From Raw Data to Governance: Refining Data with the Medallion Architecture Dec 2025

Overview

Build upon your existing data engineering expertise and discover how Medallion Architecture can transform your data strategy. This session provides a hands-on approach to implementing Medallion principles, empowering you to create a robust, scalable, and governed data platform.

We'll explore how to align data engineering processes with Medallion Architecture, identifying opportunities for optimization and improvement. By understanding the core principles and practical implementation steps, you'll learn how to optimize data pipelines, enhance data quality, and unlock valuable insights through a structured, layered approach to drive business success.

• Follow this GitHub repo during the presentation: (Give it a star)

👉 https://github.com/ozkary/data-engineering-mta-turnstile

• Read more information on my blog at:

👉 https://www.ozkary.com/2023/03/data-engineering-process-fundamentals.html

YouTube Video - Dec 2025

Video Agenda

• Introduction to Medallion Architecture

  • Defining Medallion Architecture
  • Core Principles
  • Benefits of Medallion Architecture

• The Raw Zone

  • Understanding the purpose of the Raw Zone
  • Best practices for data ingestion and storage

• The Bronze Zone

  • Data transformation and cleansing
  • Creating a foundation for analysis

• The Silver Zone

  • Data optimization and summarization
  • Preparing data for consumption

• The Gold Zone

  • Curated data for insights and action
  • Enabling self-service analytics

• Empowering Insights

  • Data-driven decision-making
  • Accelerated Insights

• Data Governance

  • Importance of data governance in Medallion Architecture
  • Implementing data ownership and stewardship
  • Ensuring data quality and security

Why Attend:

Gain a deep understanding of Medallion Architecture and its application in modern data engineering. Learn how to optimize data pipelines, improve data quality, and unlock valuable insights. Discover practical steps to implement Medallion principles in your organization and drive data-driven decision-making.

Presentation

Introducing Medallion Architecture

Medallion architecture is a data management approach that organizes data into distinct layers based on its quality and processing level.

• Improved Data Quality: By separating data into different zones, you can focus on data quality at each stage.
• Enhanced Data Governance: Clear data ownership and lineage improve data trustworthiness.
• Accelerated Insights: Optimized data in the Silver and Gold zones enables faster query performance.
• Scalability: The layered approach can accommodate growing data volumes and complexity.
• Cost Efficiency: Optimized data storage and processing can reduce costs.

The Raw Zone: Foundation of Your Data Lake

The Raw Zone is the initial landing place for raw, unprocessed data. It serves as a historical archive of your data sources.

• Key Characteristics:
  • Unstructured or semi-structured format (e.g., CSV, JSON, Parquet)
  • Data is ingested as-is, without any cleaning or transformation
  • High volume and velocity
  • Data retention policies are crucial
• Benefits:
  • Preserves original data for potential future analysis
  • Enables data reprocessing
  • Supports data lineage and auditability

The Bronze Zone: Transforming Raw Data

The Bronze Zone is where raw data undergoes initial cleaning, structuring, and transformation. It serves as a staging area for data before moving to the Silver Zone.

• Key Characteristics:
  • Data is cleansed and standardized
  • Basic transformations are applied (e.g., data type conversions, null handling)
  • Data is structured into tables or views
  • Data quality checks are implemented
  • Data retention policies may be shorter than the Raw Zone
• Benefits:
  • Improves data quality and consistency
  • Provides a foundation for further analysis
  • Enables data exploration and discovery

The Silver Zone: A Foundation for Insights

The Silver Zone houses data that has been further refined, aggregated, and optimized for specific use cases. It serves as a bridge between the raw data and the final curated datasets.

• Key Characteristics:
  • Data is cleansed, standardized, and enriched
  • Data is structured for analytical purposes (e.g., normalized, de-normalized)
  • Data is optimized for query performance (e.g., partitioning, indexing)
  • Data is aggregated and summarized for specific use cases
• Benefits:
  • Improved query performance
  • Supports self-service analytics
  • Enables advanced analytics and machine learning
  • Reduces query costs

The Gold Zone: Your Data's Final Destination

• Definition: The Gold Zone contains the final, curated datasets ready for consumption by business users and applications. It is the pinnacle of data transformation and optimization.
• Key Characteristics:
  • Data is highly refined, aggregated, and optimized for specific use cases
  • Data is often materialized for performance
  • Data is subject to rigorous quality checks and validation
  • Data is secured and governed
• Benefits:
  • Enables rapid insights and decision-making
  • Supports self-service analytics and reporting
  • Provides a foundation for advanced analytics and machine learning
  • Reduces query latency

The Gold Zone: Empowering Insights and Actions

The Gold Zone is the final destination for data, providing a foundation for insights, analysis, and action. It houses curated, optimized datasets ready for consumption.

• Key Characteristics:
  • Data is accessible and easily consumable
  • Supports various analytical tools and platforms (BI, ML, data science)
  • Enables self-service analytics
  • Drives business decisions and actions
• Examples of Consumption Tools:
  • Business Intelligence (BI) tools (Looker, Tableau, Power BI)
  • Data science platforms (Python, R, SQL)
  • Machine learning platforms (TensorFlow, PyTorch)
  • Advanced analytics tools

Data Governance: The Cornerstone of Data Management

Data governance is the framework that defines how data is managed within an organization, while data management is the operational execution of those policies. Data Governance is essential for ensuring data quality, consistency, and security.

Key components of data governance include:

• Data Lineage: Tracking data's journey from source to consumption.
• Data Ownership: Defining who is responsible for data accuracy and usage.
• Data Stewardship: Managing data on a day-to-day basis, ensuring quality and compliance.
• Data Security: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction.
• Compliance: Adhering to industry regulations (e.g., GDPR, CCPA, HIPAA) and internal policies.

