Tech Guides - ETL

Introduction In the data-driven world of today, data engineering has emerged as one of the most promising and rewarding careers in the tech industry. As businesses place greater emphasis on data-driven decision-making, the need for skilled data engineers is surging. In this article, we’ll explore why data engineering is a lucrative career, why you should consider pursuing it, and the essential skills required to get started. Additionally, we’ll suggest some in-house tech books that can guide you on your journey to becoming a successful data engineer. What Does a Data Engineer Do? A data engineer is responsible for building, maintaining, and optimizing data pipelines and architectures that ensure data is easily accessible for analysts and data scientists. Their key duties include: Designing and constructing scalable systems to collect, store, and transform large datasets. Ensuring data reliability, security, and efficiency. Collaborating with data scientists and analysts to understand their data needs. Managing data workflows and automating data collection processes. Developing systems that enable businesses to store and analyze data in a structured manner. They essentially create the infrastructure that allows businesses to harness data for insights, machine learning models, and decision-making. If a company needs to process huge amounts of data—whether for improving user experience, predicting trends, or optimizing operations—a data engineer makes that possible. Why Data Engineering is a Lucrative Career: Key Statistics (source) Data Growth: The global datasphere is projected to reach 491 zettabytes by 2027, with an estimated 175 zettabytes expected by 2025. This exponential growth emphasizes the critical role of data engineers in managing and processing vast amounts of information.  Financial Impact of Data Quality: Poor data quality costs global businesses approximately $15 million annually, highlighting the importance of effective data engineering in ensuring high-quality data access and usability. Salary Projections: By 2026, the median salary for data engineers in the U.S. is projected to reach around $170,000, indicating a lucrative career path due to high demand and limited supply of qualified professionals. Talent Gap: The anticipated talent gap in data engineering is expected to reach 2.9 million job vacancies globally, underscoring the urgent need for skilled data engineers. Market Growth: The global big data market is expected to grow significantly, projected to reach $274.3 billion by 2028, with a compound annual growth rate (CAGR) of 23.8% from 2023 to 2028 (source). Essential Skills Required to Get Started in Data Engineering To embark on a successful career in data engineering, you’ll need to acquire a combination of technical skills and domain knowledge. Here are the key skills you should focus on, along with suggested in-house tech books to help you master them: 1. Programming Languages Mastering languages like Python or Java is essential for data engineers to build, automate, and maintain scalable data pipelines.Book: Data Engineering with Python Product Information: This book is a comprehensive introduction to building data pipelines, that will have you moving and transforming data in no time. You'll learn how to build data pipelines, transform and clean data, and deliver it to provide value to users. You will learn to deploy production data pipelines that include logging, monitoring, and version control. Author: Paul Crickard Book: Data Engineering with Scala and Spark Product Information: Learn new techniques to ingest, transform, merge, and deliver trusted data to downstream users using modern cloud data architectures and Scala, and learn end-to-end data engineering that will make you the most valuable asset on your data team. Authors: Eric Tome, Rupam Bhattacharjee, David Radford 2. SQL and Database Management Strong SQL skills enable efficient querying, storage, and retrieval of structured data, making database management crucial for optimizing data workflows.Book: SQL Query Design Patterns and Best Practices Product Information: SQL Query Design Patterns and Best Practices is a practical guide to making queries more efficient and maintainable. This book will help you improve your skills for writing complex queries, formatting, optimizing common query issues, and applying new techniques such as windowing and time-series functionality. Author: Steve Hughes, Dennis Neer, Dr. Ram Babu Singh, Shabbir H. Mala, Leslie Andrews, Chi Zhang Book: Mastering MongoDB 7.0 Product Information: Explore the full potential of MongoDB 7.0 with this comprehensive guide that offers powerful techniques for efficient data manipulation, application integration, and security. This intermediate-to-advanced level book empowers you to harness the power of the latest version of MongoDB. Author: Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer 3. Big Data Technologies Knowledge of Hadoop, Spark, and other Big Data tools helps in handling and processing massive datasets efficiently, which is critical for scalability.Book: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Product Information: Discover the roadblocks you may face in data engineering and keep up with the latest trends such as Delta Lake. This book will help you learn how to build data pipelines that can auto-adjust to changes. Using practical examples, you will implement a solid data engineering platform that will streamline data science, ML, and AI tasks. Author: Manoj Kukreja Book: Mastering Hadoop 3 Product Information: This is a comprehensive guide to understand advanced concepts of Hadoop ecosystem. You will learn how Hadoop works internally, and build solutions to some of real world use cases. Finally, you will have a solid understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable Big Data pipeline. Author: Chanchal Singh, Manish Kumar 4. Data Warehousing and ETL Expertise in ETL processes and data warehousing ensures clean, organized data is available for analysis, improving data accessibility and decision-making.Book: Data Modeling with Snowflake Product Information: Modeling guides are often steeped in theory and formal language. The innovative approach taken in Data Modeling with Snowflake combines practical modeling concepts with Snowflake best practices and unique features, allowing you to create efficient designs that leverage the power of the Data Cloud. Author: Serge Gershkovich  Book: Data Exploration and Preparation with BigQuery Product Information: Data Exploration and Preparation with BigQuery is a comprehensive guide to working with data preparation tools and strategies using BigQuery as a modern data warehouse solution. Through hands-on exercises and projects, you’ll leverage your BigQuery proficiency to overcome common challenges faced by data analysts. Author: Mike Kahn  5. Cloud Computing Proficiency in cloud platforms like AWS, GCP, or Azure is vital for modern data engineering, enabling scalable storage, processing, and real-time data management.Book: Azure Data Factory Cookbook - Second Edition Product Information: T With the help of well-structured and practical recipes, this book will teach you how to integrate data from the cloud and on-premises. You’ll learn how to transform, clean, and consolidate data into a single data platform and get to grips with ADF Author: Dmitry Anoshin ,Dmitry Foshin , Tonya Chernyh, Xenia Ireton Book: Data Engineering with Google Cloud Platform - Second Edition Product Information: This book will help you delve into data governance on Google Cloud. Moreover, you’ll also cover the latest technological advancements in the domain and be able to build and deploy data pipelines confidently. Author: Adi Wijaya 6. Data Governance and Security Understanding data governance and security protocols ensures that data is managed legally and securely, protecting sensitive information and maintaining compliance.Book: Data Governance Handbook Product Information: This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You’ll also find useful insights into how to implement data governance initiatives. Author: Wendy S. Batchelder Conclusion Data engineering is not only a lucrative career but also a field full of opportunities for growth and specialization. With the right skills and continuous learning, you can position yourself for success in this dynamic and rewarding profession. If you’re ready to embark on your data engineering journey, the in-house tech books mentioned above are excellent resources to help you get started. Whether you’re just beginning or looking to advance your career, now is the perfect time to dive into the world of data engineering.