Retail Analytics
One pipeline you can explain start to finish in an interview, pushed to your own GitHub repository.
- SourcesSQL Server, CSV, APIs
- Azure Data FactoryIngestion
- SnowflakeWarehousing
- dbtTransformation
- Power BIReporting
Snowflake with dbt Training Online — Analytics Engineering Course
Key takeaways
- Learn the full modern analytics stack together — Azure Data Factory, Snowflake, dbt and Power BI — the way analytics engineering teams actually work.
- 46 hours of live training across 2 months, delivered online and in classroom, taking you from raw source data to a published Power BI dashboard.
- Covers Azure Data Factory pipelines, Snowflake architecture and virtual warehouses, dbt models, tests, snapshots and macros, and Power BI data modelling and DAX.
- Built around a real-time Retail Analytics project: SQL Server, CSV and API sources ingested with Azure Data Factory, warehoused in Snowflake, transformed with dbt and reported in Power BI.
- GitHub repository, resume preparation, mock interviews and 100+ interview questions to get you placement-ready.
Published · Last updated
46 hrsLive training across 2 months
4 toolsADF, Snowflake, dbt, Power BI
1 projectRetail Analytics, end to end
100+ questionsInterview Q&A with answers
What is Snowflake with dbt, and why are they taught together?
Snowflake is a cloud data warehouse that stores and computes over your data; dbt is the transformation layer that turns raw tables inside it into tested, documented, production-ready models using SQL. You learn dbt with Snowflake because dbt does not store data — it needs a warehouse to run in, and Snowflake is the one most teams use.
Almost every analytics engineering job advertised in India today names both. Snowflake handles storage, compute and access; dbt handles the modelling, testing, documentation and deployment that sit on top. Learning either one alone leaves you able to describe half a pipeline.
This course teaches them as a single stack, in the order a working team uses them: land raw data in Snowflake, model it in dbt, test it, document it, schedule it, and ship changes through version control without breaking what is already running.
Who is this Snowflake dbt course online for?
This Snowflake dbt course online suits SQL developers moving into analytics engineering, Power BI and Tableau developers who want to own the layer beneath their dashboards, ETL and Informatica developers modernising onto a cloud stack, and data analysts ready to take responsibility for the models they report on.
It also suits data engineers who already work in Azure or AWS and keep meeting dbt on job specifications. You do not need prior Snowflake or dbt experience. You do need to be comfortable writing SQL joins and aggregations, because dbt is written almost entirely in SQL — that is the point of it.
The course runs live online and in classroom, with the same trainer, syllabus and projects in both. Every session is recorded, so a missed class does not put you behind the batch.
Can a beginner learn dbt with Snowflake?
Yes. dbt is deliberately built on SQL rather than a new language, so anyone who can write a SELECT with joins and a GROUP BY can write a dbt model in their first week. The course opens with SQL revision and Snowflake fundamentals before any dbt project is created, so beginners start from the same line as everyone else.
What beginners find harder is not the syntax but the discipline around it: why a model is split into staging, intermediate and marts layers; why a test that fails should stop a deployment; why you never edit a table by hand once dbt owns it. Those habits are what separate someone who has followed a dbt Snowflake tutorial from someone an employer will hire.
The course teaches the habits alongside the commands, which is why it runs across 46 structured days rather than a weekend.
What does the Snowflake with dbt training cover?
The Snowflake with dbt training covers four blocks across 2 months: Azure Data Factory for ingestion, Snowflake for warehousing, dbt for transformation, and Power BI for reporting. Each block ends with a hands-on build, and the four combine into the end-to-end Retail Analytics project.
6 days
Days 1–6 · ingestion
Azure Data Factory for ingestion
Azure fundamentals and storage; Azure Data Factory pipelines, activities, linked services and datasets; a self-hosted integration runtime to connect on-premises SQL Server; copy activity, Lookup and ForEach patterns for multiple tables; dynamic datasets, parameterization and metadata-driven pipelines; incremental loading with watermarks; error handling, monitoring and alerts. Six days, building the pipelines that land data in Snowflake.
