The data engineering space is evolving. Here are the resources I collected for practical data engineering resource.
Last Updated: 2023–04–02
Books
- The Master Book On Distributed System: Designing Data-Intensive Application
- The SQL Fundamental Book: T-SQL Querying (Developer Reference)
- OLAP/Data Warehouse Must Read: The Data Warehouse Toolkit (The Definitive Guide to Dimensional Modeling, 3rd Edition)
- In-depth on Python: Fluent Python: Clear, Concise, and Effective Programming 2nd Edition
- Learning From the Creator: Spark: The Definitive Guide: Big Data Processing Made Simple
- Workflow Management Tool: Data Pipelines with Apache Airflow
- All You Need to Know About Streaming Foundation: Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing
- Understand Data Science: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Data Communication: Storytelling with Data: A Data Visualization Guide for Business Professionals
- Gain Knowledge on Cloud Infra: Kubernetes in Action
- The Philosophy of Data Engineering: Fundamentals of Data Engineering: Plan and Build Robust Data Systems
- Data Mesh: Delivering Data-Driven Value at Scale
The Essential Reading List for Data Engineers: 10 Classic Books You Can’t Miss
Discover the Essential Reading List for Data Engineers: 10 Classic Books You Can’t Miss. While many free online resources are available, they often lack the depth and context needed to truly master the field. In this article, I will share ten classic books that cover everything from fundamental technical skills like Python and SQL, to more advanced topics like Apache Spark, Apache Flink, Apache Beam, Apache Airflow, Kubernetes, distributed systems, and dimensional modeling.
Data Engineering Space Leader (who to follow)
Practical Data Engineering Framework
Data Processing
- Apache Spark: Unified engine for large-scale data analytics
- Apache Flink: Stateful Computations over Data Streams
- Apache Beam: The easiest way to do batch and streaming data processing.
Deep Dive into Handling Apache Spark Data Skew
“Why my Spark job is running slow?” is an inevitable question. We will cover how to identify Spark data skew and how to handle data skew with different options, including key salting
Workflow Orchestration
Here Is What I Learned Using Apache Airflow over 6 Years
Apache Airflow is undoubtedly the most popular open-source project for data engineering for years. It gains popularity at the right time with The Rise Of Data Engineer. Today, I want to share my journey with Airflow and what I learned over 6 years.
Is Apache Airflow Due for Replacement? The First Impression Of mage-ai
Airflow has been widespread for years. Is Apache Airflow due for a replacement? mage-ai is the new ETL tool for data engineers to check out as a substitution. I have taken a first impression of mage-ai and will share my thoughts.
5 Fantastic Data Pipeline Orchestration Tools For R
Many modern data orchestration projects like Apache Airflow and Luigi are Python-based. Let’s explore the popular data pipeline orchestration options for R.
OLAP Query
- Druid: a high-performance, real-time analytics database that delivers sub-second queries on streaming and batch data at scale and under load.
- Trino: a query engine that runs at ludicrous speed
- ClickHouse: a column-oriented database that enables its users to generate powerful analytics, using SQL queries, in real-time.
Awesome Blogs
Classic Articles
- Classic Streaming Foundation (1): Streaming 101: The world beyond batch
- Classic Streaming Foundation (2): Streaming 102: The world beyond batch
- Spark Tuning Foundation (1): How-to: Tune Your Apache Spark Jobs (Part 1)
- Spark Tuning Foundation (2): How-to: Tune Your Apache Spark Jobs (Part 2)
- Airflow Scheduler Demystify: Airflow Scheduler 101
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