The only data engineering roadmap you’ll need to learn about is the ideas, instruments, and methods involved in gathering, storing, transforming, analyzing, and modeling data.
Data engineering refers to the process of creating and maintaining systems and structures that collect, store, and transform data into a format that data scientists, analysts, and business stakeholders can easily access and analyze. This course will help you become an expert in a range of concepts and resources, allowing you to construct and manage a variety of data pipelines.
1. Containerization and Infrastructure as Code
Containerization allows developers to combine dependencies and software into portable, lightweight containers that perform consistently in a range of environments. In contrast, the “Infrastructure as Code” method enables developers to construct, version, and automate cloud infrastructure using code.
In the first level, you’ll learn about Docker containers, PostgreSQL databases, and SQL syntax. You’ll learn how to utilize Docker to start a database server locally and create a data pipeline. You will also learn about Terraform and Google Cloud Provider (GCP). Terraform can help you deploy your databases, frameworks, and tools to the cloud.
2. Workflow Orchestration
Data intake, cleansing, transformation, and analysis are just a few of the processing steps that workflow orchestration controls and automates. It is a more scalable, dependable, and effective method of operation.
You will get familiar with data orchestration solutions such as Airflow, Mage, or Prefect in the second stage. They are all open source and have many necessary capabilities for monitoring, controlling, implementing, and carrying out data pipelines. You will discover how to use Docker to set up Prefect and how to create an ETL pipeline with BigQuery APIs, Postgres, and Google Cloud Storage (GCS).
Examine the 5 Airflow Data Orchestration Alternatives and select the one that best suits your needs.
3. Data Warehousing
Large volumes of data from several sources are gathered, stored, and managed in a centralized repository through the process of data warehousing, which facilitates analysis and the extraction of insightful information.
You will get comprehensive knowledge about either the local Postgres or the cloud-based BigQuery data warehouse in the third level. Along with learning about BigQuery’s best practices, you will also gain an understanding of partitioning and clustering principles. Additionally, BigQuery offers machine learning integration, which enables hyperparameter tweaking, feature preprocessing, model deployment, and model training on massive datasets. It is comparable to machine learning SQL.
4. Analytical Engineer
Analytics engineering is the core focus of business intelligence and data science teams, with the design, construction, and maintenance of data models and analytical pipelines being particularly important.
The fourth phase will show you how to use the Data Build Tool (dbt) to build an analytical pipeline from an existing data warehouse, such as BigQuery or PostgreSQL. You’ll learn about fundamental concepts such as data modeling and the distinctions between ETL and ELT. Additionally, advanced DBT features like as incremental models, tags, hooks, and snapshots will be discussed.
Finally, this course will show you how to use visualization tools like Metabase and Google Data Studio to develop interactive dashboards and data analytics reports.
5. Batch Processing
Batch processing is a data engineering approach that includes processing huge amounts of data in batches (every minute, hour, or even day), as opposed to real-time or near-real-time.
In the fifth part of your learning journey, you will learn about batch processing with Apache Spark. You’ll learn how to install it on different operating systems, use Spark SQL and DataFrames, prepare data, conduct SQL operations, and grasp Spark internals. Towards the conclusion of this stage, you will also learn how to launch Spark instances in the cloud and interface them with the data warehouse BigQuery.
6. Streaming
Streaming is the collection, processing, and analysis of data in real-time or near real-time. Unlike typical batch processing, which collects and processes data at regular intervals, streaming data processing enables continuous analysis of the most recent information.
In the sixth stage, you’ll learn about data streaming using Apache Kafka. Begin with the fundamentals, then on to integration with Confluent Cloud and real applications for producers and consumers. You will also need to learn about stream joins, testing, windowing, and how to utilize Kafka, SQL DB, and Connect.
If you want to learn more about different tools for data engineering processes, check out 14 Essential Data Engineering Tools to Use in 2024.
7. Project: Build an end-to-end Data Pipeline
In the last stage, you will apply all of the ideas and tools you acquired in earlier levels to develop a complete end-to-end data engineering project. This will entail constructing a pipeline for processing the data, storing it in a data lake, moving the processed data from the data lake to a data warehouse, converting the data in the data warehouse, and preparing it for the dashboard. Finally, you’ll create a dashboard to graphically show the data.
Conclusion
All of the steps described in this article may be found in the Data Engineering ZoomCamp. This ZoomCamp is made up of several modules, each including tutorials, videos, questions, and projects to help you understand and construct data pipelines.
In this data engineering roadmap, we learned about the phases involved in learning, building, and executing data pipelines for data processing, analysis, and modeling. We’ve also learned about both cloud and local technologies. You may develop anything locally or in the cloud for convenience of usage. I propose using the cloud because most organizations prefer it and want you to acquire expertise with cloud platforms like GCP.