25 days
Days 7–31 · warehousing
Snowflake architecture and administration
Data warehouse concepts, OLTP versus OLAP and ETL versus ELT; Snowflake architecture — storage, compute and cloud services; databases, schemas, tables and SQL (DDL, DML, MERGE, transactions); virtual warehouses and cost optimization; file formats, internal and external stages, COPY INTO and Snowpipe; semi-structured data with VARIANT, OBJECT, ARRAY and FLATTEN; Time Travel, fail-safe and zero-copy cloning; Streams for CDC and Tasks for scheduling; query profiling, micro-partitions and clustering; users, roles and RBAC; secure views, materialized views, dynamic tables, UDFs, stored procedures and administration. Twenty-five days — the largest block in the course.
10 days
Days 32–41 · transformation
dbt from first model to production
Analytics engineering fundamentals and dbt architecture; project structure, sources, models, ref() and source(); materialisations — view, table, incremental and ephemeral; seeds, snapshots and SCD Type 2; data tests and data quality; Jinja, variables, macros and packages; incremental models and SCD Type 1; documentation and lineage; Git, dbt Cloud and CI/CD; and an end-to-end Bronze → Silver → Gold pipeline. Ten days, from first model to production.
5 days
Days 42–46 · reporting
Power BI reporting
Connecting Power BI to Snowflake with Import and DirectQuery; star schema, fact and dimension modelling; DAX measures, KPIs and time intelligence; dashboard development and visualisation best practices; publishing and an end-to-end reporting project. Five days, closing the loop from warehouse to dashboard.
Analytics engineer or data engineer — which does this course make you?
Both roles are hiring for this stack, and the course covers the overlap. An analytics engineer owns the transformation layer — dbt models, tests, documentation and the semantic definitions the business reports on. A data engineer owns everything that gets data into the warehouse and keeps it running: ingestion, orchestration, performance and cost.
In most Indian teams the two titles blur, and the job description asks for both. That is why this dbt analytics engineering training also covers Snowflake data engineering with dbt rather than teaching dbt in isolation: you learn to load data into Snowflake and to model it once it is there.
If you are aiming squarely at analytics engineering roles, the dbt blocks and the certification module are the ones to over-invest in. If you are aiming at data engineering, spend the extra time on Snowpipe, Streams and Tasks, warehouse sizing and cost control.
What real-time scenarios will you work on?
The training closes with an end-to-end Retail Analytics project: SQL Server, CSV and API sources ingested through Azure Data Factory, warehoused in Snowflake, transformed with dbt and reported in Power BI. Alongside it, the scenarios below are taken from live warehouse work rather than clean tutorial data, and each one has appeared in interview rounds for this stack.
Incremental loads that must not double-count
You build a model that processes only new rows, then handle the case where yesterday's batch arrives twice — the single most common production incident on a dbt project.
Slowly changing dimensions with dbt snapshots
A customer changes address; the order placed last March must still report against the old one. You implement the snapshot, then explain the choice between type 1 and type 2 the way an interviewer will ask you to.
Late-arriving and out-of-order data
Records land after the window they belong to has already been reported. You design the model so restating a period is routine rather than an emergency.
A failing test that blocks a deployment
A uniqueness test breaks on a pull request. You trace it back through the lineage graph to the source, fix it, and ship — which is exactly what the job is on most days.
Which certifications does this course prepare you for?
The syllabus maps to two certifications: the dbt Analytics Engineering Certification, which tests modelling, testing, deployment and dbt project structure, and Snowflake's SnowPro Core Certification, which tests warehouse architecture, loading, security and performance. Guidance for both runs alongside the training.
Eclasess covers the exam objectives, the question patterns and a preparation plan, and the mock interviews double as certification revision. Both exams are booked and paid for separately with dbt Labs and Snowflake — the course fee does not include exam vouchers.
Snowflake and dbt certification training is worth taking together rather than sequentially. The two exams overlap on materialisation, incremental logic and warehouse behaviour, and preparing for one shortens preparation for the other.