By establishing clear roles, responsibilities, and data lineage, organizations can build trust in their data, improve decision-making, and mitigate risks.

Data Transformation and Incremental Strategy

The data transformation phase is a critical stage in a data warehouse project. This phase involves several key steps, including data extraction, cleaning, loading, data type casting, use of naming conventions, and implementing incremental loads to continuously insert the new information since the last update via batch processes.

Data Lineage: Tracks the flow of data from its origin to its destination, including all the intermediate processes and transformations that it undergoes.

Data Governance : Metadata

Assigns the owner, steward and responsibilities of the data.

Summary: Leverage Medallion Architecture for Success

• Key Benefits:
  • Improved data quality
  • Enhanced governance
  • Accelerated insights
  • Scalability
  • Cost Efficiency.

We've covered a lot today, but this is just the beginning!

If you're interested in learning more about building cloud data pipelines, I encourage you to check out my book, 'Data Engineering Process Fundamentals,' part of the Data Engineering Process Fundamentals series. It provides in-depth explanations, code samples, and practical exercises to help in your learning.

Upcoming Talks:

Join us for subsequent sessions in our Data Engineering Process Fundamentals series, where we will delve deeper into specific facets of data engineering, exploring topics such as data modeling, pipelines, and best practices in data governance.

This presentation is based on the book, Data Engineering Process Fundamentals, which provides a more comprehensive guide to the topics we'll cover. You can find all the sample code and datasets used in this presentation on our popular GitHub repository Introduction to Data Engineering Process Fundamentals.

Thanks for reading! 😊 If you enjoyed this post and would like to stay updated with our latest content, don’t forget to follow us. Join our community and be the first to know about new articles, exclusive insights, and more!

• Google Developer Group
• GitHub
• Twitter
• YouTube
• BlueSky

👍 Originally published by ozkary.com

Overview Build upon your existing data engineering expertise and discover how Medallion Architecture can transform your data strategy. This...

Read Article →

From Raw Data to Governance: Refining Data with the Medallion Architecture Nov 2025

Overview

Build upon your existing data engineering expertise and discover how Medallion Architecture can transform your data strategy. This session provides a hands-on approach to implementing Medallion principles, empowering you to create a robust, scalable, and governed data platform.

We'll explore how to align data engineering processes with Medallion Architecture, identifying opportunities for optimization and improvement. By understanding the core principles and practical implementation steps, you'll learn how to optimize data pipelines, enhance data quality, and unlock valuable insights through a structured, layered approach to drive business success.

• Follow this GitHub repo during the presentation: (Give it a star)

👉 https://github.com/ozkary/data-engineering-mta-turnstile

• Read more information on my blog at:

👉 https://www.ozkary.com/2023/03/data-engineering-process-fundamentals.html

YouTube Video

Video Agenda

• Introduction to Medallion Architecture

  • Defining Medallion Architecture
  • Core Principles
  • Benefits of Medallion Architecture

• The Raw Zone

  • Understanding the purpose of the Raw Zone
  • Best practices for data ingestion and storage

• The Bronze Zone

  • Data transformation and cleansing
  • Creating a foundation for analysis

• The Silver Zone

  • Data optimization and summarization
  • Preparing data for consumption

• The Gold Zone

  • Curated data for insights and action
  • Enabling self-service analytics

• Empowering Insights

  • Data-driven decision-making
  • Accelerated Insights

• Data Governance

  • Importance of data governance in Medallion Architecture
  • Implementing data ownership and stewardship
  • Ensuring data quality and security

Why Attend:

Gain a deep understanding of Medallion Architecture and its application in modern data engineering. Learn how to optimize data pipelines, improve data quality, and unlock valuable insights. Discover practical steps to implement Medallion principles in your organization and drive data-driven decision-making.

Presentation

Introducing Medallion Architecture

Medallion architecture is a data management approach that organizes data into distinct layers based on its quality and processing level.

• Improved Data Quality: By separating data into different zones, you can focus on data quality at each stage.
• Enhanced Data Governance: Clear data ownership and lineage improve data trustworthiness.
• Accelerated Insights: Optimized data in the Silver and Gold zones enables faster query performance.
• Scalability: The layered approach can accommodate growing data volumes and complexity.
• Cost Efficiency: Optimized data storage and processing can reduce costs.

The Raw Zone: Foundation of Your Data Lake

The Raw Zone is the initial landing place for raw, unprocessed data. It serves as a historical archive of your data sources.

• Key Characteristics:
  • Unstructured or semi-structured format (e.g., CSV, JSON, Parquet)
  • Data is ingested as-is, without any cleaning or transformation
  • High volume and velocity
  • Data retention policies are crucial
• Benefits:
  • Preserves original data for potential future analysis
  • Enables data reprocessing
  • Supports data lineage and auditability

The Bronze Zone: Transforming Raw Data

The Bronze Zone is where raw data undergoes initial cleaning, structuring, and transformation. It serves as a staging area for data before moving to the Silver Zone.

• Key Characteristics:
  • Data is cleansed and standardized
  • Basic transformations are applied (e.g., data type conversions, null handling)
  • Data is structured into tables or views
  • Data quality checks are implemented
  • Data retention policies may be shorter than the Raw Zone
• Benefits:
  • Improves data quality and consistency
  • Provides a foundation for further analysis
  • Enables data exploration and discovery

The Silver Zone: A Foundation for Insights

The Silver Zone houses data that has been further refined, aggregated, and optimized for specific use cases. It serves as a bridge between the raw data and the final curated datasets.

