Flexible Enrollment — Data Engineering Daily

Section 1 — Foundation

Basics — Python, SQL, PySpark & Your First Project

Master the three languages every DE interview tests — Python, SQL, PySpark — and build a real Bronze→Silver→Gold pipeline from scratch. This is the foundation every other section is built on. No prior experience required.

What You Build & Learn

Python 10 Days 📅 View Day-by-Day Plan ▼

Python for Data Engineers

Not "learn Python" — learn the Python that DE interviewers actually test. Pipeline automation, file processing, API calls, JSON wrangling. Build it live. Own it.

For Loop, Break, Continue, Enumerate, ZipLoop over data, accumulate running totals, pair datasets — the iteration patterns every pipeline script uses daily

String Methods & ManipulationParse CSV rows, extract email domains, clean log lines — string ops that show up in every ingestion pipeline

Array & List OperationsFind Nth highest, remove duplicates preserving order, sliding window problems — list mastery for DE interviews

Dictionary & HashMapTwo-sum patterns, word frequency, grouping records by key — hashmap thinking that makes O(n²) problems O(n)

HashSet & Set OperationsFind items in A not B, first non-repeating character, intersection of transaction lists — set logic for data reconciliation

Copy vs Deep CopyMutation bugs in pipeline configs, deepcopy nested dicts safely, write the bug and the fix — classic interview trap

File Handling — CSV, JSON, TXTRead without pandas, filter and write JSON, process large log files with generators — production file patterns

Database Connection — sqlite3Create tables, bulk insert with executemany(), parameterized queries to prevent injection — DB fundamentals for DEs

API Response Handling — JSON ParsingFlatten nested responses, simulate pagination with generators, handle missing fields and API errors gracefully

D10

DSA — Hashing, Prefix Sum, Two PointerUnique pairs summing to target, max revenue in 7-day window, busiest 1-hour window — DE-flavoured DSA problems

🎯 Milestone: Solve any Python DE interview problem live — loops, dicts, file I/O, APIs — without hesitation

SQL For Data Engineer 10 Days 📅 View Day-by-Day Plan ▼

SQL for Data Engineers

Window functions, CTEs, performance tuning — the SQL depth that gets you shortlisted while everyone else gets a rejection mail. One question at this level can double your negotiating power.

SQL Select and Filteringselect, where, filter, between, in, not in — the foundation of every data pipeline query

String Column Functionsupper(), lower(), trim(), regexp_replace(), substring(), concat() — SQL string methods + PySpark equivalents side by side

Date & Time Functionsdatediff(), date_trunc(), date_format(), months_between() — date logic that appears in every reporting pipeline

Aggregations — groupBy, aggsum(), count(), avg(), min(), max() — compute revenue per region, products sold, multi-column aggregations

Multi-level Grouping — rollup, cubeGROUPING SETS, ROLLUP, CUBE — produce subtotals and grand totals in a single pass

Joins — inner, left, rightStandard joins with real datasets: customers with no orders, order totals per customer including zeroes

Advanced Joins — self, cross, outerSelf join on manager_id, FULL OUTER JOIN for unmatched records, CROSS JOIN for all combinations

Set Operations — union, intersect, subtractunion() vs unionByName(), intersect(), subtract() — the SQL UNION / EXCEPT / INTERSECT family in PySpark

Window Functions Part 1 — rank, dense_rankWindow.partitionBy().orderBy() + ranking functions — top-N per group, deduplication, pagination

D10

Window Functions Part 2 — lag, lead, running totalslag(), lead(), sum().over(unboundedPreceding) — previous order value, running revenue, spike detection

🎯 Milestone: Write any window function or multi-level aggregation query live — the ones that eliminate 70% of candidates in round 2

PySpark 10 Days 📅 View Day-by-Day Plan ▼

PySpark Fundamentals

The moment you say "I've built distributed pipelines in Spark," the interview changes. Most freshers can't say that. You will — with code to prove it.

🟣 Basics — Days 1–5

SparkSession, RDDs & DataFramesSparkSession setup, RDD vs DataFrame trade-offs, reading CSV/JSON/Parquet, printSchema(), show() — the entry point every PySpark learner needs first

Select, Filter, withColumn, when/otherwiseColumn selection, filtering rows, adding derived columns, conditional logic with when().otherwise() — the 4 operations in every pipeline script

String & Date Functionsupper(), lower(), trim(), regexp_replace(), to_date(), datediff(), date_format() — column-level transformations used in every ingestion layer

groupBy, agg, orderBysum(), count(), avg(), min(), max() with groupBy; orderBy with asc/desc; multi-column aggregations — analytics queries built from raw data

Joins — inner, left, right, antiJoin types with real datasets, handling duplicates after join, anti-join for finding unmatched records — the join patterns every DE pipeline uses

🔵 Medium — Days 6–10

Window Functions — rank, dense_rank, row_numberWindow.partitionBy().orderBy(), ranking functions for top-N per group, deduplication keeping latest row — the window pattern every interviewer tests

Window Functions — lag, lead, running totalslag(), lead(), sum().over(unboundedPreceding) — previous row value, running revenue, consecutive event detection

