---- Modern Data Engineering · Instructor-Led Workshop
Build Data
Pipelines that Scale
at Speed..
Pipelines that Scale
at Speed..
From ingestion to transformation to orchestration — master dbt, Spark, Snowflake, and Azure Data Factory across three progressive levels. Build production-grade data platforms and land roles that matter.
dbt CertifiedDatabricks AssociateSnowPro Core
3×Workshop Levels
40+Hands-on Labs
2×Microsoft Exams Covered
96%PL-300 Pass Rate
---- Three Levels
Choose Your
Data Engineering Level.
Data Engineering Level.
Level 01 — Beginner
SQL, Python & Data Fundamentals
Start your data engineering journey with the tools every data professional relies on — advanced SQL, Python for data, pandas, and cloud storage fundamentals. Built for career changers and graduates entering the field.
Key Modules
Advanced SQL — window functions, CTEs, performance tuning
Python for data — pandas, polars, and data wrangling
Cloud storage basics — Azure ADLS, S3, GCS fundamentals
Introduction to dbt — models, sources, and testing
Data modelling concepts — star schema and dimensional design
dbt FundamentalsSnowPro Core Prep
GBP
£549
per person
/
INR
₹29K
per person
⭐ Most Popular
Level 02 — Intermediate
Modern Data Stack & Lakehouse
Master the production-grade modern data stack — dbt Cloud, Snowflake, Azure Data Factory, and Delta Lake. Build end-to-end ELT pipelines that are tested, documented, and CI/CD-ready.
Key Modules
dbt Cloud — advanced models, macros, packages, exposures
Snowflake — warehouses, data sharing, performance optimisation
Azure Data Factory — pipelines, triggers, and ADF patterns
Delta Lake — ACID transactions, time travel, and Z-ordering
Pipeline orchestration with Apache Airflow & Dagster
Data quality — Great Expectations, dbt tests, and alerting
dbt Developer CertSnowPro CoreDP-203 Foundation
GBP
£895
per person
/
INR
₹49K
per person
Level 03 — Advanced
Spark, Streaming & Data Platform Engineering
Go deep on distributed computing, real-time streaming, and enterprise data platform design. Build Databricks-native pipelines with Unity Catalog governance, Kafka streaming architectures, and Data Vault 2.0 modelling.
Key Modules
Apache Spark & PySpark — optimisation, broadcast, partitioning
Databricks — Unity Catalog, Asset Bundles, Workflows
Apache Kafka & Spark Structured Streaming
Data Vault 2.0 — hubs, links, satellites, and business vault
Platform engineering — IaC, Terraform, CI/CD for data
Data mesh principles and federated governance at scale
Databricks Associate DEDP-203
GBP
£1,195
per person
/
INR
₹69K
per person
Level 01 — Beginner
SQL, Python & Data Fundamentals
Start your data engineering journey with the tools every data professional relies on — advanced SQL, Python for data, pandas, and cloud storage fundamentals. Built for career changers and graduates entering the field.
Key Modules
Advanced SQL — window functions, CTEs, performance tuning
Python for data — pandas, polars, and data wrangling
Cloud storage basics — Azure ADLS, S3, GCS fundamentals
Introduction to dbt — models, sources, and testing
Data modelling concepts — star schema and dimensional design
dbt FundamentalsSnowPro Core Prep
GBP
£549
per person
/
INR
₹29K
per person
⭐ Most Popular
Level 02 — Intermediate
Modern Data Stack & Lakehouse
Master the production-grade modern data stack — dbt Cloud, Snowflake, Azure Data Factory, and Delta Lake. Build end-to-end ELT pipelines that are tested, documented, and CI/CD-ready.