• Key Characteristics:
  • Data is cleansed, standardized, and enriched
  • Data is structured for analytical purposes (e.g., normalized, de-normalized)
  • Data is optimized for query performance (e.g., partitioning, indexing)
  • Data is aggregated and summarized for specific use cases
• Benefits:
  • Improved query performance
  • Supports self-service analytics
  • Enables advanced analytics and machine learning
  • Reduces query costs

The Gold Zone: Your Data's Final Destination

• Definition: The Gold Zone contains the final, curated datasets ready for consumption by business users and applications. It is the pinnacle of data transformation and optimization.
• Key Characteristics:
  • Data is highly refined, aggregated, and optimized for specific use cases
  • Data is often materialized for performance
  • Data is subject to rigorous quality checks and validation
  • Data is secured and governed
• Benefits:
  • Enables rapid insights and decision-making
  • Supports self-service analytics and reporting
  • Provides a foundation for advanced analytics and machine learning
  • Reduces query latency

The Gold Zone: Empowering Insights and Actions

The Gold Zone is the final destination for data, providing a foundation for insights, analysis, and action. It houses curated, optimized datasets ready for consumption.

• Key Characteristics:
  • Data is accessible and easily consumable
  • Supports various analytical tools and platforms (BI, ML, data science)
  • Enables self-service analytics
  • Drives business decisions and actions
• Examples of Consumption Tools:
  • Business Intelligence (BI) tools (Looker, Tableau, Power BI)
  • Data science platforms (Python, R, SQL)
  • Machine learning platforms (TensorFlow, PyTorch)
  • Advanced analytics tools

Data Governance: The Cornerstone of Data Management

Data governance is the framework that defines how data is managed within an organization, while data management is the operational execution of those policies. Data Governance is essential for ensuring data quality, consistency, and security.

Key components of data governance include:

• Data Lineage: Tracking data's journey from source to consumption.
• Data Ownership: Defining who is responsible for data accuracy and usage.
• Data Stewardship: Managing data on a day-to-day basis, ensuring quality and compliance.
• Data Security: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction.
• Compliance: Adhering to industry regulations (e.g., GDPR, CCPA, HIPAA) and internal policies.

By establishing clear roles, responsibilities, and data lineage, organizations can build trust in their data, improve decision-making, and mitigate risks.

Data Transformation and Incremental Strategy

The data transformation phase is a critical stage in a data warehouse project. This phase involves several key steps, including data extraction, cleaning, loading, data type casting, use of naming conventions, and implementing incremental loads to continuously insert the new information since the last update via batch processes.

Data Lineage: Tracks the flow of data from its origin to its destination, including all the intermediate processes and transformations that it undergoes.

Data Governance : Metadata

Assigns the owner, steward and responsibilities of the data.

Summary: Leverage Medallion Architecture for Success

• Key Benefits:
  • Improved data quality
  • Enhanced governance
  • Accelerated insights
  • Scalability
  • Cost Efficiency.

We've covered a lot today, but this is just the beginning!

If you're interested in learning more about building cloud data pipelines, I encourage you to check out my book, 'Data Engineering Process Fundamentals,' part of the Data Engineering Process Fundamentals series. It provides in-depth explanations, code samples, and practical exercises to help in your learning.

Upcoming Talks:

Join us for subsequent sessions in our Data Engineering Process Fundamentals series, where we will delve deeper into specific facets of data engineering, exploring topics such as data modeling, pipelines, and best practices in data governance.

This presentation is based on the book, Data Engineering Process Fundamentals, which provides a more comprehensive guide to the topics we'll cover. You can find all the sample code and datasets used in this presentation on our popular GitHub repository Introduction to Data Engineering Process Fundamentals.

Thanks for reading! 😊 If you enjoyed this post and would like to stay updated with our latest content, don’t forget to follow us. Join our community and be the first to know about new articles, exclusive insights, and more!

• Google Developer Group
• GitHub
• Twitter
• YouTube
• BlueSky

👍 Originally published by ozkary.com

Overview Build upon your existing data engineering expertise and discover how Medallion Architecture can transform your data strategy. This...

Read Article →

From Blueprint to Build - The Design and Planning Phase in Data Engineering

Overview

The design and planning phase of a data engineering project is crucial for laying out the foundation of a successful and scalable solution. This phase ensures that the architecture is strategically aligned with business objectives, optimizes resource utilization, and mitigates potential risks.

• Follow this GitHub repo during the presentation: (Give it a star)

👉 https://github.com/ozkary/data-engineering-mta-turnstile

• Read more information on my blog at:

👉 https://www.ozkary.com/2023/03/data-engineering-process-fundamentals.html

YouTube Video

Video Agenda

In this session, we embark on the next chapter of our data journey, delving into the critical Design and Planning Phase. As we transition from discovery to design, we'll unravel the intricacies of:

System Design and Architecture:

• Understanding the foundational principles that shape a robust and scalable data system.

  Data Pipeline and Orchestration:

• Uncovering the essentials of designing an efficient data pipeline and orchestrating seamless data flows.

  Source Control and Deployment:

• Navigating the best practices for source control, versioning, and deployment strategies.

  CI/CD in Data Engineering:

• Implementing Continuous Integration and Continuous Deployment (CI/CD) practices for agility and reliability.

  Docker Container and Docker Hub:

• Harnessing the power of Docker containers and Docker Hub for containerized deployments.

  Cloud Infrastructure with IaC:

• Exploring technologies for building out cloud infrastructure using Infrastructure as Code (IaC), ensuring efficiency and consistency.

