understand the role of data catalogs in supporting compliance, security, and data quality management.

Who Should Attend?

This course is ideal for:

• Data Engineers responsible for building and managing data pipelines and data architectures.
• Data Stewards involved in data quality, governance, and metadata management.
• Data Architects designing scalable and efficient data ecosystems.
• Business Intelligence Analysts and Data Scientists who need to discover and analyze data.
• IT Managers and CIOs leading data-driven initiatives and data governance practices.
• Compliance Officers focusing on regulatory requirements, auditing, and governance of data.

Day 1: Introduction to Data Catalogs and Their Role in Data Management

• Overview of Data Catalogs

  • What is a data catalog? Key features and components
  • Data catalog vs. data warehouse vs. data lake
  • Benefits of implementing a data catalog in modern data environments

• The Role of Data Catalogs in Data Governance

  • Managing metadata and data lineage
  • Data stewardship and ensuring data quality
  • Ensuring data compliance and regulatory reporting

• Key Features of a Data Catalog

  • Data discovery: Searching, filtering, and classifying data
  • Metadata management: Storing and organizing metadata
  • Data lineage: Tracking the flow and transformations of data
  • Data quality: Managing data profiling and validation

• Selecting the Right Data Catalog Solution

  • Overview of popular data catalog tools (e.g., Alation, Collibra, AWS Glue Data Catalog, Microsoft Purview, Apache Atlas)
  • Cloud vs. on-premises solutions: Pros and cons
  • Integration capabilities with ETL, BI tools, and data lakes

• Hands-on Lab:

  • Exploring a data catalog tool (e.g., Alation or AWS Glue) and performing basic data discovery and metadata management tasks.

Day 2: Metadata Management and Data Lineage

• Understanding Metadata in Data Catalogs

  • Types of metadata: Business metadata, technical metadata, operational metadata
  • How to capture and maintain metadata from diverse data sources
  • The importance of metadata for data governance and data quality management

• Data Lineage and its Role in Data Catalogs

  • What is data lineage? Understanding data flow and transformations
  • Visualizing and tracking the movement of data across systems
  • Benefits of data lineage: Impact analysis, data troubleshooting, and auditability

• Implementing Metadata Management Best Practices

  • Organizing and classifying metadata for easy discovery and understanding
  • Ensuring metadata consistency and standardization
  • Integrating metadata with other data management systems (e.g., ETL pipelines, data warehouses)

• Hands-on Lab:

  • Mapping data lineage for a dataset and visualizing it using a data catalog tool

Day 3: Data Governance, Security, and Compliance

• Data Governance Frameworks

  • Building a robust data governance strategy using a data catalog
  • Roles and responsibilities in data governance: Data stewards, data owners, and data users
  • Managing data access and ensuring data privacy

• Implementing Data Security

  • Securing sensitive data in the catalog (e.g., PII, financial data)
  • Role-based access control (RBAC) and fine-grained access management
  • Data masking and encryption techniques

• Ensuring Compliance with Data Regulations

  • Data catalog’s role in ensuring compliance with GDPR, HIPAA, and other regulations
  • Auditing data usage and implementing data retention policies
  • Reporting and tracking data access and data lineage for regulatory purposes

• Hands-on Lab:

  • Implementing data governance policies using a data catalog tool
  • Configuring security settings and access controls in a data catalog

Day 4: Data Discovery, Collaboration, and Data Stewardship

• Enabling Self-Service Data Discovery

  • Empowering business users to discover, understand, and trust data
  • Data profiling: Understanding data types, quality, and content
  • Using tags, comments, and ratings to improve data accessibility

• Data Stewardship Best Practices

  • The role of data stewards in ensuring data quality and consistency
  • Implementing data quality checks and validating incoming data
  • Managing data definitions, glossaries, and business rules

• Facilitating Collaboration Through Data Catalogs

  • Encouraging data sharing and collaboration within teams
  • Documenting and sharing data insights and business logic
  • Using data catalogs to streamline communication between technical and business users

• Hands-on Lab:

  • Conducting data discovery using a data catalog tool and adding business metadata
  • Setting up data stewardship workflows

Day 5: Advanced Topics in Data Catalog Implementation

• Integrating Data Catalogs with Data Lakes, Warehouses, and BI Tools

  • Integrating a data catalog with data lakes (e.g., AWS S3, Azure Data Lake)
  • Connecting a catalog with data warehouses (e.g., Snowflake, Redshift, BigQuery)
  • Enabling BI tools (e.g., Tableau, Power BI) to leverage cataloged data for analytics

• Scaling Data Catalogs for Large Enterprises

  • Managing metadata across multiple business units and departments
  • Implementing distributed catalog systems for global organizations
  • Automating metadata ingestion and catalog updates

• Future Trends in Data Catalogs

  • The role of AI/ML in automating data classification and metadata management
  • Blockchain and data catalogs for provenance and audit trails
  • The rise of cloud-native data catalog solutions

• Final Project: Implementing a Data Catalog Solution

  • Design and implement a data catalog solution for a real-world use case
  • Incorporate data governance, security, and metadata management principles

• Course Wrap-Up & Certification

  • Review of key concepts and takeaways
  • Final Q&A session and discussion on industry-specific implementations
  • Certification of completion