Solid Foundations for Data Engineering (SDF)

Motivation of SFDE

I love documenting the things that like wording, filming. I am also writing personal blogs from many years ago, and bringing things related to data engineering, software development onto the blogs.

But for summarizing years of experiences I have been working with, the book is the well-known format of writing that I can document and introduce to others or myself.

How to package knowledge, let’s take a look at this picture in blow

graph LR
    subgraph Info
        direction TB
        I1((Info 1))
        I2((Info 2))
        I3((Info 3))
        I4((Info 4))
        I5((Info 5))
        I6((Info 6))
        I7((Info 7))
    end

    subgraph Knowledge
        direction TB
        K1[Knowledge 1]
        K2[Knowledge 2]
        K3[Knowledge 3]
        K4[Knowledge 4]
    end

    subgraph Insight
        direction TB
        S1>Insight 1]
        S2>Insight 2]
    end

    I1 -->|Process| K1
    I2 -->|Process| K1
    I3 -->|Process| K1
    I4 -->|Process| K4
    I5 -->|Process| K2
    I7 -->|Process| K4
    I6 -->|Process| K2
    I1 -->|Process| K3
    I2 -->|Process| K1
    I3 -->|Process| K2
    K1 -->|Analyze| S1
    K2 -->|Analyze| S1
    K3 -->|Analyze| S1
    K4 -->|Analyze| S1
    K1 -->|Analyze| S2
    K4 -->|Analyze| S2

Why I created this book ?

I’d felt paint because I was not able to understand fundamental concepts, I wanted to give my notes of journal to others and share them the same way that how I learned and discovered the data engineering.

I’ve been playing with several roles and experiences for designing and implementing data applications, data platforms to resolve data business problems. With different roles and perspective, I make this book as assets for the works that I have.

From that, I have decided to consolidate knowledge an experience into single source of documentation, developers and contributors can contribute your thought and working into this.

A side part, I mentored and coached for personal class, company academy and realized that mentee and student they don’t have enough foundations for learning the new things, there are misunderstanding, confusing and misleading the data knowledge.

This document is intended to be used for daily operations tasks that require you to revise your mind. It is not replaceable for google source or other comprehensive solutions, but this book is the base for deep enough to build your foundations.

Why Learning Foundations and Fundamental Knowledge ?

Understanding the foundations and fundamental principles in any field, including data engineering, is essential for several reasons:

  • Timeless Knowledge: Foundational concepts and principles often remain unchanged over time, even as specific tools and technologies evolve. By mastering these core ideas, you build a solid base of knowledge that will serve you well throughout your career, regardless of the latest trends or innovations.

  • Adaptability: With a strong understanding of fundamental principles, you can adapt more easily to new tools and technologies. When you grasp the underlying concepts, learning and applying new methodologies becomes simpler and more intuitive.

  • Problem-Solving: Deep knowledge of foundational principles enhances your problem-solving skills. You can approach complex challenges with a better understanding of the core issues, leading to more effective and efficient solutions.

  • Better Decision Making: A thorough understanding of the basics allows you to make more informed decisions. Whether you’re designing a new system or troubleshooting an existing one, knowing the fundamentals helps you evaluate options and choose the best course of action.

  • Enhanced Communication: Knowledge of fundamental concepts helps you communicate more effectively with colleagues and stakeholders. It ensures that everyone is on the same page, reducing misunderstandings and fostering better collaboration.

  • Continuous Learning: The field of data engineering is constantly evolving, but the foundational principles provide a stable base from which to grow. As you encounter new information and technologies, you can build on your existing knowledge rather than starting from scratch.

  • Long-Term Success: Investing time in learning the fundamentals pays off in the long run. It prepares you for a successful career by equipping you with the skills and knowledge to handle future advancements and challenges.

By focusing on the core principles of data engineering, you ensure that your knowledge is both deep and versatile, giving you a strong advantage in a rapidly changing field

What is this Data Engineering uses for?

  • Let you in the iteration of data development and interact with data engineering problems.
  • Providing answers and deep insights into data engineering and data infrastructure, with a focus on best practices.
  • Serving as a guide to help you navigate the data landscape and make informed decisions.
  • Help to become a Data Engineer, Data Architect, and Data Leader and doing a personal project as well as daily works.
  • Please notes: this is not a tutorial for any specific tools, or services, this is giving you the problems and how you interact with them.