Why Join:

• Gain insights into designing scalable and efficient data systems.

• Learn best practices for cloud infrastructure and IaC.

• Discover the importance of data pipeline orchestration and source control.

• Explore the world of CI/CD in the context of data engineering.

• Unlock the potential of Docker containers for your data workflows.

Some of the technologies that we will be covering:

• Cloud Infrastructure
• Data Pipelines
• GitHub and Actions
• VSCode
• Docker and Docker Hub
• Terraform

Presentation

Data Engineering Overview

A Data Engineering Process involves executing steps to understand the problem, scope, design, and architecture for creating a solution. This enables ongoing big data analysis using analytical and visualization tools.

Topics

• Importance of Design and Planning
• System Design and Architecture
• Data Pipeline and Orchestration
• Source Control and CI/CD
• Docker Containers
• Cloud Infrastructure with IaC

Follow this project: Give a star

👉 Data Engineering Process Fundamentals

Importance of Design and Planning

The design and planning phase of a data engineering project is crucial for laying out the foundation of a successful and scalable solution. This phase ensures that the architecture is strategically aligned with business objectives, optimizes resource utilization, and mitigates potential risks.

Foundational Areas

• Designing the data pipeline and technology specifications like flows, coding language, data governance and tools
• Define the system architecture like cloud services for scalability, data platform
• Source control and deployment automation with CI/CD
• Using Docker containers for environment isolation to avoid deployment issues
• Infrastructure automation with Terraform or cloud CLI tools
• System monitor, notification and recovery

System Design and Architecture

In a system design, we need to clearly define the different technologies that should be used for each area of the solution. It includes the high-level system architecture, which defines the different components and their integration.

• The design outlines the technical solution, including system architecture, data integration, flow orchestration, storage platforms, and data processing tools. It focuses on defining technologies for each component to ensure a cohesive and efficient solution.

• A system architecture is a critical high-level design encompassing various components such as data sources, ingestion resources, workflow orchestration, storage, transformation services, continuous ingestion, validation mechanisms, and analytics tools.

Data Pipeline and Orchestration

A data pipeline is basically a workflow of tasks that can be executed in Docker containers. The execution, scheduling, managing and monitoring of the pipeline is referred to as orchestration. In order to support the operations of the pipeline and its orchestration, we need to provision a VM and data lake, and monitor cloud resources.

• This can be code-centric, leveraging languages like Python, SQL
• Or a low-code approach, utilizing tools such as Azure Data Factory, which provides a turn-key solution
• Monitor services enable us to track telemetry data to support operational requirements
• Docker Hub, GitHub can be used for the CI/CD process and deployed our code-centric solutions
• Scheduling, recovering from failures and dashboards are essentials for orchestration
• Low-code solutions , like data factory, can also be used

Source Control - CI/CD

Implementing source control practices alongside Continuous Integration and Continuous Delivery (CI/CD) pipelines is vital for facilitating agile development. This ensures efficient collaboration, change tracking, and seamless code deployment, crucial for addressing ongoing feature changes, bug fixes, and new environment deployments.

• Systems like Git facilitates effective code and configuration file management, enabling collaboration and change tracking.
• Platforms such as GitHub enhance collaboration by providing a remote repository for sharing code.
• CI involves integrating code changes into a central repository, followed by automated build and test processes to validate changes and provide feedback.
• CD automates the deployment of code builds to various environments, such as staging and production, streamlining the release process and ensuring consistency across environments.

Docker Container and Docker Hub

Docker proves invaluable for our data pipelines by providing self-contained environments with all necessary dependencies. With Docker Hub, we can effortlessly distribute pipeline images, facilitating swift and reliable provisioning of new environments.

• Docker containers streamline the deployment process by encapsulating application and dependency configurations, reducing runtime errors.
• Containerizing data pipelines ensures reliability and portability by packaging all necessary components within a single container image.
• Docker Hub serves as a centralized container registry, enabling seamless image storage and distribution for streamlined environment provisioning and scalability.

Cloud Infrastructure with IaC

Infrastructure automation is crucial for maintaining consistency, scalability, and reliability across environments. By defining infrastructure as code (IaC), organizations can efficiently provision and modify cloud resources, mitigating manual errors.

• Define infrastructure configurations as code, ensuring consistency across environments.
• Easily scale resources up or down to meet changing demands with code-defined infrastructure.
• Reduce manual errors and ensure reproducibility by automating resource provisioning and management.
• Track infrastructure changes under version control, enabling collaboration and ensuring auditability.
• Track infrastructure state, allowing for precise updates and minimizing drift between desired and actual configurations.

Summary

The design and planning phase of a data engineering project sets the stage for success. From designing the system architecture and data pipelines to implementing source control, CI/CD, Docker, and infrastructure automation with Terraform, every aspect contributes to efficient and reliable deployment. Infrastructure automation, in particular, plays a critical role by simplifying provisioning of cloud resources, ensuring consistency, and enabling scalability, ultimately leading to a robust and manageable data engineering system.

Upcoming Talks:

Join us for subsequent sessions in our Data Engineering Process Fundamentals series, where we will delve deeper into specific facets of data engineering, exploring topics such as data modeling, pipelines, and best practices in data governance.

This presentation is based on the book, Data Engineering Process Fundamentals, which provides a more comprehensive guide to the topics we'll cover. You can find all the sample code and datasets used in this presentation on our popular GitHub repository Introduction to Data Engineering Process Fundamentals.

Thanks for reading! 😊 If you enjoyed this post and would like to stay updated with our latest content, don’t forget to follow us. Join our community and be the first to know about new articles, exclusive insights, and more!

