Why this course?

Description

Apache Druid for Data Engineers (Hands-On)

Modern data-driven organizations need real-time insights from massive streams of events, logs, and metrics. Traditional data warehouses and batch processing systems struggle to deliver sub-second query performance on high-volume, high-velocity data. This is where Apache Druid comes in.

Apache Druid is a high-performance real-time analytics database widely adopted by companies like Netflix, Airbnb, Lyft, and Cisco for powering interactive dashboards, anomaly detection, log analytics, and user-facing applications. Designed for speed and scalability, Druid combines the best of OLAP databases, time-series stores, and search systems.

This course, Apache Druid for Data Engineers (Hands-On), is a step-by-step, practical guide that takes you from installation to real-world use cases. You’ll learn how to install Druid on both Linux and Windows (via Docker), explore its architecture, storage design, and segment structure, and practice loading and querying data from local files, URIs, and Kafka streams. You’ll also gain clarity on where Druid fits in the modern big data stack by comparing it with Redshift, BigQuery, and Elasticsearch.

What You’ll Learn

By the end of this course, you will be able to:

• Understand the fundamentals of real-time analytics databases and why Apache Druid is unique.
• Explore key features, technology stack, and use cases of Druid.
• Install Apache Druid on a Linux environment and on Windows using Docker Desktop.
• Navigate the Druid web console to load, query, and manage data interactively.
• Understand the architecture of Druid, including servers, services, and external dependencies.
• Learn how Druid organizes data with datasources, segments, and identifiers.
• Load data into Druid from local files, URIs, and real-time Kafka streams.
• Run queries and explain plans, aggregate data with rollups, and optimize query performance.
• Compare Druid with data warehouses (Redshift, BigQuery), search systems (Elasticsearch), and time-series databases.
• Answer common FAQs around deployment, memory, compute, and integration with other tools.

 

What will students learn in your course?

• Understand the fundamentals of real-time analytics databases and why Apache Druid is unique.
• Explore key features, technology stack, and use cases of Druid.
• Install Apache Druid on a Linux environment and on Windows using Docker Desktop.
• Navigate the Druid web console to load, query, and manage data interactively.
• Understand the architecture of Druid, including servers, services, and external dependencies.
• Learn how Druid organizes data with datasources, segments, and identifiers.
• Load data into Druid from local files, URIs, and real-time Kafka streams.
• Run queries and explain plans, aggregate data with rollups, and optimize query performance.
• Compare Druid with data warehouses (Redshift, BigQuery), search systems (Elasticsearch), and time-series databases.
• Answer common FAQs around deployment, memory, compute, and integration with other tools.

 

What are the requirements or prerequisites for taking your course?

• Basic understanding of databases – familiarity with concepts like tables, queries, and indexing will help.
• Knowledge of SQL – since querying data in Druid often uses SQL-like syntax.
• Basic Linux command-line skills – useful for installation and managing services.
• Familiarity with Docker (optional) – helpful for the Windows setup section, but step-by-step guidance is provided.
• Some exposure to Big Data or Analytics tools (optional) – knowledge of tools like Kafka, Spark, or data warehouses will give extra context, but it’s not mandatory.
• Eagerness to learn real-time analytics – no prior experience with Apache Druid is required; the course starts from the basics.

 

Who is this course for?

• Data Engineers & Big Data Developers who want to master real-time analytics databases.
• Data Analysts & BI Professionals looking to build sub-second interactive dashboards on streaming data.
• Software Engineers integrating analytics into user-facing applications.
• Students & Enthusiasts who want to learn how modern analytics systems like Druid power platforms at Netflix, Airbnb, and Lyft.