Key Modules
dbt Cloud — advanced models, macros, packages, exposures
Snowflake — warehouses, data sharing, performance optimisation
Azure Data Factory — pipelines, triggers, and ADF patterns
Delta Lake — ACID transactions, time travel, and Z-ordering
Pipeline orchestration with Apache Airflow & Dagster
Data quality — Great Expectations, dbt tests, and alerting
dbt Developer CertSnowPro CoreDP-203 Foundation
GBP
£895
per person
/
INR
₹49K
per person
Level 03 — Advanced
Spark, Streaming & Data Platform Engineering
Go deep on distributed computing, real-time streaming, and enterprise data platform design. Build Databricks-native pipelines with Unity Catalog governance, Kafka streaming architectures, and Data Vault 2.0 modelling.
Key Modules
Apache Spark & PySpark — optimisation, broadcast, partitioning
Databricks — Unity Catalog, Asset Bundles, Workflows
Apache Kafka & Spark Structured Streaming
Data Vault 2.0 — hubs, links, satellites, and business vault
Platform engineering — IaC, Terraform, CI/CD for data
Data mesh principles and federated governance at scale
Databricks Associate DEDP-203
GBP
£1,195
per person
/
INR
₹69K
per person
---- What You'll Learn
Deep-Dive
Curriculum Topics.
Curriculum Topics.
Every workshop is built around real production scenarios. No hello-world pipelines — every lab uses realistic datasets and enterprise-grade tooling.
SQL & Python Foundations
📋 All Levels — Beginner Track
Advanced SQL — window functions, LAG/LEAD, recursive CTEs, explain plans
Python for data — pandas, polars, and efficient data wrangling at scale
Cloud storage patterns — ADLS Gen2, S3, partitioning and file formats
Data modelling fundamentals — star schema, snowflake schema, SCD types
Introduction to dbt — sources, models, tests, and documentation
Version control for data — Git workflows, branching, and PR reviews
dbt & Modern Transformation
📋 dbt Developer Certification
dbt advanced — macros, Jinja templating, packages, and custom generic tests
Snapshots & SCD Type 2 — tracking slowly changing dimensions with dbt
Exposures, metrics, and the semantic layer for self-serve analytics
dbt CI/CD — GitHub Actions, slim CI, and blue/green deployments
dbt Cloud IDE vs CLI — environments, job scheduling, and alerting
dbt testing strategies — schema tests, data tests, singular tests
Snowflake & Cloud Data Warehouse
📋 SnowPro Core
Snowflake architecture — virtual warehouses, micro-partitioning, and clustering
ELT patterns — Snowpipe, Streams, and Tasks for continuous data loading
Data sharing — secure shares, data exchange, and reader accounts
Snowpark for Python — pushing Python code into Snowflake for scalability
Cost optimisation — result caching, auto-suspend, and credit monitoring
Security — role-based access, column masking, and row-level security
Apache Spark & Databricks
📋 Databricks DE Associate
Spark architecture — driver, executors, DAGs, stages, and the Spark UI
PySpark optimisation — broadcast joins, partition tuning, and caching strategy
Delta Lake — ACID transactions, schema enforcement, and time travel
Databricks Workflows — task orchestration, job clusters, and notebooks
Unity Catalog — data governance, fine-grained access, and lineage
Delta Live Tables — declarative pipeline authoring with DLT expectations
Kafka & Real-Time Streaming
📋 Advanced Track
Kafka architecture — brokers, topics, partitions, consumer groups, and offsets
Kafka Connect — source and sink connectors for zero-code integration
Schema Registry — Avro, Protobuf, and schema evolution patterns
Spark Structured Streaming — watermarking, windowing, and late data handling
Exactly-once semantics — idempotent producers and transactional consumers
Production patterns — dead letter queues, compaction, and Kafka Streams
DataOps & Governance
📋 Enterprise Track
Data quality at scale — Great Expectations, dbt tests, and Monte Carlo observability
Infrastructure as Code — Terraform for Snowflake, Databricks, and ADF
DataOps CI/CD — automated testing, deployment pipelines, and rollback strategies
Data mesh architecture — domain ownership, data products, and federated governance
Data cataloguing — Azure Purview, Atlan, and data lineage visualisation
Compliance & security — GDPR in data platforms, masking, and audit trails
---- Tech Stack
Tools You'll
Master in Every Lab.
