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Data engineering refers to the process of creating and maintaining structures and systems that collect, store, and transform data into a format that can be easily analyzed and used by data scientists, analysts, and business stakeholders. This roadmap will guide you to master various concepts and tools, allowing you to effectively create and run different types of data pipelines.
Containerization allows developers to package their applications and dependencies into lightweight, portable containers that can run consistently across different environments. Infrastructure as code, on the other hand, is the practice of managing and provisioning infrastructure through code, allowing developers to define, version, and automate cloud infrastructure.
In the first step, you will learn the basics of SQL syntax, Docker containers, and the Postgres database. You will learn how to start a database server using Docker locally, as well as how to create a data pipeline in Docker. Additionally, you will develop an understanding of Google Cloud Provider (GCP) and Terraform. You'll find Terraform especially useful when deploying your tools, databases, and frameworks in the cloud.
Workflow orchestration manages and automates the flow of data through various processing stages, such as data ingestion, cleansing, transformation, and analysis. It is a more efficient, reliable and scalable way of doing things.
In the second step, you will learn about data orchestration tools like Airflow, Mage or Prefect. They are all open source and come with multiple essential features to observe, manage, deploy and execute the data pipeline. You'll learn how to configure Prefect using Docker and create an ETL pipeline using Postgres, Google Cloud Storage (GCS), and BigQuery APIs.
Check out 5 airflow alternatives for data orchestration and choose the one that works best for you.
Data warehousing is the process of collecting, storing and managing large amounts of data from various sources in a centralized repository, making it easier to analyze and extract valuable information.
In the third step, you will learn all about Postgres (on-premises) or BigQuery (cloud) data warehouse. You'll learn about the concepts of partitioning and clustering, and delve into BigQuery best practices. BigQuery also provides machine learning integration where you can train models with large data, hyperparameter tuning, feature preprocessing, and model deployment. It's like SQL for machine learning.
Analytical engineering is a specialized discipline that focuses on the design, development, and maintenance of data models and analytical pipelines for business intelligence and data science teams.
In the fourth step, you will learn how to create an analytical pipeline using dbt (Data Build Tool) with an existing data warehouse, such as BigQuery or PostgreSQL. You will gain an understanding of key concepts such as ETL vs ELT as well as data modeling. You will also learn advanced dbt features such as incremental models, tags, hooks, and snapshots.
In the end, you'll learn how to use visualization tools like Google Data Studio and Metabase to create interactive dashboards and data analytics reports.
Batch processing is a data engineering technique that involves processing large volumes of data in batches (every minute, hour, or even days), rather than processing data in real-time or near real-time.
In the fifth step of your learning journey, you will be introduced to batch processing with Apache Spark. You will learn how to install it on various operating systems, work with Spark SQL and DataFrames, prepare data, perform SQL operations, and understand the internals of Spark. Towards the end of this step, you will also learn how to launch Spark instances in the cloud and integrate them with the BigQuery data warehouse.
Streaming refers to the collection, processing, and analysis of data in real time or near real time. Unlike traditional batch processing, where data is collected and processed at regular intervals, streaming data processing allows for continuous analysis of the most up-to-date information.
In the sixth step, you will learn about streaming data with Apache Kafka. Start with the basics and then dive into integration with Confluent Cloud and practical applications involving producers and consumers. Additionally, you will need to learn about stream joining, testing, windowing, and using Kafka ksqldb & Connect.
If you want to explore different tools for various data engineering processes, you can check out 14 Essential Data Engineering Tools to Use in 2024.
In the final step, you will use all the concepts and tools you learned in the previous steps to create a comprehensive end-to-end data engineering project. This will involve building a pipeline to process the data, storing the data in a data lake, creating a pipeline to transfer the processed data from the data lake to a data warehouse, transforming the data in the data warehouse, and preparing the data. for the panel. . Finally, you'll create a dashboard that visually presents the data.
All the steps mentioned in this guide can be found in Data Engineering ZoomCamp. This ZoomCamp consists of several modules, each containing tutorials, videos, questions, and projects to help you learn and create data pipelines.
In this data engineering roadmap, we have learned the various steps required to learn, build, and run data pipelines for data processing, analysis, and modeling. We have also learned as much about cloud applications and tools as we have about on-premises tools. You can choose to build everything locally or use the cloud for ease of use. I would recommend using the cloud as most companies prefer it and want you to gain experience in cloud platforms like GCP.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