SparkSQL & Temp ViewscreateOrReplaceTempView(), spark.sql() with CTEs, mixing SQL and DataFrame API — bridging SQL knowledge to distributed Spark processing

Reading & Writing — Parquet, Delta, PartitionedRead/write Parquet and Delta format, partitionBy() on write, predicate pushdown on read, overwrite vs append modes

D10

UDFs, Null Handling & Schema EnforcementPython UDFs vs built-ins (performance trade-off), fillna/dropna strategies, StructType schema enforcement at read — production-grade DataFrame hygiene

🎯 Milestone: Read raw files, apply joins + window functions, write partitioned Delta output — a complete PySpark transformation pipeline built from scratch

🛠️ Weekend DE Project 8 Weekends · 4–7 PM 📅 View Weekend Plan ▼

Weekend DE Project — Bronze → Silver → Gold

Build a production-grade local pipeline using PySpark, real data, and the exact patterns interviewers probe: idempotency, SCD Type 2, CDC, backfill, and data quality.

Bronze Ingestion LayerRead CSV/JSON with StructType schema enforcement; write partitioned Parquet; idempotent re-run by overwriting the date partition

Silver Cleaning — Business RulesDrop NULL customer IDs, cast dates, standardise strings, deduplicate by order_id keeping latest; flag invalid amounts — output DQ report

SCD Type 2 DimensionSHA-256 hash change detection on customer attributes; expire old rows, insert new versions; track new / changed / unchanged / expired counts

Gold Layer + Backfill CLIDaily KPIs (orders, revenue, unique customers, AOV); --start-date / --end-date backfill via argparse; idempotency verified by running twice

CDC & Fact TableDetect INSERT / UPDATE / DELETE via hash comparison; join orders with dim tables using surrogate key lookup; handle late-arriving data

Sessionisation & RFM SegmentationAssign session IDs from clickstream using Window LAG + 30-min gap; RFM customer segmentation in Gold; pipeline run metadata log

Unit Tests & Orchestrationpytest with PySpark fixture DataFrames for clean, SCD, and DQ functions; end-to-end runner with dependency chain

Integration Test + Interview WalkthroughFull pipeline integration test; README + architecture diagram; practice "walk me through your project" in 2 minutes

🎯 Milestone: Walk any interviewer through your complete pipeline — ingestion to Gold — in under 3 minutes, answering every architecture question that follows

Section 2 — First Cloud Project

Project 1 — Data Engineering Fundamentals + Azure Cloud Project

Learn the concepts interviewers expect — warehouse design, lakehouse architecture, dimensional modelling — then immediately apply them in a production-grade Azure project using ADF, ADLS Gen2, Databricks, and Delta Lake. Build it. Own it. Walk interviewers through it.

What You Build & Learn

🏛️ DE Fundamentals 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Data Engineering Fundamentals — Warehouse, Lakehouse & Modelling

The conceptual foundation every DE interview tests: warehouse vs lakehouse vs lake, dimensional modelling, SCD strategies, partitioning, and data quality design. Build the mental model that makes every Azure question easier to answer.

Data Warehouse vs Lakehouse vs Lake + MedallionOLAP vs OLTP, MPP architecture, columnar storage, Bronze/Silver/Gold layer ownership, freshness SLAs — the architecture every DE interview now references

Dimensional Modelling — Star & Snowflake SchemaKimball vs Inmon vs Data Vault, grain definition, star vs snowflake trade-offs, role-playing & junk dimensions — design a schema from a business requirement live

Fact & Dimension Table PatternsTransaction / snapshot / accumulating facts, additive vs semi-additive measures, SCD Type 1 / 2 / 3 / 6 — implement SCD2 from scratch in SQL and PySpark

Partitioning, Clustering & Data QualityPartition pruning, Z-ordering, clustering keys, DQ checks at each layer, freshness monitoring, lineage tracking — design for both performance and observability

Mock Design Round — End-to-End ModellingGiven a business domain (e-commerce / fintech / SaaS), design the full warehouse: grain, fact tables, dims, SCD strategy, partitioning — live whiteboard with interviewer pushback

🎯 Milestone: Given any business domain, design the full dimensional model — fact tables, dimension types, SCD strategy, partitioning — on a whiteboard in 15 minutes

🗄️ ADLS Gen2 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Azure Data Lake Storage Gen2 — Architecture & Ingestion

Master the storage backbone of every Azure pipeline. Hierarchical namespace, Delta Lake on ADLS, multi-format raw landing, partition design, and secure access via service principals — everything you need to design and defend your storage layer.