• Google Developer Group
• GitHub
• Twitter
• YouTube
• BlueSky

👍 Originally published by ozkary.com

Overview The design and planning phase of a data engineering project is crucial for laying out the foundation of a successful and scalable ...

Read Article →

From Raw Data to Roadmap: The Discovery Phase in Data Engineering Process Fundamentals

Overview

The discovery process involves identifying the problem, analyzing data sources, defining project requirements, establishing the project scope, and designing an effective architecture to address the identified challenges.

In this session, we will delve into the essential building blocks of data engineering, placing a spotlight on the discovery process. From framing the problem statement to navigating the intricacies of exploratory data analysis (EDA) using Python, VSCode, Jupyter Notebooks, and GitHub, you'll gain a solid understanding of the fundamental aspects that drive effective data engineering projects.

DevFest Series Data Engineering Process Fundamentals Series

• Follow this GitHub repo during the presentation: (Give it a star)

👉 GitHub Repo

Jupyter Notebook

👉 Jupyter Notebook

• Data engineering Series:

👉 Blog Series

👉 Data Engineering Book on Amazon

YouTube Video

Video Agenda

In this session, we will delve into the essential building blocks of data engineering, placing a spotlight on the discovery process. From framing the problem statement to navigating the intricacies of exploratory data analysis (EDA), data modeling using Python, VS Code, Jupyter Notebooks, SQL, and GitHub, you'll gain a solid understanding of the fundamental aspects that drive effective data engineering projects.

1. Introduction:

   • The "Why": We'll discuss why understanding your data upfront is crucial for success.
   • The Problem: We'll introduce a real-world problem that will guide our exploration.

2. Data Loading and Preparation:

   • Loading: We'll demonstrate how to efficiently load data from an online source directly into our workspace.
   • Structuring: We'll prepare the loaded data for analysis, making it easy to work with.

3. Exploratory Data Analysis (EDA):

   • First Look: We'll learn how to quickly generate and interpret summary statistics for our data.
   • The Story: We'll use these statistics to understand the data's characteristics and identify any red flags or anomalies.

4. Data Cleaning and Modeling:

   • Cleaning: We'll identify and handle common data issues like missing values and inconsistencies.
   • Modeling: We'll organize our data into separate tables for dimensions (descriptive attributes) and facts (measurable values).

5. Visualization and Real-World Application:

   • Bringing it to Life: We'll create charts to visualize the data and find patterns.
   • Solving the Problem: We'll apply the insights gained to address our original problem and discuss practical solutions.

Key Takeaways:

• Mastery of the foundational aspects of data engineering.
• Hands-on experience with EDA techniques, emphasizing the discovery phase.
• Appreciation for the value of a code-centric approach in the data engineering discovery process.

Upcoming Talks:

Join us for subsequent sessions in our Data Engineering Process Fundamentals series, where we will delve deeper into specific facets of data engineering, exploring topics such as data modeling, pipelines, and best practices in data governance.

This presentation is based on the book, "Data Engineering Process Fundamentals," which provides a more comprehensive guide to the topics we'll cover. You can find all the sample code and datasets used in this presentation on our popular GitHub repository.

Presentation

Data Engineering Overview

A Data Engineering Process involves executing steps to understand the problem, scope, design, and architecture for creating a solution. This enables ongoing big data analysis using analytical and visualization tools.

Topics

• Importance of the Discovery Process
• Setting the Stage - Technologies
• Exploratory Data Analysis (EDA)
• Code-Centric Approach
• Version Control
• Real-World Use Case

Follow this project: Give a star

👉 Data Engineering Process Fundamentals

Importance of the Discovery Process

The discovery process involves identifying the problem, analyzing data sources, defining project requirements, establishing the project scope, and designing an effective architecture to address the identified challenges.

• Clearly document the problem statement to understand the challenges the project aims to address.
• Make observations about the data, its structure, and sources during the discovery process.
• Define project requirements based on the observations, enabling the team to understand the scope and goals.
• Clearly outline the scope of the project, ensuring a focused and well-defined set of objectives.
• Use insights from the discovery phase to inform the design of the solution, including data architecture.
• Develop a robust project architecture that aligns with the defined requirements and scope.

Setting the Stage - Technologies

To set the stage, we need to identify and select the tools that can facilitate the analysis and documentation of the data. Here are key technologies that play a crucial role in this stage:

• Python: A versatile programming language with rich libraries for data manipulation, analysis, and scripting.

Use Cases: Data download, cleaning, exploration, and scripting for automation.

• Jupyter Notebooks: An interactive tool for creating and sharing documents containing live code, visualizations, and narrative text.

Use Cases: Exploratory data analysis, documentation, and code collaboration.

• Visual Studio Code: A lightweight, extensible code editor with powerful features for source code editing and debugging.

Use Cases: Writing and debugging code, integrating with version control systems like GitHub.

• SQL (Structured Query Language): A domain-specific language for managing and manipulating relational databases.

Use Cases: Querying databases, data extraction, and transformation.

Exploratory Data Analysis (EDA)

EDA is our go-to method for downloading, analyzing, understanding and documenting the intricacies of the datasets. It's like peeling back the layers of information to reveal the stories hidden within the data. Here's what EDA is all about:

• EDA is the process of analyzing data to identify patterns, relationships, and anomalies, guiding the project's direction.

• Python and Jupyter Notebook collaboratively empower us to download, describe, and transform data through live queries.

• Insights gained from EDA set the foundation for informed decision-making in subsequent data engineering steps.

• Code written on Jupyter Notebook can be exported and used as the starting point for components for the data pipeline and transformation services.