Full syllabus, module by module
Ten modules across 46 days, in the order a working analytics team builds a pipeline.
Module 1 — Azure Data Factory Days 1–6
- Azure cloud introduction, storage account and ADLS Gen2
- ADF pipelines, activities, linked services and datasets
- Self-hosted integration runtime
- Copy data from on-prem SQL Server
- Lookup and ForEach for multiple tables
- Dynamic datasets and parameterization
- Incremental loading with watermarks
- Error handling, monitoring and alerts
Module 2 — Snowflake foundations Days 7–13
- Data warehouse concepts, OLTP vs OLAP, ETL vs ELT
- Snowflake architecture: compute, storage, cloud services
- Databases, schemas, tables
- SQL in Snowflake (DDL, DML, MERGE, transactions)
- Virtual warehouses and cost optimization
- File formats (CSV, JSON, Parquet, Avro)
- Internal stages
Module 3 — Snowflake loading & semi-structured data Days 14–19
- External stages (Azure Blob, AWS S3)
- COPY INTO, validation and error handling
- Semi-structured data (VARIANT, OBJECT, ARRAY, FLATTEN)
- Time Travel and fail-safe
- Zero-copy cloning
- Streams (CDC)
Module 4 — Snowflake automation & performance Days 20–25
- Tasks and scheduling
- Snowpipe
- Query profile and performance tuning
- Micro-partitions and clustering
- Users, roles and RBAC
- Secure views and data sharing
Module 5 — Snowflake advanced objects & admin Days 26–31
- Views, materialized views and dynamic tables
- User-defined functions (UDFs)
- Stored procedures
- Administration and resource monitors
- Best practices and real-time scenarios
- Revision, interview questions and mini project
Module 6 — dbt fundamentals Days 32–35
- Analytics engineering, dbt architecture, installation
- Project structure, sources, models, ref() and source()
- Materializations (view, table, incremental, ephemeral)
- Seeds, snapshots and SCD Type 2
Module 7 — dbt testing & advanced modelling Days 36–39
- Data tests and data quality
- Jinja, variables, macros and packages
- Incremental models and SCD Type 1
- Documentation and lineage
Module 8 — dbt deployment Days 40–41
- Git, dbt Cloud and CI/CD
- End-to-end Bronze → Silver → Gold pipeline
Module 9 — Power BI Days 42–46
- Connect Power BI to Snowflake, Import vs DirectQuery
- Star schema, fact and dimension modelling
- DAX measures, KPIs and time intelligence
- Dashboard development and visualization best practices
- End-to-end reporting project, publishing, resume and mock interview
Module 10 — Real-time project & career prep Throughout
- End-to-end Retail Analytics solution using SQL Server/CSV/APIs as sources, Snowflake for warehousing, dbt for transformations and Power BI for dashboards
- GitHub repository
- Resume preparation
- Mock interviews
- 100+ interview questions
Course facts
| Total duration | 46 hours across 2 months of live training, covering Azure Data Factory, Snowflake, dbt and Power BI |
|---|---|
| Training schedule | Monday to Friday, one live class daily across 2 months |
| Placement schedule | Included throughout the 2 months — resume prep, mock interviews and 100+ interview questions |
| Mode | Live online (available anywhere) and classroom in KPHB, Hyderabad |
| Recordings | Every class recorded, notes shared the same day |
| Projects | One end-to-end Retail Analytics project spanning Azure Data Factory, Snowflake, dbt and Power BI |
| Deliverables | End-to-end project · GitHub repository · resume preparation · mock interviews · 100+ interview questions |
| Prerequisites | Working SQL. Snowflake and dbt are taught from fundamentals |
| Fee | ₹15,000 — correct as of September 2026, for the 46-hour (46-day) program |
| Learners trained | [CONFIRM] across all Eclasess courses |
Snowflake and dbt compared
Snowflake is where the data lives; dbt is how it gets modelled once it is there. This course teaches both, because analytics engineering roles ask for both.