Data Volume: IoT Telemetry 20M events/day · PostgreSQL CDC 5M rows/day · Partner CSV/ORC 10M rows/batch · Parquet Alerts 2M/day ~37M+ records / day

ADLS Gen2 Architecture & SetupHierarchical namespace, storage accounts, containers, directory structure — service principals, RBAC, Key Vault integration — zero hardcoded credentials

Delta Lake on ADLS — Bronze Raw LandingWrite partitioned Delta tables to ADLS Gen2; append-only raw landing zone; idempotent date-partition overwrite; read-back verification

Multi-Format File IngestionUnified reader handling Parquet, CSV, JSON, XML, ORC on ADLS — format detected from metadata config table; schema validation at landing

Partition Strategy & Storage OptimisationDate/entity partition design for read pruning, Delta OPTIMIZE & VACUUM on ADLS, Z-ordering for analytical queries, file size tuning

Streaming to ADLS & CDC IngestionAzure Event Stream → Bronze Delta on ADLS at 20M IoT events/day; watermark management; CDC watermark from PostgreSQL fleet DB

🎯 Milestone: Design and defend an ADLS Gen2 storage layout — partitions, Delta format, access control — for any production pipeline question

⚙️ Azure Data Factory 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Azure Data Factory — Pipelines, Triggers & Orchestration

ADF is the orchestration layer in every enterprise Azure stack. Parameterised pipelines, ForEach loops, meta-driven ingestion, linked services, triggers, and monitoring — build one ADF pipeline that handles every data source without duplication.

ADF Architecture & Core ConceptsPipelines, activities, datasets, linked services, integration runtimes — connect to Postgres, ADLS Gen2, Databricks; understand the execution model

Copy Activity & Batch ELTPostgres → ADLS via Copy Activity; append-only raw landing; incremental load with high-watermark; idempotent date-partition overwrite

Parameterised Pipelines & ForEach LoopsDynamic content, parameter passing, ForEach over source config table — one ADF pipeline that handles 10+ sources without duplication

Triggers, Dependencies & Error HandlingSchedule triggers, tumbling window, event-based triggers, pipeline dependency chains, retry policies, failure email alerts via Logic Apps

ADF + Databricks Integration & MonitoringNotebook activity calling Databricks jobs from ADF, pass parameters, monitor activity runs, pipeline audit logs, cost tracking per activity

🎯 Milestone: Build a meta-driven ADF pipeline that ingests from any configured source — parameterised, monitored, and fully auditable — without writing a new pipeline per source

🔥 Databricks 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Databricks — PySpark Transforms, Delta Lake & End-to-End Project

Where raw data becomes analytics-ready Gold. Silver transforms on Databricks, SCD Type 2, full medallion build with Delta Lake, monitoring, and a complete end-to-end run of the EV Intelligence pipeline — from raw IoT events to Gold fact tables.

⬜ Silver Layer — Databricks Transforms

Databricks Setup & Silver TransformsCluster config, Notebooks, mount ADLS Gen2 via Unity Catalog; dedup on composite key, type cast, schema enforcement, NULL handling — PySpark DQ pipeline

Incremental Load + SCD Type 2High-watermark + MERGE on Delta for idempotent Silver loads; hash-based SCD2 for dim_vehicle — effective_from/to tracking, late-event reconciliation

🔆 Gold Layer — Analytics-Ready

Gold Layer Buildfact_charging_session (~15M rows/month), dim_vehicle (SCD2), dim_charging_station — Z-ordering + partition strategy; Databricks Workflows for scheduling

Monitoring, Failure Simulation & Observabilitypipeline_audit table, DQ gate checks post-Silver, Azure Monitor alerts, DBU cost tracking; corrupt file → dead-letter; schema drift → alert + continue

🎯 End-to-End Pipeline Run

Full Pipeline Run + Interview Walk-throughEnd-to-end: raw IoT events → Bronze (ADLS) → Silver (Databricks) → Gold; row count reconciliation, data lineage trace; "walk me through your pipeline" in 2 minutes

Azure Services Used: ADLS Gen2 · Azure Data Factory · Databricks · Delta Lake · Azure Event Stream · Key Vault · Azure Monitor · Entra ID / Service Principals

🎯 Milestone: Walk any interviewer through the complete 50M-record EV pipeline — ADLS storage design, ADF orchestration, Databricks transforms, Delta Lake Gold — without opening a slide deck

☁️ Azure Cloud Project 18 Days · 36 hrs Live 📅 View Day-by-Day Plan ▼

Azure Cloud Services — ADF · ADLS Gen2 · Databricks · Delta Lake

Build a production-grade pipeline processing 50M+ records / day from the Electric Vehicles domain — streaming IoT, batch CDC, multi-format ingestion — using ADF, ADLS Gen2, Databricks, and Delta Lake. The cloud project that turns an interview into a portfolio walkthrough.

Data Volume: IoT Telemetry 20M events/day · PostgreSQL CDC 5M rows/day · Partner CSV/ORC 10M rows/batch · Parquet Alerts 2M/day · XML Govt 500K/month ~50M+ records / day total

⚙️ Azure Cloud Services Setup — Days 1–2

Azure Setup & Cloud ServicesDatabricks workspace, ADLS Gen2, Key Vault, ADF, service principals, RBAC — hands-on with every Azure DE service from day one

Data Source Mapping & ArchitecturePostgres schema, API contracts, file formats, volume estimation — the design decisions you'll defend in system design rounds

🔶 Bronze Layer — Days 3–6 · Raw Ingestion via ADF + ADLS

Bronze Ingestion — Batch ELT with ADFPostgres + partner file drops → ADLS Delta via ADF; append-only raw landing; idempotent date-partition overwrite