Code-Centric Approach

A code-centric approach, using programming languages and tools in EDA, helps us understand the coding methodology for building data structures, defining schemas, and establishing relationships. This robust understanding seamlessly guides project implementation.

• Code delves deep into data intricacies, revealing integration and transformation challenges often unclear with visual tools.

• Using code taps into Pandas and Numpy libraries, empowering robust manipulation of data frames, establishment of loading schemas, and addressing transformation needs.

• Code-centricity enables sophisticated analyses, covering aggregation, distribution, and in-depth examinations of the data.

• While visual tools have their merits, a code-centric approach excels in hands-on, detailed data exploration, uncovering subtle nuances and potential challenges.

Version Control

Using a tool like GitHub is essential for effective version control and collaboration in our discovery process. GitHub enables us to track our exploratory code and Jupyter Notebooks, fostering collaboration, documentation, and comprehensive project management. Here's how GitHub enhances our process:

• Centralized Tracking: GitHub centralizes tracking and managing our exploratory code and Jupyter Notebooks, ensuring a transparent and organized record of our data exploration.

• Sharing: Easily share code and Notebooks with team members on GitHub, fostering seamless collaboration and knowledge sharing.

• Documentation: GitHub supports Markdown, enabling comprehensive documentation of processes, findings, and insights within the same repository.

• Project Management: GitHub acts as a project management hub, facilitating CI/CD pipeline integration for smooth and automated delivery of data engineering projects.

Summary: The Power of Discovery

By mastering the discovery phase, you lay a strong foundation for successful data engineering projects. A thorough understanding of your data is essential for extracting meaningful insights.

• Understanding Your Data: The discovery phase is crucial for understanding your data's characteristics, quality, and potential.
• Exploratory Data Analysis (EDA): Use techniques to uncover patterns, trends, and anomalies.
• Data Profiling: Assess data quality, identify missing values, and understand data distributions.
• Data Cleaning: Address data inconsistencies and errors to ensure data accuracy.
• Domain Knowledge: Leverage domain expertise to guide data exploration and interpretation.
• Setting the Stage: Choose the right language and tools for efficient data exploration and analysis.

The data engineering discovery process involves defining the problem statement, gathering requirements, and determining the scope of work. It also includes a data analysis exercise utilizing Python and Jupyter Notebooks or other tools to extract valuable insights from the data. These steps collectively lay the foundation for successful data engineering endeavors.

Thanks for reading! 😊 If you enjoyed this post and would like to stay updated with our latest content, don’t forget to follow us. Join our community and be the first to know about new articles, exclusive insights, and more!

• Google Developer Group
• GitHub
• Twitter
• YouTube
• BlueSky

👍 Originally published by ozkary.com

Overview The discovery process involves identifying the problem, analyzing data sources, defining project requirements, establishing the pr...

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Discovering Machine Language a Primer Guide

Overview

Machine Learning can seem like a complex and mysterious field. This presentation aims to discover the core concepts of Machine Learning, providing a primer guide to key ideas like supervised and unsupervised learning, along with practical examples to illustrate their real-world applications. We'll also explore a GitHub repository with code examples to help you further your understanding and experimentation.

#BuildwithAI Series

• Follow this GitHub repo during the presentation: (Please star and follow the project for updates.)

👉 https://github.com/ozkary/machine-learning-engineering

YouTube Video

Video Agenda

Agenda:

1. What is Machine Learning?

   • Definition and core concepts

2. Why is Machine Learning Important?

   • Key applications and benefits

3. Types of Machine Learning

   • Supervised Learning
   • Examples: Classification & Regression
   • Unsupervised Learning
   • Examples: Clustering & Dimensionality Reduction

4. Problem Types

   • Regression: Predicting continuous values
   • Classification: Predicting categorical outcomes

5. Model Development Process

   • Understand the Problem
   • Exploratory Data Analysis (EDA)
   • Data Preprocessing
   • Feature Engineering
   • Data Splitting
   • Model Selection
   • Training & Evaluation

Presentation

What is Machine Learning

ML is a subset of AI that focuses on enabling computers to learn and improve performance on a specific task without being explicitly programmed. In essence, it's about learning from data patterns to make predictions or decisions based on it.

Core Concepts

• Learn from data
• Improve performance with more training data
• Main goal is to make predictions and decisions on new data
• Learn the relation of data + outcome to define the model
• The new data + model predicts an outcome

Why is Machine Learning Important?

ML impacts how computers solve problems. Traditional systems rely on pre-defined rules programmed by humans. This approach struggles with complexity and doesn't adapt to new information. In contrast, ML enables computers to learn directly from data, similar to how humans learn.

• Coding Rules

def heart_disease_risk_rule_based(age, overweight, diabetic):
     """
     Assesses heart disease risk based on a set of predefined rules.

     Args:
         age: Age of the individual (int).
         overweight: True if overweight, False otherwise (bool).
         diabetic: True if diabetic, False otherwise (bool).

     Returns:
         "High Risk" or "Low Risk" (str).
     """
     if age > 50 and overweight and diabetic:
         return "High Risk"
     elif age > 60 and (overweight or diabetic):
         return "High Risk"
     elif age > 40 and overweight and not diabetic:
        return "Moderate Risk"
     else:
         return "Low Risk"

• Learning from data

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score


df = pd.DataFrame(data)
# Prepare the data
X = df[['Age', 'Overweight', 'Diabetic']]  # Features
y = df['Heart Disease']  # Target

# Split data into training and testing sets
# X has the categories/features
# y has the target value
# train data is for training
# test data is for testing
# .2 means 20% of the data is used for testing 80% for training
# 42 is the seed for random shuffling

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Random Forest classifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of the model : {accuracy}")

# 70% - 80%: Often considered a reasonable starting point for many classification problems.
# 80% - 90%: Good performance for many applications.
# 90% - 95%: Very good performance. Often challenging to achieve, but possible for well-behaved problems with good data.
# > 95%: Excellent performance, potentially approaching the limits of what's possible for the problem. Be careful of overfitting if you're achieving very high accuracy.
# 100%: Usually a sign of overfitting.

