Data Engineer
Build strong competencies in large-scale data processing. Master Apache Spark, Kafka, Airflow, Hadoop and Data Mesh architecture and ETL pipelines.
Featured Trainings
Apache Spark for developers - large-scale data processing
Advanced Apache Spark training focusing on the practical aspects of data processing in distributed environments. The program covers both fundamental concepts of distributed processing and advanced techniques for optimizing and implementing complex data flows. The workshop is conducted in the form of intensive hands-on classes, where participants work on real data sets, implementing a variety of analytical scenarios. Special emphasis is placed on understanding the internal mechanisms of Spark and the ability to use them effectively in production projects.
View trainingApache Kafka for developers - architecture and implementation
Intensive workshop training devoted to the architecture and implementation of solutions based on Apache Kafka. During the course, participants will learn both the theoretical basics of the platform and the practical aspects of its use in a production environment. The training is carried out in the form of workshops, where 70% of the time is devoted to practical exercises. The classes are based on real use cases and project scenarios.
View trainingApache Airflow - data flow orchestration
Advanced training in dataflow orchestration using Apache Airflow. The program focuses on the design, implementation and management of complex data processing pipelines. Participants, through hands-on workshops, will learn techniques for automating ETL processes, task monitoring and error handling. The training combines theory with intensive hands-on exercises, enabling real-world experience.
View trainingData Engineer Path
This path prepares you for the Data Engineer role — from data processing fundamentals with Apache Spark and Kafka, through orchestration with Airflow, to advanced Data Mesh architectures and real-time analytics. The program combines theory with practice on real data pipelines.
Path 1: Goal
Apache Spark — large-scale data processing, SQL, MLlib and streaming.
Recommended EITT Trainings
Rationale
Apache Spark is the leading engine for big data processing. Trainings cover the full spectrum — from basics through SQL and PySpark to MLlib and streaming, providing a complete Data Engineer skill set.
Path 2: Goal
Apache Kafka — messaging systems, streaming and data source integration.
Recommended EITT Trainings
Rationale
Kafka is the foundation of modern data architectures — streaming, event-driven and real-time processing. Trainings cover core Kafka, Kafka Connect, Streams and Confluent Platform.
Path 3: Goal
Orchestration and architecture — Airflow, Data Mesh and real-time analytics.
Recommended EITT Trainings
Rationale
Airflow is the standard for data pipeline orchestration. Combined with Flink and Kafka Streaming, it enables building complete real-time data processing systems.
Interested in this path?
Contact us to discuss the details of the training program and tailor it to your needs.