Topic 4.4. Principles of data warehouse construction: cloud storage, architecture
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☁️ **A data warehouse is more than just a database. It´s a strategic company asset that transforms disparate data into valuable business intelligence.**
This lecture (11 pages long) is your guide to the world of modern data warehouses. You´ll master the principles of building cloud warehouses, explore the three-tier architecture of a data warehouse, understand the differences between SQL, NoSQL, and NewSQL databases, and learn about AI tools that reduce warehouse design time by 3-5 times.
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📦 **What´s inside the lecture (11 pages of Nebulae-style content):**
• **Pages 1-2. Title page and plan.** Academic structure (GBPOU, specialty 09.02.07), lesson objectives, and a current list of sources (1C documentation, books by Radchenko, Gabetz, Khrustaleva, Azheronok).
• **Page 3. ☁️ Cloud data storage (1/2).** Key concepts, 4 key advantages (scalability, availability, reliability, cost-effectiveness), and 3 deployment models: public (Yandex Cloud, VK Cloud, SberCloud), private, and hybrid cloud.
• **Page 4. ☁️ Cloud data storage (2/2).** 1C configuration storage for collaborative development and modern alternatives as of 2026: Git 1C:EDT, GitLab/GitHub with CI/CD, Azure DevOps. AI tools: GitHub Copilot, GitLab Duo, AI merge conflict analysis.
• **Page 5. 🏗️ Data warehouse architecture (1/2).** Three-tier architecture: Source Layer (1C, CRM, ERP) → Integration Layer (ETL processes) → Presentation Layer (analytics). Key components: staging area, data mart, ODS.
• **Page 6. 🏗️ Data warehouse architecture (2/2).** Comparison of approaches: traditional Inmon (top-down) vs. modern Kimball (bottom-up). Data Lake vs. Data Warehouse vs. Lakehouse. Cloud architecture 2026: Yandex Managed ClickHouse, DataLens, Apache Kafka, Apache Spark.
• **Page 7. 🗄️ Data Warehouse Systems (1/2).** DBMS classification: relational (PostgreSQL, MS SQL, Oracle, MySQL), NoSQL (MongoDB, Cassandra, Redis, Elasticsearch). Selection criteria: data volume, ACID, scalability, cost.
• **Page 8. 🗄️ Data Warehouse Systems (2/2).** Current trends 2026: NewSQL (CockroachDB, TiDB, YugabyteDB), time-series databases (InfluxDB, TimescaleDB), graph databases (Neo4j, Neptune). **Russian DBMS (import substitution):** Postgres Pro, Tarantool, ClickHouse, YDB.
• **Page 9. 🤖 AI tools for working with data warehouses.** AI for design (DDL scripts, schema optimization), AI for ETL (field mapping, anomaly detection), AI for analytics (NLQ queries, forecasting). A ready-made prompt for data architects.
• **Page 10. ✅ Checklists and practice.** Data warehouse design checklist (8 items), 4 practical exercises, 3 exercises with AI.
• **Page 11. Final and legal section.** Action plan and full copyright protection.
---
👤 **Who is this lecture for?**
• 🎓 Students majoring in 09.02.07 "Information Systems and Programming."
• 💼 1C developers transitioning to business analytics and BI tasks.
• 🏢 Data architects and analysts designing warehouses for enterprise solutions.
• 🚀 Freelancers building 1C-based analytical systems.
• 👨💻 Database administrators mastering modern DBMSs (PostgreSQL, ClickHouse, YDB).
---
✓ **What you´ll get:**
• **Deep understanding of cloud storage:** from basic concepts to deployment models (public/private/hybrid cloud).
• **Knowledge of Data Warehouse architecture:** three-tier model, ETL processes, staging area, data mart, ODS.
• **Comparison of Inmon/Kimball/Lakehouse approaches:** understanding when to use each approach. • **Overview of modern DBMSs 2026:** from classic SQL to NewSQL, time-series, and graph databases.
• **Knowledge of Russian DBMSs:** Postgres Pro, Tarantool, ClickHouse, YDB — relevant in the context of import substitution.
• **AI tools for data architects:** ready-made prompts for ChatGPT/Claude, GitHub Copilot, GitLab Duo.
• **Ready-made design checklist:** 8 points that will prevent you from missing important things during design
This lecture (11 pages long) is your guide to the world of modern data warehouses. You´ll master the principles of building cloud warehouses, explore the three-tier architecture of a data warehouse, understand the differences between SQL, NoSQL, and NewSQL databases, and learn about AI tools that reduce warehouse design time by 3-5 times.
---
📦 **What´s inside the lecture (11 pages of Nebulae-style content):**
• **Pages 1-2. Title page and plan.** Academic structure (GBPOU, specialty 09.02.07), lesson objectives, and a current list of sources (1C documentation, books by Radchenko, Gabetz, Khrustaleva, Azheronok).
• **Page 3. ☁️ Cloud data storage (1/2).** Key concepts, 4 key advantages (scalability, availability, reliability, cost-effectiveness), and 3 deployment models: public (Yandex Cloud, VK Cloud, SberCloud), private, and hybrid cloud.
• **Page 4. ☁️ Cloud data storage (2/2).** 1C configuration storage for collaborative development and modern alternatives as of 2026: Git 1C:EDT, GitLab/GitHub with CI/CD, Azure DevOps. AI tools: GitHub Copilot, GitLab Duo, AI merge conflict analysis.