Master in Every Lab.
Every session uses the real tools used by data teams at scale. No toy datasets. No simulators.
dbt Core & Cloud
Build modular, tested, documented SQL transformation layers with dbt. Covers models, macros, seeds, sources, snapshots, exposures, and CI/CD with GitHub Actions.
All levels
Snowflake Data Cloud
Virtual warehouses, result caching, zero-copy cloning, dynamic tables, and Snowpark for Python. Build performant, cost-efficient analytical workloads on Snowflake.
Intermediate+
Apache Spark & PySpark
Distributed data processing at petabyte scale. Covers DataFrames, RDDs, Structured Streaming, broadcast joins, shuffle partitioning, and Spark UI debugging.
Advanced track
Databricks & Delta Lake
The unified data intelligence platform. Unity Catalog governance, Workflows, Asset Bundles, Delta Live Tables, and ACID transactions with time travel on Delta Lake.
Intermediate+
Azure Data Factory
Cloud-native ETL/ELT orchestration on Azure. Copy activity, data flows, triggers, linked services, and integration patterns with ADLS Gen2 and Synapse Analytics.
All levels
Apache Kafka & Flink
Event-driven data pipelines and real-time stream processing. Kafka Connect, Schema Registry, consumer groups, and Flink for stateful streaming transformations.
Advanced track
Apache Airflow & Dagster
Pipeline orchestration and observability. Airflow DAGs, operators, sensors, XComs, and Dagster assets for modern declarative data pipeline management.
Intermediate+
Data Quality & Observability
Great Expectations, dbt tests, Monte Carlo, and custom anomaly detection. Catch data issues before they reach dashboards and automate quality gates in CI/CD.
All levels
---- Why Data Engineering
Why Learn
Data Engineering Now?
Data Engineering Now?
01
The AI Revolution Runs on Data Pipelines
Every AI model in production needs reliable, clean, and well-governed data. As AI adoption explodes, demand for the engineers who build the pipelines feeding these models has never been higher. Data Engineering is the unsexy foundation of every AI headline.
02
Senior DE Roles Command £90k–£150k
Senior Data Engineers with Spark, dbt, and Snowflake skills regularly clear £90k–£130k in London, and $130k–$180k in the US. The combination of cloud + modern stack is the premium skill bracket in the data market right now.
03
The Modern Data Stack Replaced Legacy ETL
SSIS, Informatica, and hand-rolled Python ETL are being replaced. dbt, Snowflake, Databricks, and Airflow are now the standard. Engineers who can operate the modern stack are in demand at both startups and enterprise scale.
78%
of Fortune 500 Fortune 500 companies have adopted or are actively migrating to a modern data stack — creating tens of thousands of open roles globally that outpace supply.
78%
of Fortune 500 Fortune 500 companies have adopted or are actively migrating to a modern data stack — creating tens of thousands of open roles globally that outpace supply.
04
Data Mesh & Governance Are Now Required Skills
As organisations scale data platforms, federated ownership, data products, and Unity Catalog-style governance are becoming core competencies, not nice-to-haves. This workshop covers the architectural thinking, not just the tooling.
--- Learner Stories
What Our
Graduates Say.
Graduates Say.
“
The dbt + Snowflake lab on day two completely changed how I think about transformation layers. I went back to work and refactored our entire SQL codebase into dbt models with proper tests within two weeks. Night and day improvement.
“
I passed the Databricks Data Engineer Associate exam one month after the advanced workshop. The instructor had clearly sat the exam multiple times and knew exactly where people lose marks. The Spark optimisation section alone was worth the course fee.
“
We trained 22 analysts and junior engineers as a corporate batch. Within a quarter we had deprecated our legacy Informatica pipelines and moved everything to dbt + ADF. The team now ships pipeline changes in hours, not weeks. Transformative investment.
