Unlocking the Potential of the Modern Data Stack

The course is designed to provide a comprehensive understanding of the modern technologies and tools used in building data infrastructure and analytics pipelines. It focuses on the latest advancements in data management, processing, and analysis that enable organizations to effectively leverage data for decision-making. The course is typically designed for individuals or professionals who are interested in working with data or involved in data-related roles within organizations.

•  An overview of the components and architecture of a modern data stack, including data sources, data integration, data warehousing, and data visualization.

• Techniques and tools for collecting data from various sources, such as databases, APIs, web scraping, and streaming data. Exploring methods to cleanse, transform, and integrate data into a unified format.

• An examination of modern data warehousing solutions, including cloud-based data warehouses like Amazon Redshift, Google BigQuery, or Snowflake. Understanding how to design and manage scalable, high-performance data storage systems.

• Exploring Extract, Transform, Load (ETL) processes and tools used to process and prepare data for analysis. Introduction to workflow management tools like Apache Airflow or Luigi for orchestrating data pipelines.

• Techniques for designing data models and schemas that support efficient querying and analysis. Understanding concepts like star schemas, snowflake schemas, and dimensional modeling.

• Basic Data Literacy

• Python Programming Knowledge

• Database Fundamentals

• Statistics and Mathematics

• Familiarity with Data Analysis Tools

• Introduction to Modern Data Stack

  • Overview of data stack components and their role in data management and analytics
  • Understanding the evolution of data stacks and their benefits in modern organizations

• Data Collection and Integration

  • Techniques for collecting data from various sources, such as databases, APIs, and streaming data
  • Data integration methods and tools for cleansing, transforming, and combining data

• Data Warehousing and Storage

  • Introduction to modern data warehousing concepts and architectures
  • Cloud-based data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake)

• Data Processing and ETL

  • Extract, Transform, Load (ETL) processes and best practices
  • Workflow management tools for orchestrating data pipelines (e.g., Apache Airflow, Luigi)

• Data Modeling and Schema Design

  • Data modeling techniques for efficient querying and analysis
  • Designing star schemas, snowflake schemas, and dimensional models

• Data Visualization and Reporting

  • Principles of data visualization and best practices
  • Using visualization tools (e.g., Tableau, Power BI) to create interactive dashboards and reports

• Data Governance and Security

  • Data governance frameworks and practices
  • Ensuring data security, privacy, and compliance in a modern data stack