Data processing and analysis

Learn to build streaming pipelines with Apache Kafka and Flink, create data lakes on AWS, run ML workflows on Spark, and integrate LLM models into...

Study Apache Spark and PySpark for big data processing. Practical assignments will help you acquire key skills of a data engineer.

Analytics Engineering is the foundation of Data Science and artificial intelligence. This approach represents a dynamic combination of data engineering and...

Semantic search is one of the most practical ways to apply generative AI in real-world data processing projects. In this course, we go beyond...

Modern data platforms need the flexibility of data lakes and the reliability of warehouses. Apache Iceberg combines both approaches. In this course, you will...

This practical course starts with the basics and step by step guides you to building real orchestration scenarios - from task retry executions to...

Azure is becoming an increasingly popular platform for companies using the Microsoft365 ecosystem. If you want to enhance your data engineering skills...

In this practical course, you will learn how to build a complete data pipeline on the AWS platform - from obtaining data from the Twitter API to analysis, stora

Processing, storing, and visualizing time series data is becoming an increasingly important task. From IoT data and system logs to statistics...

Big Data is not just a buzzword but a real phenomenon. Every day, companies around the world collect and process massive volumes of data at a high...

Data engineers often need to quickly set up a simple ETL script that just does its job. In this project, you will learn how to easily implement...

This course is a comprehensive project with a full cycle of real-time data processing. You will work with data from an online store, including invoices...

This course is the perfect start for those who want to learn cloud technologies and start working with Amazon Web Services (AWS), one of the most popular..

Microsoft Azure is a cloud platform offering more than 200 products and services for data storage, management, virtual machine deployment, and...

Google Cloud Platform (GCP) is one of the most popular cloud platforms in the world, providing an extensive set of tools and services for building...

As a data engineer, you will regularly work with analytics platforms where companies store data in Data Lakes and Data Warehouses for building...

This course is your first step into the world of data analysis using one of the main tools for analysts - Python. Without complicated terms, advanced...

dbt (data build tool) is a data transformation tool with a priority on SQL. It allows for simple and transparent transformation, testing, and documentation...

Snowflake is a next-generation cloud data warehouse that everyone is talking about today. The platform operates 100% in the cloud, providing flexible access...

In this course, you will acquire the basic knowledge necessary for confidently starting to work with Apache Kafka. You will learn how to set up a message...

Databricks is one of the most popular platforms for data processing using Apache Spark and creating modern data warehouses (Lakehouse).

During my coaching sessions, one important topic repeatedly comes up - designing diagrams. Therefore, I decided to create a separate course in the academy to...

After building data pipelines, data processing is one of the most important tasks in Data Engineering. As a data engineer, you constantly encounter...

One of the key tasks when creating a data platform and pipelines is the selection of appropriate data storage systems. This course is dedicated to that topic.

In today's world, where data plays a key role, effective organization of information is the foundation for quality analytics and report building.

Apache Airflow is a platform-independent tool for workflow orchestration that provides extensive capabilities for creating and...

API is the foundation of any modern data platform. You either provide an API for clients or use external APIs yourself. In any case, it's important to be...

A reliable security concept for platforms and pipelines is critically important. Almost anyone can put together a Proof of Concept without an adequate level...

Relational modeling is widely used in building transactional databases. You might say, "But I'm not planning to become a backend engineer."

Data pipelines are a key component of any Data Science platform. Without them, data loading and machine learning model deployment would not be possible. This...