Streaming IngestionAzure Event Stream → Bronze Delta at 20M EV IoT events/day; watermark, trigger intervals, offset management

Multi-Format Unified ReaderSingle parameterised reader handles Parquet, CSV, JSON, XML, ORC — format detected from metadata config table

CDC ImplementationWatermark-based change capture from PostgreSQL fleet DB — only changed rows ingested, not full dumps

⬜ Silver Layer — Days 7–10 · Databricks Transforms

Incremental Load + Idempotency on DatabricksHigh-watermark pattern + MERGE on Delta; every pipeline run produces identical output no matter how many times it runs

Silver TransformsDedup on composite key, type cast, schema enforcement, NULL handling — PySpark pipeline on Databricks with full DQ report

ADF Parameterised PipelinesForEach loops, dynamic content, meta-driven ingestion — one ADF pipeline that handles every source without duplication

D10

SCD Type 2 + Late Data + Schema Drifteffective_from/to tracking, late-event reconciliation, automated schema drift alerting — the Silver patterns interviewers probe hardest

🔆 Gold Layer — Days 11–14 · Analytics-Ready Delta Lake

D11

Gold Layer Buildfact_charging_session (~15M rows/month), dim_vehicle (SCD2), dim_charging_station — Z-ordering + partition strategy for BI

D12

Monitoring & Observabilitypipeline_audit table, automated DQ checks post-Silver, Azure Monitor alerts, cost tracking by DBU + ADF activity

D13

Failure SimulationCorrupt file → dead-letter; schema drift → alert + continue; duplicate IoT events → Silver dedup — failures introduced and fixed live

D14

End-to-End ValidationFull pipeline run raw events → Gold; row count reconciliation, DQ gate checks, data lineage trace — production readiness confirmed

⚡ Advanced — Days 15–18 · Interview-Ready

D15

Interview Walk-throughSystem design Q&A, architecture trade-off narration, resume bullet templates — "walk me through your pipeline" answered cold in 2 min

D16

Spark Performance Tuning on DatabricksAQE, shuffle optimisation, skew handling, broadcast join strategy, cluster right-sizing — the senior-level Spark questions answered

D17

Metadata-Driven FrameworkControl table design, config-based ingestion engine, reusable parameterised reader — enterprise-grade pipeline architecture

D18

CI/CD ImplementationAzure DevOps pipelines, Git branching workflow for DE deployments — how production teams actually ship data engineering code

Azure Services Used: Azure Data Factory · ADLS Gen2 · Databricks · Event Stream · Delta Lake · Key Vault · Azure Monitor · Entra ID / Service Principals

🎯 Milestone: Walk any interviewer through a 50M-record Azure pipeline — ADF design, ADLS structure, Databricks transforms, Delta Lake — without opening a slide deck

Section 3 — Second Cloud Project

Project 2 — Snowflake · Airflow · dbt Analytics Pipeline

Snowflake, dbt, and Airflow appear in 60%+ of product-company DE job descriptions — yet most candidates have never used the full stack together. Build it live from scratch, push it to GitHub, and walk into every interview as the rare candidate who actually has.

What You Build & Learn

❄️ Snowflake 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Snowflake — Architecture, Internals & Cloud Data Warehouse

Master the data warehouse that shows up in 60%+ of modern DE job descriptions. Multi-cluster architecture, micro-partitions, Streams & Tasks, Time Travel, zero-copy cloning — understand the internals well enough to defend every design decision in an interview.

Snowflake Architecture & SetupMulti-cluster virtual warehouses, compute vs storage separation, account setup, worksheets, roles & RBAC — the internals interviewers expect you to know

Storage Internals & PerformanceMicro-partitions, clustering keys, query pruning, result cache, warehouse sizing — understand why Snowflake is fast and how to keep it that way

Data Loading — Stages & COPY INTOInternal & external stages, COPY INTO patterns, file formats (CSV, Parquet, JSON), error handling, bulk vs continuous loading best practices

Streams, Tasks & Change TrackingAppend-only vs standard streams, Tasks for scheduling, stream + task CDC pattern, system$stream_has_data — the change tracking model explained

Time Travel, Cloning & Advanced FeaturesTime Travel queries & undrop, zero-copy cloning for dev/test, data sharing, Dynamic Tables intro, cost monitoring & warehouse auto-suspend

🎯 Milestone: Explain Snowflake's micro-partition model, set up a staging pipeline via COPY INTO, and implement a Stream + Task CDC flow — all from scratch

🔧 dbt 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

dbt — Data Modelling, Tests, Macros & SCD Snapshots

dbt has replaced raw SQL transforms in most modern analytics stacks. Build a full layered model — staging → intermediate → mart — with incremental materializations, SCD Type 2 snapshots, custom tests, and macros. The kind of dbt project that stands out in a portfolio review.

dbt Project Structure & Sourcesdbt project layout, profiles.yml, sources & refs, staging layer models, materializations (table, view, incremental) — first model end to end with Snowflake

Intermediate & Mart Layer DesignIntermediate models for joins & aggregations, mart layer star schema, incremental models with unique_key & merge strategy, full-refresh vs incremental

