Illustration of a data engineer at a dual-monitor workstation showing an Airflow DAG and a warehouse query, with server racks glowing in the background at night.
SELECT customer_id, SUM(amount) FROM orders GROUP BY 1;
DATA ENGINEER · 2026 · ZUBAIRHUSSAIN.XYZ
Data engineer workstation: Airflow DAG on the left, warehouse SQL on the right.
Illustration of a data engineer at a dual-monitor workstation showing an Airflow DAG and a warehouse query, with server racks glowing in the background at night.
SELECT customer_id, SUM(amount) FROM orders GROUP BY 1;
DATA ENGINEER · 2026 · ZUBAIRHUSSAIN.XYZ
Data engineer workstation: Airflow DAG on the left, warehouse SQL on the right.
ИИ не убил профессию дата-инженера. Наоборот: каждой модели нужны чистые и управляемые данные.
Кого коснулись сокращения
В первую очередь тех, кто только строил отчёты. Люди, отвечающие за пайплайны и облако, в основном остались.
Резюме
Одна колонка, одна страница. В каждом пункте цифра. Три проекта на GitHub со схемой и README. Бесплатное обучение
NVIDIA DLI, Google Cloud Skills Boost, Microsoft Learn (DP-900), Databricks Academy, DE Zoomcamp.
Бесплатное видео для старта
VIDEO
Архитектура платформы данных
Five-layer architecture: sources (apps, databases, APIs, IoT) flow through batch and streaming ingestion (Fivetran, Kafka) into a lakehouse with bronze, silver and gold layers on object storage, transformed by Spark and dbt, orchestrated by Airflow, governed by a catalog, and served to BI dashboards, ML feature stores and RAG vector databases.
1 · SOURCES 2 · INGEST 3 · LAKEHOUSE 4 · SERVE
App events OLTP database SaaS APIs IoT / logs
Batch / CDC Fivetran · Debezium
Streaming Kafka · Kinesis
Bronze · raw as-landed, append-only
Silver · clean deduped, typed, joined
Gold · business facts, dims, metrics
S3 / ADLS / GCS · Delta / Iceberg
BI dashboards Power BI · Looker · Superset
ML feature store training + online inference
RAG / LLM apps embeddings → vector DB
Orchestration · Airflow / Dagster — schedules, retries, backfills, SLAs
Transformation · Spark (big data) + dbt (SQL models, tests, docs)
Governance · catalog, lineage, access control, data quality checks, cost monitoring
MEDALLION LAKEHOUSE REFERENCE · ZUBAIRHUSSAIN.XYZ
Medallion lakehouse architecture: the pattern used by Databricks, Snowflake and Fabric teams.
Поток системы: один заказ от начала до конца
Seven-step sequence: customer checkout writes to Postgres, CDC captures the change, Kafka streams it, Spark job cleans it into silver, dbt builds gold tables, dashboard refreshes and the recommendation model retrains.
1 2 3 4 5 6 7 Checkout Postgres CDC Kafka Spark dbt Dashboard + AI t = 0s row insert ~1s ~2s micro-batch hourly refresh
● ONE ORDER · SEVEN HOPS
Как работает дата-центр
Perspective view down a cold aisle between two rows of server racks with blue and green status LEDs, raised floor tiles, overhead cable trays and cooling ducts.
COLD AISLE · GPU RACKS · ZUBAIRHUSSAIN.XYZ
Cold aisle between two rows of racks. Cold air comes up through the perforated floor; servers exhaust heat into the hot aisle behind them.
Grid power passes through substation, UPS batteries and generators to power distribution units feeding the racks. Chillers and cooling towers feed CRAH units that push cold air through the aisles. Top-of-rack switches connect to spine switches and fiber out to the internet.
⚡ POWER ❄ COOLING ⇄ NETWORK
Utility grid Substation UPS batteries ride-through seconds Diesel generators
Cooling tower Chiller CRAH / liquid direct-to-chip for GPUs
Internet / fiber Spine switches Top-of-rack
GPU node · 8× accelerators GPU node · 8× accelerators CPU node · Spark workers Storage · NVMe / object Storage · NVMe / object PDU · power strips Server rack (42U)
PUE = TOTAL FACILITY POWER ÷ IT POWER · GOOD SITES ≈ 1.1–1.3
Power, cooling and network: the three systems that keep every rack alive.
Читать полное руководство на английском →
Бесплатное видео для старта
VIDEO
Архитектура платформы данных
Five-layer architecture: sources (apps, databases, APIs, IoT) flow through batch and streaming ingestion (Fivetran, Kafka) into a lakehouse with bronze, silver and gold layers on object storage, transformed by Spark and dbt, orchestrated by Airflow, governed by a catalog, and served to BI dashboards, ML feature stores and RAG vector databases.
1 · SOURCES 2 · INGEST 3 · LAKEHOUSE 4 · SERVE
App events OLTP database SaaS APIs IoT / logs
Batch / CDC Fivetran · Debezium
Streaming Kafka · Kinesis
Bronze · raw as-landed, append-only
Silver · clean deduped, typed, joined
Gold · business facts, dims, metrics
S3 / ADLS / GCS · Delta / Iceberg
BI dashboards Power BI · Looker · Superset
ML feature store training + online inference
RAG / LLM apps embeddings → vector DB
Orchestration · Airflow / Dagster — schedules, retries, backfills, SLAs
Transformation · Spark (big data) + dbt (SQL models, tests, docs)
Governance · catalog, lineage, access control, data quality checks, cost monitoring
MEDALLION LAKEHOUSE REFERENCE · ZUBAIRHUSSAIN.XYZ
Medallion lakehouse architecture: the pattern used by Databricks, Snowflake and Fabric teams.
Поток системы: один заказ от начала до конца
Seven-step sequence: customer checkout writes to Postgres, CDC captures the change, Kafka streams it, Spark job cleans it into silver, dbt builds gold tables, dashboard refreshes and the recommendation model retrains.
1 2 3 4 5 6 7 Checkout Postgres CDC Kafka Spark dbt Dashboard + AI t = 0s row insert ~1s ~2s micro-batch hourly refresh
● ONE ORDER · SEVEN HOPS
Как работает дата-центр
Perspective view down a cold aisle between two rows of server racks with blue and green status LEDs, raised floor tiles, overhead cable trays and cooling ducts.
COLD AISLE · GPU RACKS · ZUBAIRHUSSAIN.XYZ
Cold aisle between two rows of racks. Cold air comes up through the perforated floor; servers exhaust heat into the hot aisle behind them.
Grid power passes through substation, UPS batteries and generators to power distribution units feeding the racks. Chillers and cooling towers feed CRAH units that push cold air through the aisles. Top-of-rack switches connect to spine switches and fiber out to the internet.
⚡ POWER ❄ COOLING ⇄ NETWORK
Utility grid Substation UPS batteries ride-through seconds Diesel generators
Cooling tower Chiller CRAH / liquid direct-to-chip for GPUs
Internet / fiber Spine switches Top-of-rack
GPU node · 8× accelerators GPU node · 8× accelerators CPU node · Spark workers Storage · NVMe / object Storage · NVMe / object PDU · power strips Server rack (42U)
PUE = TOTAL FACILITY POWER ÷ IT POWER · GOOD SITES ≈ 1.1–1.3
Power, cooling and network: the three systems that keep every rack alive.
Читать полное руководство на английском →