Welcome to “Data Foundations and Advanced Practices.” This book is designed to guide you through the essential concepts and advanced techniques in data processing, management, and optimization. Whether you are a data engineer, data scientist, or anyone interested in the data domain, this book will provide you with the knowledge and tools needed to excel in your field.

In Part 1: Data For Foundations

You will find Streaming and Batching Data Processing in Chapter 1 where, you will learn the fundamental differences and use cases for streaming and batching data processing. We will explore various tools and frameworks that facilitate these processes, ensuring you can handle real-time and bulk data efficiently.

In Chapter 2, after data is landed and resided in Data Warehouse, Lake, One Big Table, by discovering the architectures and best practices for setting up data warehouses, data lakes, and the concept of the One Big Table, why the revolutionaries of streaming and batching data processing been changed to delta lake.

Step further, the module will help you understand how to store and organize large volumes of data effectively. Moving to Modeling data and shaping the structure oif data by modeling techniques in Chapter 3 of Dimension Data Modeling focuses on dimension data modeling, a critical technique for organizing data into dimensions for easier analysis and reporting.

You will learn how to create and manage dimension tables and their relationships. After building on the concepts of dimension modeling, this Chapter 4 delves into fact data modeling. Learn how to create fact tables that capture the metrics and measurements critical for business intelligence and analytics.

An essential part which ensure data always well-merged into production that is mentioned in Chapter 5 Data Quality Dimensions ensure your data is accurate, complete, and reliable with this module on data quality dimensions. We will discuss various dimensions of data quality and strategies to maintain high-quality data standards.

In Part 2: Data In Advanced

Chapter 6 mentions Distributed Systems, Applying Data Area and Explore the world of distributed systems and how they apply to data processing. This module covers the architecture and design principles of distributed data systems, ensuring scalability and reliability.

Focus on Foundation and Specification of Warehouse and data pipeline, in Chapter 7 of Data Pipeline Building Spec design, we learn how to design and build robust data pipelines. This module provides a comprehensive guide to specifying, developing, and maintaining data pipelines that can handle complex data workflows.

After all there are 2 key operation concepts have discussed in Chapter 8 that Maintaining Data Warehouse and Pipeline, focusing on the best practices for maintaining your data warehouse and pipelines. You will learn about monitoring, troubleshooting, and optimizing your data systems to ensure continuous performance and reliability.

Chapter 9 is a side part of Maintenance, the Optimizing Data Warehouse and Pipeline takes your data systems to the next level with advanced optimization techniques. This module covers performance tuning, resource management, and other strategies to enhance the efficiency of your data warehouse and pipelines.

Part 3: Data For Management

With Chapter 10 Understand the importance of auditing and governance in data systems. This module covers the principles and practices for ensuring data compliance, security, and proper management throughout the data lifecycle. Finally, learn how to assess the impact of data, conduct thorough investigations, and visualize data effectively.

Finally, the Chapter 11 module provides the tools and techniques for deriving meaningful insights and presenting data in a compelling manner.

Embark on this journey to master the foundations and advanced practices of data management. Each module is crafted to build your expertise and help you become proficient in handling data challenges. Let’s dive in!

How To Contribute

If you have some cool links or topics for the Book, please become a contributor. Ask me via Email or YouTube or Twitter) If you like this book, you can support me via Paypal or buy my digital product at Store

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About me

Shorting about Long Bui - longdatadevlog

As Data Engineer, with over the years of experience in Data Engineering, ETL Architect, Data Warehouse Design & Implementation, Data Management and Ingestion & Migration projects.I experienced in providing high quality technology solutions for data pipeline design and featuring in detail for development, integration, test, maintenance, operation. Collaborating with Enterprise level to integrate solutions to others at a high level.

As Data Architect, I had played in the game of building the data pipeline from end to end includes Data Migration, Data Integration, Data Warehouse, Lake-house Operation, Data Mart, Data Operation, collaborated with Data Engineering Team and Business Operations Team, and others stakeholders to resolve the painting and support for business decision making.

As Data Consultant and Freelancer, Coaching role, I was supporting the company and Leader of the Data team to design the data warehouse as well as migrate data from System to System; modernized data platforms and cloud applications.

I’m family person, cozy working style - solo. Currently data engineer, data consultant and technical writer over the many years of experience in data engineer.

To learn more about my journey, accomplishments, and projects, visit my HomePage and stay connect me on social media using there links.