Big Data Courses 2026: Data Engineering Overview

Big Data and Data Engineering Courses Review for 2026

In 2026, the market offers a variety of Big Data and Data Engineering courses. Six programs have been selected from the Habr Courses catalog: three for beginners and three for professionals with SQL and Python skills. They cover ETL, data warehouses, Spark, Kafka, and cloud platforms. Below are the key characteristics in a table for quick analysis.

| Course | School | Duration | Format | Key Feature |

|--------|--------|----------|--------|-------------|

| Data Science Specialist | Yandex Practicum | 13 months | Cohort-based | 8 projects + Yandex Cloud |

| Data Engineer | karpov courses | 5 months | Intensive | Architecture + Hadoop |

| BI Analyst | Netology | 9 months | Online + webinars | Kafka/Spark Streaming |

| Data Analyst Profession | Skillbox | 12 months (5-9 actual) | Flexible | From scratch + 9 projects |

| Data Science Specialist, Extended | Yandex Practicum | 17 months | Cohort-based | Full cycle up to ML |

Programs for Professionals with a Foundation

Data Science Specialist by Yandex Practicum

A course for mid-level professionals: requires SQL and Python. Over 13 months, you'll learn DWH design on PostgreSQL with SCD, data marts, ETL via Airflow, NoSQL (MongoDB), Kubernetes, Yandex Cloud, and stream processing. Workload is 12 hours per week, with a cohort-based format and deadlines. Practice includes 8 projects on real data—ETL pipelines, Data Lake for e-commerce and startups.

Characteristics:

• Level: Intermediate
• Certificate: Professional retraining diploma
• Installment plan: from 4,080 ₽/month

Pros: Full stack from DWH to clouds, mentor support. Cons: Strict deadlines, delays in reviews.

Data Engineer by karpov courses

A 5-month intensive for junior+/mid-level. Focus on architecture: PostgreSQL, Greenplum (MPP), Airflow for ETL, Big Data stack (Hadoop, Spark, Hive, Kafka, S3), Yandex Cloud, visualization in Superset/Tableau. Workload is 10-15 hours per week, with 3 sessions. Project: ETL on Airflow + Spark + Greenplum, plus 200+ simulator assignments.

Characteristics:

• Level: Junior+/Intermediate
• Certificate: In Russian/English
• Installment plan: from 5,792 ₽/month

Pros: Systematic tool approach, interview simulators. Cons: One major project, fast pace.

BI Analyst by Netology

9 months for those experienced in SQL/Python. Modules: advanced SQL, DWH, ETL on Spark/Airflow, Kafka/Spark Streaming, Yandex Cloud, neural networks. Free access to Yandex Cloud. Projects: 6, including OLAP cubes and pipelines. Workload 8-10 hours, with webinars and meetups.

Characteristics:

• Level: Experienced
• Certificate: Diploma
• Installment plan: from 4,092 ₽/month

Pros: Streaming data, real-world cases. Cons: Requires foundation, extensive material.

Courses from Scratch for Career Entry

Data Analyst Profession by Skillbox

12 months (actual 5-9), starting with Python/SQL. Then: ETL/Airflow/Spark/Kafka/Docker, Big Data (Hadoop/HDFS/Spark), Data Lake (ClickHouse), DWH (Greenplum/Power BI), testing (Great Expectations). Flexible pace, 9 projects (6 real), HR support with job guarantee.

Characteristics:

• Level: Beginner
• Certificate: Standard certificate
• Installment plan: 5,370 ₽/month, first payment after 6 months

Pros: From scratch to portfolio, employment assistance. Cons: Extended duration.

Data Science Specialist, Extended, by Yandex Practicum

17 months from scratch: Python/SQL, DWH, ETL/Airflow, NoSQL, clouds, stream processing, ML prep. Cohort-based format, 8+ projects + company cases. Deep dive into business tasks.

Characteristics:

• Level: Beginner
• Certificate: Diploma
• Installment plan: from 13,440 ₽/month

Pros: Up to mid-level, real-world tasks. Cons: Long duration, heavy workload.

Criteria for Choosing a Data Engineering Course

Selection depends on level, format, and goals. Here are key parameters:

• Entry Level: Beginners—courses with Python/SQL (11-17 months). With a foundation—focus on architecture/Big Data (5-13 months).
• Format: Cohort-based—discipline + support. Flexible—self-paced. Intensive—rapid progress.
• Practice: Minimum 5-8 projects, real data, stack (Spark, Kafka, Airflow).
• Support: Mentors, reviews, HR.
• Certificate: Diploma for your resume.

Check requirements: without SQL, avoid mid-level courses—risk of burnout.

What's Important

• Full stack: ETL (Airflow), Big Data (Spark/Hadoop/Kafka), clouds (Yandex Cloud), DWH (PostgreSQL/Greenplum).
• Practice on real data: 6-9 projects for your portfolio.
• Level adaptation: beginners learn basics, mid-level—architecture.
• Guarantees: HR support, installment plans, diplomas.
• Common cons: deadlines, pace, feedback delays.

— Editorial Team