• **Page 5. 🏗️ Data warehouse architecture (1/2).** Three-tier architecture: Source Layer (1C, CRM, ERP) → Integration Layer (ETL processes) → Presentation Layer (analytics). Key components: staging area, data mart, ODS.
• **Page 6. 🏗️ Data warehouse architecture (2/2).** Comparison of approaches: traditional Inmon (top-down) vs. modern Kimball (bottom-up). Data Lake vs. Data Warehouse vs. Lakehouse. Cloud architecture 2026: Yandex Managed ClickHouse, DataLens, Apache Kafka, Apache Spark.
• **Page 7. 🗄️ Data Warehouse Systems (1/2).** DBMS classification: relational (PostgreSQL, MS SQL, Oracle, MySQL), NoSQL (MongoDB, Cassandra, Redis, Elasticsearch). Selection criteria: data volume, ACID, scalability, cost.
• **Page 8. 🗄️ Data Warehouse Systems (2/2).** Current trends 2026: NewSQL (CockroachDB, TiDB, YugabyteDB), time-series databases (InfluxDB, TimescaleDB), graph databases (Neo4j, Neptune). **Russian DBMS (import substitution):** Postgres Pro, Tarantool, ClickHouse, YDB.
• **Page 9. 🤖 AI tools for working with data warehouses.** AI for design (DDL scripts, schema optimization), AI for ETL (field mapping, anomaly detection), AI for analytics (NLQ queries, forecasting). A ready-made prompt for data architects.
• **Page 10. ✅ Checklists and practice.** Data warehouse design checklist (8 items), 4 practical exercises, 3 exercises with AI.
• **Page 11. Final and legal section.** Action plan and full copyright protection.
---
👤 **Who is this lecture for?**
• 🎓 Students majoring in 09.02.07 "Information Systems and Programming."
• 💼 1C developers transitioning to business analytics and BI tasks.
• 🏢 Data architects and analysts designing warehouses for enterprise solutions.
• 🚀 Freelancers building 1C-based analytical systems.
• 👨💻 Database administrators mastering modern DBMSs (PostgreSQL, ClickHouse, YDB).
---
✓ **What you´ll get:**
• **Deep understanding of cloud storage:** from basic concepts to deployment models (public/private/hybrid cloud).
• **Knowledge of Data Warehouse architecture:** three-tier model, ETL processes, staging area, data mart, ODS.
• **Comparison of Inmon/Kimball/Lakehouse approaches:** understanding when to use each approach. • **Overview of modern DBMSs 2026:** from classic SQL to NewSQL, time-series, and graph databases.
• **Knowledge of Russian DBMSs:** Postgres Pro, Tarantool, ClickHouse, YDB — relevant in the context of import substitution.
• **AI tools for data architects:** ready-made prompts for ChatGPT/Claude, GitHub Copilot, GitLab Duo.
• **Ready-made design checklist:** 8 points that will prevent you from missing important things during design
Main features
- Content type File
- Content description 2652,69 kB
- Updated on the site 23.08.2026
Additional description
⚖️ **INTELLECTUAL PROPERTY:**
All lecture materials (structure, original wording, checklists, and practical assignments) are the original work of Keeperlink, 2026. This lecture is based on the educational materials of the continuing professional education program (lecturers A.L. Popov and G.S. Mozyrskaya) with complete authorial revision, exclusion of outdated data, and the addition of 50 new practical content (AI tools, modern cloud solutions, and Russian DBMS). Mention of trademarks such as "1C," "1C:Enterprise," "PostgreSQL," "ClickHouse," "Yandex Cloud," "Git," "GitLab," "GitHub," "Apache Kafka," "Apache Spark," "MongoDB," "Redis," "Neo4j," "InfluxDB," "CockroachDB," "Tarantool," "YDB," "ChatGPT," "Claude," and others is for nominative purposes only and does not constitute affiliation with the copyright holders (Article 1484 of the Civil Code of the Russian Federation). © Keeperlink, 2026. All rights reserved.
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🚀 **Start designing a simple PostgreSQL data warehouse today!** Open the checklist on page 10 and design your first data warehouse. Click "Buy" to gain deep knowledge of warehouse architecture and modern DBMS that will make you a sought-after data architect.
All lecture materials (structure, original wording, checklists, and practical assignments) are the original work of Keeperlink, 2026. This lecture is based on the educational materials of the continuing professional education program (lecturers A.L. Popov and G.S. Mozyrskaya) with complete authorial revision, exclusion of outdated data, and the addition of 50 new practical content (AI tools, modern cloud solutions, and Russian DBMS). Mention of trademarks such as "1C," "1C:Enterprise," "PostgreSQL," "ClickHouse," "Yandex Cloud," "Git," "GitLab," "GitHub," "Apache Kafka," "Apache Spark," "MongoDB," "Redis," "Neo4j," "InfluxDB," "CockroachDB," "Tarantool," "YDB," "ChatGPT," "Claude," and others is for nominative purposes only and does not constitute affiliation with the copyright holders (Article 1484 of the Civil Code of the Russian Federation). © Keeperlink, 2026. All rights reserved.
---
🚀 **Start designing a simple PostgreSQL data warehouse today!** Open the checklist on page 10 and design your first data warehouse. Click "Buy" to gain deep knowledge of warehouse architecture and modern DBMS that will make you a sought-after data architect.
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Topic 4.4. Principles of data warehouse construction: cloud storage, architecture
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