SCD Type 2 Snapshotsdbt snapshot strategy (check, timestamp), effective_from/to in Snowflake, testing snapshots, when to use snapshots vs incremental models

Tests & Data QualityGeneric tests (not_null, unique, accepted_values, relationships), singular tests, custom schema tests, dbt-expectations for advanced DQ — every model tested

Macros, Packages & DocsMacros for DRY transforms, dbt-utils package, dbt docs generate & serve, lineage graph — project polished and documented for portfolio

🎯 Milestone: A fully layered dbt project on Snowflake — staging → intermediate → mart — with snapshot SCD2, tests on every model, and a live lineage graph

🌬️ Airflow 5 Days · 10 hrs Live 📅 View Day-by-Day Plan ▼

Apache Airflow — DAGs, Operators & Pipeline Orchestration

Airflow is the orchestration layer that ties every modern DE stack together. Build real DAGs with the TaskFlow API, integrate Snowflake and dbt, handle failures and retries, and understand the scheduler well enough to answer the hard interview questions about execution semantics.

Airflow Architecture & Core ConceptsScheduler, executor, webserver, metadata DB — DAG anatomy, TaskFlow API, operators (Python, Bash, HTTP), XCom, Docker Compose local setup

Connections, Variables & Dynamic DAGsAirflow connections & variables, Jinja templating in operators, dynamic DAG generation from config — one DAG pattern that handles multiple sources

Airflow + Snowflake IntegrationSnowflakeOperator, SnowflakeSqlApiOperator, sensor patterns, SLA miss callbacks, retry policies, alerting hooks — end-to-end Snowflake pipeline from Airflow

Airflow + dbt Orchestrationdbt Cloud operator, BashOperator with dbt CLI, dbt run/test/snapshot in a DAG, backfill strategies, XCom for passing run metadata between tasks

Production DAG Patterns & MonitoringFailure handling, branching (BranchPythonOperator), task groups, DAG versioning, Airflow UI monitoring, email/Slack alerts — production-grade DAG design

🎯 Milestone: A production Airflow DAG that loads raw data into Snowflake, triggers the full dbt pipeline, and alerts on failure — all running locally with Docker Compose

❄️ Snowflake + dbt + Airflow 10 Days · 20 hrs Live 📅 View Day Plan ▼

Snowflake + dbt + Airflow — End-to-End Analytics Pipeline

Raw data to a production-grade Snowflake mart — modelled with dbt, orchestrated by Airflow. Every design decision explained and defended: why Snowflake, why dbt over raw SQL, why Airflow over cron. One GitHub project. Zero gaps in the modern analytics stack.

❄️ Snowflake — Days 1–2

Snowflake Architecture & SetupMulti-cluster virtual warehouses, compute vs storage separation, account setup, worksheets, roles & RBAC — the internals interviewers expect you to know

Snowflake InternalsMicro-partitions, clustering keys, stages (internal & external), COPY INTO, streams, tasks, time travel, zero-copy cloning — the features senior DE interviews test

🔧 dbt — Days 3–5

dbt Fundamentalsdbt project structure, profiles, sources & refs, staging layer, materializations (table, view, incremental), first models end to end

dbt Intermediate & Mart LayersIntermediate models, mart layer design, incremental models with unique_key & merge strategy, snapshot models for SCD Type 2

dbt Tests, Macros & DocsGeneric & singular tests, custom schema tests, macros for DRY transformations, dbt docs generate & serve, packages (dbt-utils, dbt-expectations)

🌬️ Airflow — Days 6–9

Airflow Core ConceptsDAG anatomy, TaskFlow API, operators (Python, Bash, HTTP), XCom, connections & variables, Airflow UI — local setup with Docker Compose

Airflow + Snowflake IntegrationSnowflakeOperator, dynamic DAG generation from config, sensor patterns, SLA callbacks, retry & alerting hooks

Airflow + dbt Orchestrationdbt Cloud operator, BashOperator with dbt CLI, dbt run/test/snapshot in DAG, backfill strategies, XCom patterns for passing run metadata

End-to-End Pipeline BuildFull pipeline live: raw CSV → Snowflake external stage → COPY INTO → dbt staging → intermediate → gold mart → Airflow DAG scheduling the full flow

🎯 Portfolio & Interview — Day 10

D10

Interview Walkthrough & GitHub PolishWalk every design decision — why Snowflake, why dbt over raw SQL, why Airflow over cron; README, lineage graph, push to GitHub portfolio

Tools Used: Snowflake · dbt Core · Apache Airflow · Docker Compose · dbt-utils & dbt-expectations · GitHub

🎯 Milestone: One live GitHub project — Snowflake + dbt + Airflow — that proves you've used the modern analytics stack end to end, not just read about it

⚡ Advanced Masterclass

Spark · Databricks Lakehouse · Airflow at Scale

Production-grade deep dives for engineers who already know the basics — the sessions that separate senior hires from junior ones at ₹30–40 LPA companies.

