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.
AI ने डेटा इंजीनियरिंग को खत्म नहीं किया। हर AI मॉडल को साफ डेटा चाहिए, और वो काम डेटा इंजीनियर करता है।
छंटनी किस पर पड़ी
सिर्फ रिपोर्ट बनाने वाली भूमिकाएँ ज़्यादा कटीं। पाइपलाइन और क्लाउड संभालने वाले लोग ज़्यादातर बचे रहे।
रिज़्यूमे
एक कॉलम, एक पेज। हर पॉइंट में एक नंबर। GitHub पर तीन असली प्रोजेक्ट। मुफ्त कोर्स
NVIDIA DLI, Google Cloud Skills Boost, Microsoft Learn (DP-900), Databricks Academy और DE Zoomcamp।
इंटर्नशिप
अगस्त से अक्टूबर के बीच अप्लाई करें और सिर्फ बड़ी कंपनियों पर निर्भर न रहें।
शुरू करने के लिए मुफ्त वीडियो
VIDEO
डेटा प्लेटफॉर्म आर्किटेक्चर
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.
सिस्टम फ्लो: एक ऑर्डर शुरू से अंत तक
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
डेटा सेंटर कैसे काम करता है
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.
पूरी अंग्रेज़ी गाइड पढ़ें →
शुरू करने के लिए मुफ्त वीडियो
VIDEO
डेटा प्लेटफॉर्म आर्किटेक्चर
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.
सिस्टम फ्लो: एक ऑर्डर शुरू से अंत तक
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
डेटा सेंटर कैसे काम करता है
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.
पूरी अंग्रेज़ी गाइड पढ़ें →