Database Internals NEW
Databases aren’t magic — let’s open the hood.
350 $/місяць
пн / ср, 19:00 (UTC+3)
start:
Aug 3, 2026
// 25 занять, 3 місяці
350 $/місяць
пн / ср, 19:00 (UTC+3)
what's inside
Як побудувати систему управління базами даних? Чому існує так багато різновидів СУБД, і щороку з’являються нові? Як насправді працюють primary key, join, order by? Як дані зберігаються на диску та які оптимізації застосовують сучасні системи? Якщо ви хочете отримати відповіді на ці та інші запитання про створення СУБД — цей курс для вас.
Протягом навчання ми розглянемо повний шлях побудови бази даних — від парсингу запитів до їх виконання та масштабування. Ви дізнаєтеся, які алгоритми та концепції лежать в основі сучасних СУБД, і на практиці створите власну.
Курс буде корисний тим, хто прагне глибше зануритися в системне програмування, користувачам СУБД, які хочуть зрозуміти внутрішні принципи роботи баз даних та навчитися ефективніше їх оптимізувати. Найкращий досвід навчання — з Rust або C++, адже вони дозволять глибше зануритися в архітектуру баз даних і краще відчути роботу з пам’яттю та продуктивністю на практиці, але це лише рекомендація.
Curriculum
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FOR ENGINEERS
History of databases
The path from file systems to modern data storage
• The first DBMS models: Hierarchical, Network
• The revolution of the relational model and its impact on the industry
• The emergence of SQL, the development of object-oriented and NoSQL databases
Data warehouse basics
How data is organized on disk and in memory, and why it affects performance
• Pages and blocks as basic storage units
• Organizing records in pages: fixed and variable length
• Row-store and its applications
• Data fragmentation and defragmentation methods
Indexes. B-tree and LSM
Data structures to speed up searches, inserts, and range scans
• B-Tree and B+Tree: structure, search, insertion and deletion algorithms
• LSM trees: how log-structured merges work
• Write/Read amplification and compaction mechanisms
• Using Bloom filters to speed up searches
Columnar and hybrid data stores. Compression
Efficient storage of analytical data
• Row-oriented vs column-oriented storage
• Advantages of the columnar approach for analytical queries
• Data compression methods: RLE, dictionary encoding, delta encoding
• Hybrid HTAP systems (Hybrid Transactional/Analytical Processing)
• PAX format — combining the advantages of row and column approaches
Relational model. Relational algebra
Formal foundations of relational DBMSs and operations underlying SQL
• Relationships, attributes, tuples, and keys
• Primary and foreign keys. Ensuring data integrity
• Basic operations of relational algebra: selection, projection, union, difference, Cartesian product
• Joins: inner, outer, natural
• Properties of algebra — commutativity, associativity. The role of algebra as a basis for query optimization
Query planning and optimization
Logical plans (relational algebra trees)
• Building an Abstract Syntax Tree (AST)
• Heuristic optimizations: pushdown selections and projections
• Cost-based optimization: using statistics and evaluating selectivity
• Join strategies: nested loop, hash join, sort-merge join
Query execution. Plan vectorization. SQL compilation
Row-at-a-time vs batch-at-a-time approach
• Volcano (iterator) execution model
• Vectorization: SIMD and block processing of values
• JIT compilation of SQL queries
Data types. Type system. Type casting.
Basic types: numeric, text, time, logical, binary
• Handling NULL values
• Explicit and implicit type casting
• Rules of precedence in expressions
• User-defined types
• Semi-structured data types (JSON, XML, etc.)
Transactions and Concurrent Access Management
ACID transaction properties
• Concurrency issues: dirty reads, phantom reads, and others
• Transaction isolation levels: Read Committed, Repeatable Read, Serializable, etc.
• 2PL (two-phase locking) and deadlock detection
• MVCC (multi-version concurrency control) and snapshot isolation
Open-source databases. Modern architecture and recent issues
Using ML for indexing, planning, tuning
• Serverless databases
• Separation of compute and storage layers
• Automation and self-driving DBMS
• Lakehouse architecture, agentic DBMS and Iceberg storage
Presentation of projects and analysis of research
You've written your own DBMS. Time to present it :)
Instructor
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Денис Цьоменко
Founding Engineer @Embucket
Former Software Engineer @CaspianDB, @SingleStore, @DataRobot, @Microsoft.
Former University lecturer @Kyiv School of Economics
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reviews
What alumni say
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reviews
what alumni say
What our
alumni say
Senior Software Engineer @ICC Chess Club
Yevhen Dudnik
I went to the course with specific, quite deep questions, but it turned out that my questions were somewhere in the shallows. You have to be prepared for the fact that you will learn things about DBs that you didn't know about and that aren't even written about anywhere. The course is very interesting, my recommendation.
.Net Developer @FlexBricks
Dmytro Avilov
I liked the course. I gained a lot of knowledge, and I began to understand a little better how databases work and which way to look when solving performance problems.
Android Developer @Competo LLC
Anatolii Kokuliuk
There are a lot of topics in the course, and you can get stuck in each one for a long time. As someone who is not interested in databases, the course is 100% engaging for me. Building a storage, vocabulary, building a pipeline, execution — super interesting topics that go far beyond the database.
Java Software Engineer @Intapp
Mykola Pikuza
The course material is deep, practical, and very well structured, but what really makes it special is the community and the instructor. Denis is a great instructor who tries to explain complex database concepts simply and systematically. Always attentive to detail and willing to go over difficult points as many times as needed.
Backend Engineer @Preply
Denys Ralko
What I liked most was the depth of the topics covered. I knew a lot of things as a fact before, but I didn't understand why they worked the way they did. The course gives me an opportunity to understand the reasons and mechanics of these decisions.
Senior Director of Engineering @Pindrop
Volodymyr Shulha
I joined the course because data volumes keep growing — and so does the variety of database types. Many projects already rely on several different databases for different tasks. I wanted to develop a deeper understanding of how different types of databases are structured and which use cases each one is best suited for. The course material is extensive and high quality: finding this much information on your own and structuring it into a coherent whole would have been a real challenge.
communication that drives you
We meet twice a week on Zoom — Wednesdays at 18:30 and Saturdays at 10:30, 1.5 hours each session. Extra Q&A sessions with your instructor. All lectures recorded.
Hands-on assignments with clear deadlines. You'll write plenty of code — language is your choice.
Slack is our hub for discussions, clever test cases, and top company referrals.
format that works
Constant feedback in Slack, lectures, and Q&A — we're always connected.
No shallow slides — only deep dives into real production problems.
Certificates are earned: completed homework, active participation, tangible progress.
environment that energizes
We screen carefully — you'll learn among driven, motivated engineers.
Homework isn't exercises. These are real mini-projects mirroring production: optimization, deadlines, bugs, feedback, refactoring. You'll write serious code across Python, Java, Go, Assembly, C++. No fluff — pure engineering.
Your instructor won't leave you hanging. They explain until it clicks — third code review, staying late to debug, whatever it takes. We're here to push hard and level up.