⚡ 22 Days Total 🎥 44 hrs Live 🏆 3 Advanced Modules

🔥 Spark Performance 7 Days · 14 hrs Live 📋 Expand ▼

Spark Performance Masterclass

Every senior DE says they "know Spark." Interviewers ask about shuffle optimisation, spill handling, broadcast joins, AQE — and most candidates go silent. After 7 days, you won't. You'll be the one asking the interviewer follow-up questions.

🔷 Databricks Lakehouse 8 Days · 16 hrs Live 📋 Expand ▼

Databricks Lakehouse Architecture

₹40 LPA+ interviews hire architects, not operators. Unity Catalog, Delta Lake internals, Delta Live Tables pipelines, medallion design at enterprise scale — the architectural fluency that makes hiring managers say "this person has actually designed systems, not just run them."

Delta Lake Internals

Transaction log, ACID guarantees, checkpoint files, Delta log compaction, time travel implementation — beyond "it's ACID" to how it actually works under the hood

Delta Optimisation — Liquid Clustering, Z-Order, Vacuum

When to use liquid clustering vs Z-ordering, OPTIMIZE strategies, VACUUM retention policies, Auto-optimize and Auto-compact — Delta performance at scale

Unity Catalog — Governance Architecture

Metastore hierarchy (account → workspace → catalog → schema), service principals, column masking, row-level filters, PII tagging, audit log design

Unity Catalog — Access Control & Data Sharing

Attribute-based access control, dynamic views vs row filters, Delta Sharing for cross-org data products, credential passthrough — design enterprise security models

Medallion Architecture at Enterprise Scale

Multi-team Bronze/Silver/Gold ownership, schema evolution strategy, SLA-driven freshness contracts, cost attribution by team, cross-domain data products

Delta Live Tables (DLT) — Declarative Pipelines

DLT vs standard notebooks, @dlt.table / @dlt.expect decorators, Bronze→Silver→Gold as code, quarantine patterns, pipeline graphs, DLT expectations for data quality enforcement — the feature interviewers ask about but most candidates can't explain

Databricks Workflows & Job Orchestration

Multi-task jobs, task dependencies, job clusters vs all-purpose clusters, repair runs, job cost monitoring, DLT pipeline triggers inside Workflows — orchestrate at scale without Airflow overhead

Lakehouse Design Mock — End-to-End Architecture

Design a governed multi-tenant lakehouse: DLT pipeline, Unity Catalog security model, Delta strategy, freshness SLAs, cost controls — whiteboard presentation with interviewer pushback live

🎯 Milestone: Design and explain a production medallion architecture on Databricks with Delta Live Tables, Unity Catalog governance, and a cost-optimised cluster strategy

🌊 Airflow at Scale 7 Days · 14 hrs Live 📋 Expand ▼

Airflow at Scale — Production Patterns

Dynamic task mapping, SLA callbacks, backfill strategies, failure recovery at 3am — this is what production Airflow actually looks like. Senior-level questions go here. Now you'll have real answers.

Airflow Architecture & Scheduler Internals

Scheduler loop, DAG parsing, task lifecycle (queued → running → success/failed), executor types overview — understand what breaks before it breaks in production

Dynamic DAGs & TaskFlow API

@task decorator, XCom with TaskFlow, expand() / map() for dynamic task mapping, parameterised DAG factories — the patterns that separate Airflow users from Airflow engineers

Dataset-Driven Scheduling & Sensors

Data-aware scheduling with Dataset triggers, ExternalTaskSensor vs DatasetSensor, FileSensor, smart polling — decouple DAG dependencies without tight coupling

SLA Monitoring, Callbacks & Alerting

sla_miss_callback, on_failure_callback, on_retry_callback design, PagerDuty/Slack integration, dead-letter queue pattern — build the 3am alert before the 3am incident

Backfill Strategies & Idempotency

backfill CLI, catchup=False patterns, ignore_first_depends_on_past, clearing failed tasks without re-running succeeded ones — backfill safely on 2-year historical data

Executors, Scaling & Cloud Deployments

LocalExecutor vs CeleryExecutor vs KubernetesExecutor trade-offs, connection pool sizing, log rotation, MWAA vs Cloud Composer vs self-managed — choose and justify the right executor

Production Hardening & Mock Incident

DAG versioning, CI/CD for DAGs, scheduler HA, metadata DB maintenance, task concurrency tuning — then a live mock: a DAG that fails nightly at 2am, diagnose and fix it

🎯 Milestone: Handle any production Airflow incident — failed backfill, SLA breach, scheduler slowdown — diagnose the root cause and explain the fix, cold, in a live senior interview

Section 4 — Interview Ready

Interview Preparation — Every Round, Every Format.

You can build pipelines. You know the tools. But when the interviewer asks "how would you handle a CDC pipeline with late-arriving data?" — do you answer with confidence, or buy time? This 13-day intensive covers every real interview format — plus AI for Data Engineers, the skill that is fast becoming the deciding factor in senior hires. Free if you enroll in All Sections. ₹2799 as a standalone.

What Interviewers Actually Ask — And How to Answer Cold

🎤 Round 1 3 Days 📅 View Topics ▼

Python, SQL & PySpark — Interview Mode

Not a review of syntax — a live simulation of round-1 questions. The patterns that trip candidates, the edge cases interviewers probe, and exactly how to frame answers to signal seniority, not just correctness.