👉 Jupyter Notebook

Types of ML Models - Supervised Learning

Examples

• Regression: Predicting a continuous value (e.g., house prices, stock prices).
• Classification: Predicting a category or class label (e.g., cat/dog/bird, disease/no disease).
• Model Examples: Linear Regression, Logistic Regression, Decision Trees, Random Forest.

Types of ML Models - Unsupervised Learning

Examples

• Clustering: Grouping similar data points together (e.g., group patients by symptoms, age groups)

• Association: Discovering relationships or associations between items (e.g., symptom association)

"Patients who report 'Fever' and 'Cough' are also frequently reporting 'Headache' or 'Muscle Aches'."

• Model Examples: Clustering (k-means), association (Frequent Pattern Growth)

Supervise Learning - Common Problem Types

Regression and Classification are two main problem types to solve. With Regression, we look to predict a continuous target variable like price, cost. With Classification, we look to predict a discrete target like a group or y/n class.

Problem Types

1. Regression:

   • In regression, the target variable is continuous and represents a quantity or a number.
   • Example: Predicting house prices, temperature predictions, stock prices.

2. Classification:

   • In classification, the target variable is discrete and represents a category or a class.
   • Example: spam vs. non-spam emails, predicting heart disease Y/N.

ML Model Development Process - MLOps

Developing a new ML model involves understanding the core problem, then using a data engineering process to gather, explore, and prepare the data. We then move to the ML process to split the data, select the algorithm, train and evaluate the model.

• Development Process
  • Understand the problem
  • Exploratory Data Analysis (EDA)
  • Data Preprocessing
  • Feature Engineering
  • Data Splitting
  • Model Selection
  • Train TRaining
  • Model Evaluate & Tuning
  • Deployment

MLOps is the operation process to manage the training, evaluation and deployment of your models

👉 Vehicle MSRP Regression

Machine Learning Summary

Machine learning (ML) enables computers to learn patterns from data and make predictions or decisions without explicit programming, unlike rule-based systems. ML models improve as they process more data.

• Supervised Learning:

  • Learns from labeled data (input-output pairs)
  • Regression: Predicts continuous values (e.g., house prices)
  • Classification: Predicts categories (e.g., heart disease y/n)

• Unsupervised Learning:

  • Learns from unlabeled data (only inputs) and discovers patterns and structures.
  • Clustering: Grouping similar data points (e.g., group patients by symptoms, age groups)
  • Association: Discovering relationships between items (e.g., symptoms association)

While we've explored foundational areas, numerous other exciting topics exist, such as neural networks, natural language processing, computer vision, large language models (LLMs). Visit the repository for more exploration.

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👍 Originally published by ozkary.com

Overview Machine Learning can seem like a complex and mysterious field. This presentation aims to discover the core concepts of Machine Lea...

Read Article →

Introduction to Data Lakes and Data Warehouses - Data Engineering Process Fundamentals -

Overview

In this technical presentation, we will delve into the fundamental concepts of Data Engineering, focusing on two pivotal components of modern data architecture - Data Lakes and Data Warehouses. We will explore their roles, differences, and how they collectively empower organizations to harness the true potential of their data.

• Follow this GitHub repo during the presentation: (Star the project to follow and get updates)

👉 GitHub Repo

• Data engineering Series:

👉 Blog Series

YouTube Video

Video Agenda

Agenda:

1. Introduction to Data Engineering:

2. Brief overview of the data engineering landscape and its critical role in modern data-driven organizations.

3. Operational Data

4. Understanding Data Lakes:

5. Explanation of what a data lake is and its purpose in storing vast amounts of raw and unstructured data.

6. Exploring Data Warehouses:

7. Definition of data warehouses and their role in storing structured, processed, and business-ready data.

8. Comparing Data Lakes and Data Warehouses:

9. Comparative analysis of data lakes and data warehouses, highlighting their strengths and weaknesses.

10. Discussing when to use each based on specific use cases and business needs.

11. Integration and Data Pipelines:

12. Insight into the seamless integration of data lakes and data warehouses within a data engineering pipeline.

13. Code walkthrough showcasing data movement and transformation between these two crucial components.

14. Real-world Use Cases:

15. Presentation of real-world use cases where effective use of data lakes and data warehouses led to actionable insights and business success.

16. Hands-on demonstration using Python, Jupyter Notebook and SQL to solidify the concepts discussed, providing attendees with practical insights and skills.

17. Q&A and Hands-on Session:

18. An interactive Q&A session to address any queries.

Conclusion:

This session aims to equip attendees with a strong foundation in data engineering, focusing on the pivotal role of data lakes and data warehouses. By the end of this presentation, participants will grasp how to effectively utilize these tools, enabling them to design efficient data solutions and drive informed business decisions.

Presentation

Data Engineering Overview

A Data Engineering Process involves executing steps to understand the problem, scope, design, and architecture for creating a solution. This enables ongoing big data analysis using analytical and visualization tools.

