Technology trainings

Hands-on training in the platforms teams actually use.

Live, instructor-led programs across Snowflake, Databricks, Microsoft Fabric, Data Vault 2.0, modeling, engineering and AI for data teams. 30% concepts, 70% hands-on, with real business scenarios.

Training catalogue

6 trainings

SnowflakeIntermediate

Snowflake Data Engineering Bootcamp

For data engineers and SQL developers who want to build reliable, cost-aware pipelines on Snowflake.

Duration
24 hrs
Mode
Live online
Starts
To be announced
Syllabus · 5 modules · Sat–Sun, 10am–1pm IST
  1. Snowflake architecture and setup · 4h
    Storage, compute and services layers; warehouses; databases and schemas; roles and cost basics
  2. Loading data · 5h
    Stages, file formats, COPY INTO, Snowpipe, semi-structured data with VARIANT
  3. Transformations and ELT · 6h
    Streams, tasks, dynamic tables, MERGE patterns, incremental loads, time travel
  4. Security and governance · 4h
    RBAC design, masking and row access policies, tagging, data sharing
  5. Performance, cost and capstone · 5h
    Clustering, query profile, resource monitors; end-to-end capstone reviewed by the trainer
You will be able to: Design Snowflake databases, schemas and warehouses for a real workload · Load batch and continuous data with COPY, stages and Snowpipe · Build incremental ELT with streams, tasks and dynamic tables · Apply role-based access, masking and cost controls · Review AI-assisted SQL critically before it runs
Fees on requestEnquire for fees and the next batch
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DatabricksIntermediate

Databricks Lakehouse Engineering with PySpark

Build medallion-architecture pipelines on Databricks with PySpark and Delta Lake.

Duration
30 hrs
Mode
Live online
Starts
To be announced
Syllabus · 5 modules · Weekday evenings, 7–9pm IST
  1. Lakehouse fundamentals · 4h
    Workspace, clusters, notebooks, Delta Lake basics, medallion architecture
  2. PySpark for data engineering · 8h
    DataFrames, joins, window functions, UDF trade-offs, performance basics
  3. Delta Lake in depth · 6h
    MERGE, change data feed, schema evolution, OPTIMIZE and Z-ORDER, time travel
  4. Governance and quality · 5h
    Unity Catalog, expectations and data quality checks, lineage
  5. Orchestration and capstone · 7h
    Workflows, parameters, monitoring; capstone pipeline reviewed by the trainer
You will be able to: Build medallion (Bronze, Silver, Gold) pipelines with PySpark and Delta Lake · Handle incremental and historical loads, schema evolution and data quality checks · Govern data with Unity Catalog · Orchestrate and monitor jobs with Databricks Workflows
Fees on requestEnquire for fees and the next batch
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AI for Data TeamsIntermediate

AI for Data Teams: LLMs, RAG and Reviewing AI Output

Use AI productively in data work, and know when not to trust it.

Duration
16 hrs
Mode
Live online
Starts
To be announced
Syllabus · 4 modules · Sat–Sun, 10am–2pm IST
  1. LLM basics for data people · 3h
    How models work, strengths, failure modes, hallucination
  2. Prompting for data work · 4h
    SQL generation, documentation, data quality rules, STTM drafts
  3. Retrieval and vector search · 4h
    Embeddings, chunking, RAG over metadata and documentation
  4. Safe use and review · 5h
    Data privacy, human gates, review checklists, measuring quality; capstone reviewed by the trainer
You will be able to: Explain how LLMs work and where they fail · Design prompts for SQL, documentation and data quality tasks · Build a simple retrieval (RAG) pipeline over your metadata · Set up review gates so AI output is checked before use · Keep personal and sensitive data out of AI tools
Fees on requestEnquire for fees and the next batch
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Microsoft FabricBeginner

Microsoft Fabric for Data Engineers

Get productive with Microsoft Fabric: Lakehouse, Warehouse, pipelines and Power BI.

Duration
20 hrs
Mode
Live online
Starts
To be announced
Syllabus · 5 modules · Sat–Sun, 2–5pm IST
  1. Fabric and OneLake · 3h
    Workspaces, items, capacities, OneLake shortcuts
  2. Ingestion · 5h
    Data Factory pipelines, Dataflows Gen2, notebooks
  3. Lakehouse and Warehouse · 6h
    Delta tables, SQL endpoint, Warehouse modelling, T-SQL transformations
  4. Semantic models and reporting · 4h
    Direct Lake, measures, Power BI reports
  5. Copilot and review · 2h
    Where Copilot helps, how to verify its suggestions
You will be able to: Navigate Fabric workspaces, OneLake and capacities · Ingest data with pipelines and dataflows into a Lakehouse · Model a Warehouse and build a semantic model for Power BI · Use Copilot features and check their output before relying on it
Fees on requestEnquire for fees and the next batch
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Data Vault 2.0Advanced

Data Vault 2.0 Modelling in Practice

Model Raw and Business Vault structures that stay auditable as sources change.

Duration
24 hrs
Mode
Hybrid
Starts
To be announced
Syllabus · 5 modules · Two weekends, 10am–4pm IST
  1. Why Data Vault · 3h
    Architecture layers, Raw vs Business Vault, when to use it
  2. Core structures · 7h
    Business keys, Hubs, Links, Satellites, hash keys, naming standards
  3. Advanced patterns · 6h
    Effectivity and multi-active Satellites, same-as links, hierarchical links
  4. Query layer · 4h
    PIT and Bridge tables, information marts
  5. Case study review · 4h
    Banking case study; design review with the trainer
You will be able to: Identify business keys and design Hubs, Links and Satellites · Model history, effectivity and multi-active data correctly · Build PIT and Bridge tables for efficient querying · Review AI-drafted Data Vault models against the business
Fees on requestEnquire for fees and the next batch
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Dimensional ModelingBeginner

Dimensional Modelling for Analytics

Design star schemas that make reporting fast, consistent and trustworthy.

Duration
16 hrs
Mode
Live online
Starts
To be announced
Syllabus · 4 modules · Weekday evenings, 7–9pm IST
  1. Foundations · 3h
    Business processes, grain, facts and dimensions
  2. Facts · 4h
    Transaction, periodic and accumulating snapshots; additivity
  3. Dimensions · 5h
    Conformed dimensions, SCD types 1, 2 and 3, junk and role-playing dimensions
  4. Delivery · 4h
    Bus matrix, semantic models, modelling review with the trainer
You will be able to: Declare the grain and design fact tables · Design conformed dimensions and slowly changing dimensions · Build a bus matrix for an enterprise data warehouse · Translate a star schema into a semantic model
Fees on requestEnquire for fees and the next batch
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