Python Interview PatternsDSA-flavoured Python — HashMap/HashSet, two-pointer, prefix sum, file I/O, JSON parsing — every round-1 Python question type drilled live

SQL Interview PatternsWindow functions, CTEs, self-joins, GROUPING SETS — written cold on a whiteboard with no IDE, the way interviewers actually test them

PySpark Interview PatternsBroadcast joins, SCD Type 2, Window functions, execution plan reading — the specific Spark questions that appear in every DE final round

🎯 Milestone: Solve any round-1 DE question live — Python, SQL, or PySpark — in under 5 minutes, with the interviewer nodding

🔍 Round 2 4 Days 📅 View Topics ▼

Scenario & Project-Walkthrough Questions

This is where most candidates lose. Second rounds aren't about syntax — they're about judgment. CDC, SCD Type 2, idempotency, backfill, governance, failure recovery — answered in a live Q&A format with the same pressure as the real interview.

CDC & Incremental LoadsChange Data Capture design, late-arriving data, merge strategies, exactly-once guarantees — the scenario every second round includes

SCD Type 2 & Data ModelingSlowly Changing Dimensions, star schema vs Data Vault, dimensional modeling tradeoffs — drawn on a whiteboard from scratch

Idempotency, Backfilling & Rate LimitingRe-runnable pipeline design, historical backfill without duplicates, throttled API ingestion with exponential backoff

Pipeline Failure Recovery & GovernanceRetry logic, dead-letter queues, PII masking, row-level security, audit logs — everything that separates a production-grade answer from a textbook one

🎯 Milestone: Walk through any real-world scenario question — CDC design, failure recovery, governance model — with architectural confidence, not improvisation

🏗️ System Design 3 Days 📅 View Topics ▼

System Design & Architecture Rounds

The open-ended design questions no standard course prepares you for — "design a lakehouse for a fintech," "architect for schema evolution," "how do you handle data access control at scale?" We run these live, with real pushback from the interviewer role.

Lakehouse Design PatternsDesign from scratch: fintech lakehouse, multi-tenant pipeline, GDPR-compliant architecture — trade-offs presented and defended live

Schema Evolution & Real-Time ArchitectureSchema registry, backward/forward compatibility, streaming vs micro-batch trade-offs, Lambda vs Kappa — the senior architecture questions

D10

Data Warehousing & Cost ArchitectureLakehouse vs warehouse, partitioning strategy, query optimization, compute vs storage cost trade-offs — design answers that signal commercial awareness

🎯 Milestone: Design any DE system architecture on a whiteboard — storage, compute, governance, cost — and defend every decision under interviewer pressure

🤖 AI for DE 3 Days · NEW 📅 View Topics ▼

AI for Data Engineers — MCP, Models & Cloud Integration

AI is no longer a data scientist's job. Senior DE interviews are now asking how you integrate LLMs into pipelines, build AI-powered tooling, and architect AI-ready data products on Azure and Microsoft Fabric. This 3-day module is the edge most candidates don't have yet.

D11

LLM Fundamentals for EngineersModels, tokens, context windows, temperature, embeddings, RAG architecture — the vocabulary and mechanics every DE needs to work with AI teams confidently

D11

MCP Servers & Spec-Driven DevelopmentModel Context Protocol — build MCP servers that expose your pipeline tools to AI agents; spec-driven API design so LLMs can interact with your data infra reliably

D12

AI Integration on AzureAzure OpenAI Service, Cognitive Search + RAG, Azure ML pipelines feeding DE data products, AI-powered anomaly detection in your pipeline monitoring

D12

AI Integration on Microsoft FabricFabric Copilot, AI Skills, OneLake as the AI-ready data layer, Eventhouse for real-time AI context — how Fabric positions DEs at the centre of the AI stack

D13

Building AI-Powered Data ToolsLLM-assisted data quality checks, natural language to SQL pipelines, AI agents that trigger and monitor your Airflow DAGs via MCP — practical builds, not demos

D13

AI Interview Questions for DEs"How would you build a RAG pipeline on your data lake?" "How do you govern LLM access to PII data?" — the AI-flavoured DE questions appearing in 2025–26 senior rounds

🎯 Milestone: Explain how you'd integrate an LLM into a production data pipeline — architecture, governance, and cost — in a live senior interview, with zero hesitation