| Aspect | Snowflake | dbt |
|---|---|---|
| What it is | Cloud data warehouse — storage and compute | SQL transformation and testing framework |
| Language | SQL, plus Python via Snowpark | SQL, plus Jinja templating |
| Stores data | Yes | No — it runs inside Snowflake |
| What you build | Databases, schemas, warehouses, pipes, streams, tasks | Models, tests, snapshots, seeds, macros, docs |
| Handles loading | Snowpipe, COPY INTO, external stages | No — dbt transforms data already loaded |
| Version control | Not natively | Git-native; every model is a file |
| Certification | SnowPro Core | dbt Analytics Engineering Certification |
| Role it maps to | Data engineer | Analytics engineer |
Frequently asked questions
Is this Snowflake dbt course available online?
Yes. The course runs as live online classes, available anywhere, and as classroom training in KPHB, Hyderabad, with the same trainer, syllabus, projects and placement program in both. Every session is recorded.
How long is the Snowflake with dbt training?
46 hours in total, delivered as one-hour live classes across 2 months, Monday to Friday. The program covers Azure Data Factory, Snowflake, dbt and Power BI.
Do I need to know Snowflake before learning dbt?
No. The course covers Snowflake architecture, warehouses, loading and security from fundamentals before the first dbt project is created.
Can I learn dbt with Snowflake as a complete beginner?
You need working SQL — joins, aggregations, subqueries. Everything else is taught from scratch. dbt is written in SQL, so SQL is the only real prerequisite.
What is the difference between Snowflake and dbt?
Snowflake stores and computes over your data. dbt transforms it — turning raw tables into tested, documented models. dbt does not store anything; it runs inside Snowflake.
Does the course cover dbt Core or dbt Cloud?
Both. dbt Core is taught first so you understand what is happening under the interface, then dbt Cloud for scheduling, environments and team workflows.
What real-time scenarios does the training cover?
Incremental loads that must not double-count, slowly changing dimensions with snapshots, late-arriving and out-of-order data, and failing tests that block a deployment.
Which certification should I take after this course?
The dbt Analytics Engineering Certification if you are targeting analytics engineering roles, and Snowflake SnowPro Core if you are targeting data engineering. Guidance for both is included; exam fees are paid separately.
What jobs can I apply for after this course?
Analytics Engineer, Snowflake Developer, Data Engineer, ETL/ADF Developer and Power BI Developer roles. The end-to-end project and 100+ interview questions are built around the technical rounds these roles use.
Does Eclasess provide placement assistance?
Yes. The program includes a GitHub repository of your project work, resume preparation, mock interviews and 100+ interview questions with answers.
Will I build real projects?
Yes — one end-to-end Retail Analytics project spanning the full stack: Azure Data Factory for ingestion, Snowflake for warehousing, dbt for transformation and Power BI for reporting, so you can explain the full architecture in an interview.
Is this a dbt Snowflake tutorial for beginners, or a full course?
It is a full course. A dbt Snowflake tutorial for beginners shows you the commands; this course covers the commands plus the modelling layers, testing discipline and deployment habits an employer expects, across 46 structured days that also include Azure Data Factory and Power BI.
Does the course include real time scenarios training?
Yes. Snowflake dbt real time scenarios training runs through the whole placement program — incremental loads, slowly changing dimensions, late-arriving data and failing tests that block a deployment.
Is there a free demo class?
Yes. Eclasess runs a free demo before every batch so you can meet the trainer and see the syllabus before paying.
What is the course fee?
₹15,000 — the fee covers all 46 hours, recordings, notes, the end-to-end project, GitHub repository, resume preparation, mock interviews and 100+ interview questions.
When does the next batch start?
The next three batches are listed below with start date, timing and mode. The table is refreshed monthly.
| Start date | Timing | Mode |
|---|---|---|
| [PUBLISH DATE] | [TIMING] | Live online |
| [PUBLISH DATE] | [TIMING] | Classroom, KPHB |
| [PUBLISH DATE] | [TIMING] | Live online |
Try a free demo class before you pay
Meet the trainer, see the syllabus and ask your questions. New online and classroom batches open regularly.