Topics

• Data Lake and Data Warehouse
• Discovery and Data Analysis
• Design and Infrastructure Planning
• Data Lake - Pipeline and Orchestration
• Data Warehouse - Design and Implementation
• Analysis and Visualization

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👉 Data Engineering Process Fundamentals

Operational Data

Operational data is often generated by applications, and it is stored in transactional relational databases like SQL Server, Oracle and NoSQL (JSON) databases like MongoDB, Firebase. This is the data that is created after an application saves a user transaction like contact information, a purchase or other activities that are available from the application.

Features:

• Application support and transactions
• Relational data structure and SQL or document structure NoSQL
• Small queries for case analysis

Not Best For:

• Reporting system
• Large queries
• Centralized Big Data system

Data Lake - Analytical Data Staging

A Data Lake is an optimized storage system for Big Data scenarios. The primary function is to store the data in its raw format without any transformation. Analytical data is the transaction data that has been extracted from a source system via a data pipeline as part of the staging data process.

Features:

• Store the data in its raw format without any transformation
• This can include structure data like CSV files, unstructured data like JSON and XML documents, or column-base data like parquet files
• Low Cost for massive storage power
• Not Designed for querying or data analysis
• It is used as external tables by most systems

Data Warehouse - Analytical Data

A Data Warehouse is a centralized storage system that stores integrated data from multiple sources. The system is designed to host and serve Big Data scenarios with lower operational cost than transaction databases, but higher costs than a Data Lake. This system host the Analytical Data that has been processed and is ready for analytical purposes.

Data Warehouse Features:

• Stores historical data in relational tables with an optimized schema, which enables the data analysis process
• Provides SQL support to query the data
• It can integrate external resources like CSV and parquet files that are stored on Data Lakes as external tables
• The system is designed to host and serve Big Data scenarios. It is not meant to be used as a transactional system
• Storage is more expensive
• Offloads archived data to Data Lakes

Discovery - Data Analysis

During the discovery phase of a Data Engineering Process, we look to identify and clearly document a problem statement, which helps us have an understanding of what we are trying to solve. We also look at our analytical approach to make observations about at the data, its structure and source. This leads us into defining the requirements for the project, so we can define the scope, design and architecture of the solution.

• Download sample data files
• Run experiments to make observations
• Write Python scripts using VS Code or Jupyter Notebooks
• Transform the data with Pandas
• Make charts with Plotly
• Document the requirements

Design and Planning

The design and planning phase of a data engineering project is crucial for laying out the foundation of a successful system. It involves defining the system architecture, designing data pipelines, implementing source control practices, ensuring continuous integration and deployment (CI/CD), and leveraging tools like Docker and Terraform for infrastructure automation.

• Use GitHub for code repo and for CI/CD actions
• Use Terraform is an Infrastructure as Code (IaC) tool that enables us to manage cloud resources across multiple cloud providers
• Use Docker containers to run the code and manage its dependencies

Data Lake - Pipeline and Orchestration

A data pipeline is basically a workflow of tasks that can be executed in Docker containers. The execution, scheduling, managing and monitoring of the pipeline is referred to as orchestration. In order to support the operations of the pipeline and its orchestration, we need to provision a VM and data lake, and monitor cloud resources.

• This can be code-centric, leveraging languages like Python
• Or a low-code approach, utilizing tools such as Azure Data Factory, which provides a turn-key solution
• Monitor services enable us to track telemetry data
• Docker Hub, GitHub can be used for the CI/CD process

Data Warehouse - Design and Implementation

In the design phase, we lay the groundwork by defining the database system, schema model, and technology stack required to support the data warehouse’s implementation and operations. In the implementation phase, we focus on converting conceptual data models into a functional system. By creating concrete structures like dimension and fact tables and performing data transformation tasks, including data cleansing, integration, and scheduled batch loading, we ensure that raw data is processed and unified for analysis. Create a repeatable and extendable process.

Data Warehouse - Data Analysis

Data analysis is the practice of exploring data and understanding its meaning. It involves activities that can help us achieve a specific goal, such as identifying data dimensions and measures, as well as data analysis to identify outliers, trends, and distributions.

• We can accomplish these activities by writing code using Python and Pandas, SQL, Visual Studio Code or Jupyter Notebooks.
• What's more, we can use libraries, such as Plotly, to generate some visuals to further analyze data and create prototypes.

Data Analysis and Visualization

Data visualization is a powerful tool that takes the insights derived from data analysis and presents them in a visual format. While tables with numbers on a report provide raw information, visualizations allow us to grasp complex relationships and trends at a glance.

• Dashboards, in particular, bring together various visual components like charts, graphs, and scorecards into a unified interface that can help us tell a story
• Use tools like PowerBI, Looker, Tableau to model the data and create enterprise level visualizations

Conclusion

Both data lakes and data warehouses are essential components of a data engineering project. The primary function of a data lake is to store large amounts of operational data in its raw format, serving as a staging area for analytical processes. In contrast, a data warehouse acts as a centralized repository for information, enabling engineers to transform, process, and store extensive data. This allows the analytical team to utilize coding languages like Python and tools such as Jupyter Notebooks, as well as low-code platforms like Looker Studio and Power BI, to create enterprise-quality dashboards for the organization.

We've covered a lot today, but this is just the beginning!

If you're interested in learning more about building cloud data pipelines, I encourage you to check out my book, 'Data Engineering Process Fundamentals,' part of the Data Engineering Process Fundamentals series. It provides in-depth explanations, code samples, and practical exercises to help in your learning.

Amazon

Thanks for reading.

Send question or comment at Twitter @ozkary 👍 Originally published by ozkary.com

Overview In this technical presentation, we will delve into the fundamental concepts of Data Engineering, focusing on two pivotal component...

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