What's Included	🟢 Basics Section 1	🔵 Project 1 Section 2	🟡 Project 2 Section 3	🌟 Full Bundle Best Value 32% OFF
🐍 Python for Data Engineers (10 Days)	✓	—	—	✓
🗄️ SQL for Data Engineers (10 Days)	✓	—	—	✓
⚡ PySpark Fundamentals (10 Days)	✓	—	—	✓
🛠️ Weekend DE Project — Bronze → Silver → Gold (8 Weekends)	✓	—	—	✓
🏛️ DE Fundamentals — Warehouse, Lakehouse & Modelling (5 Days)	—	✓	—	✓
🗄️ ADLS Gen2 — Storage architecture & ingestion (5 Days)	—	✓	—	✓
⚙️ Azure Data Factory — Pipelines & orchestration (5 Days)	—	✓	—	✓
🔥 Databricks — PySpark, Delta Lake & transforms (5 Days)	—	✓	—	✓
☁️ Azure Cloud Project — ADF · ADLS · Databricks · Delta Lake (18 Days)	—	✓	—	✓
❄️ Snowflake — architecture, internals, streams & tasks (5 Days)	—	—	✓	✓
🔧 dbt — models, tests, macros, SCD snapshots (5 Days)	—	—	✓	✓
🌬️ Airflow — DAGs, operators, dbt + Snowflake orchestration (5 Days)	—	—	✓	✓
📊 Snowflake + dbt + Airflow End-to-End Project (10 Days)	—	—	✓	✓
⚡ Spark Performance Masterclass — shuffle, spill, AQE, skew (7 Days)	—	—	—	✓
🔷 Databricks Lakehouse Architecture — Unity Catalog, DLT, Medallion (8 Days)	—	—	—	✓
🌊 Airflow at Scale — dynamic tasks, SLA, backfill, CI/CD (7 Days)	—	—	—	✓
Price				32% OFF ₹7999 ₹11796 Save ₹3797
🎤 Interview Preparation & AI for Data Engineering 13 Days · Round 1 Screening · Scenario & Case Rounds · System Design · Mock Interviews · AI for DE: MCP Servers · Azure AI · Microsoft Fabric AI				✅ Included FREE

Follow It Alone.
Or Let Us 10X Your Speed.

This roadmap was built for one specific person

Starting Fresh in Data

Switching Into Data Engineering

Already in Data, Ready to Level Up

Questions you're probably asking right now

Basics — Python, SQL, PySpark & Your First Project

What You Build & Learn

Python for Data Engineers

SQL for Data Engineers

PySpark Fundamentals

Weekend DE Project — Bronze → Silver → Gold

Project 1 — Data Engineering Fundamentals + Azure Cloud Project

What You Build & Learn

Data Engineering Fundamentals — Warehouse, Lakehouse & Modelling

Azure Data Lake Storage Gen2 — Architecture & Ingestion

Azure Data Factory — Pipelines, Triggers & Orchestration

Databricks — PySpark Transforms, Delta Lake & End-to-End Project

Azure Cloud Services — ADF · ADLS Gen2 · Databricks · Delta Lake

Project 2 — Snowflake · Airflow · dbt Analytics Pipeline

What You Build & Learn

Snowflake — Architecture, Internals & Cloud Data Warehouse

dbt — Data Modelling, Tests, Macros & SCD Snapshots

Apache Airflow — DAGs, Operators & Pipeline Orchestration

Snowflake + dbt + Airflow — End-to-End Analytics Pipeline

Spark · Databricks Lakehouse · Airflow at Scale

Spark Performance Masterclass

Databricks Lakehouse Architecture

Airflow at Scale — Production Patterns

Interview Preparation — Every Round, Every Format.

What Interviewers Actually Ask — And How to Answer Cold

Python, SQL & PySpark — Interview Mode

Scenario & Project-Walkthrough Questions

System Design & Architecture Rounds

AI for Data Engineers — MCP, Models & Cloud Integration

Everything in One Bundle.

Still Have Questions?

Follow It Alone.Or Let Us 10X Your Speed.

This roadmap was built for one specific person

Starting Fresh in Data

Switching Into Data Engineering

Already in Data, Ready to Level Up

Questions you're probably asking right now

Basics — Python, SQL, PySpark & Your First Project

What You Build & Learn

Python for Data Engineers

SQL for Data Engineers

PySpark Fundamentals

Weekend DE Project — Bronze → Silver → Gold

Project 1 — Data Engineering Fundamentals + Azure Cloud Project

What You Build & Learn

Data Engineering Fundamentals — Warehouse, Lakehouse & Modelling

Azure Data Lake Storage Gen2 — Architecture & Ingestion

Azure Data Factory — Pipelines, Triggers & Orchestration

Databricks — PySpark Transforms, Delta Lake & End-to-End Project

Azure Cloud Services — ADF · ADLS Gen2 · Databricks · Delta Lake

Project 2 — Snowflake · Airflow · dbt Analytics Pipeline

What You Build & Learn

Snowflake — Architecture, Internals & Cloud Data Warehouse

dbt — Data Modelling, Tests, Macros & SCD Snapshots

Apache Airflow — DAGs, Operators & Pipeline Orchestration

Snowflake + dbt + Airflow — End-to-End Analytics Pipeline

Spark · Databricks Lakehouse · Airflow at Scale

Spark Performance Masterclass

Databricks Lakehouse Architecture

Airflow at Scale — Production Patterns

Interview Preparation — Every Round, Every Format.

What Interviewers Actually Ask — And How to Answer Cold

Python, SQL & PySpark — Interview Mode

Scenario & Project-Walkthrough Questions

System Design & Architecture Rounds

AI for Data Engineers — MCP, Models & Cloud Integration

Everything in One Bundle.

Still Have Questions?

Follow It Alone.
Or Let Us 10X Your Speed.