Read data fluently
Reason about grain, keys, relationships, and the shape of any dataset.
Architecture Path · Foundations · Step 1
Data Foundation
The rigorous core every senior data role rests on. Build genuine command of data literacy, SQL, data modeling, metadata, data quality, and the basics of pipelines and governance, so the architecture, product, and platform work later in the path sits on solid ground.
8
Modules
40
Chapters
Core
Skills
Capstone
Data model + quality plan
The journey
Eight modules, one arc
The eight modules build cumulatively toward a real capstone. Watch the work move, and the value compound, at every stage.
Each module builds the capability the next one depends on, ending in a portfolio-ready capstone.
Outcomes
What you'll be able to do
Model with intent
Move from conceptual to logical to physical models deliberately.
Write real SQL
Query, join, aggregate, and shape data to answer real questions.
Make data trustworthy
Apply metadata, quality checks, and lineage thinking from the start.
Move data safely
Understand ETL and ELT patterns and where each fits.
Speak governance
Use the language of ownership, stewardship, and controlled access.
Curriculum
8 modules, 40 chapters, ending in a capstone
Eight modules of five chapters each, sequenced so the material builds cumulatively to a real, portfolio-ready capstone. Expand any module for its focus and lessons.
01 Data Literacy and the Data Landscape
Build the mental model of what data is and how it flows through an organization.
- What data is: records, entities, attributes, and grain
- OLTP versus OLAP, and the shape of transactional and analytical data
- The data landscape: sources, stores, pipelines, and consumers
- Structured, semi-structured, and unstructured data
- How a request for insight travels from source to decision
02 Relational Modeling and SQL
Model relational data well and query it with confidence.
- Tables, keys, relationships, and referential integrity
- Normalization and when to denormalize
- SQL foundations: select, filter, sort, and project
- Joins, grouping, and aggregation for real questions
- Subqueries, window functions, and set operations
03 Dimensional and Analytical Modeling
Model data for analytics with facts, dimensions, and clear grain.
- Facts, dimensions, and the star schema
- Choosing grain and avoiding fan and chasm traps
- Slowly changing dimensions and history
- Conformed dimensions and shared definitions
- Modeling a small analytical subject area end to end
04 Metadata, Cataloging and Definitions
Make data understandable through metadata and shared definitions.
- Business versus technical metadata
- Data catalogs and why discoverability matters
- Business glossaries and canonical definitions
- Tagging, classification, and ownership metadata
- Documenting a dataset so others can trust and reuse it
05 Data Quality and Trust
Measure and improve data quality so consumers can rely on it.
- Dimensions of data quality: accuracy, completeness, timeliness, consistency
- Profiling data to find real problems
- Validation rules, checks, and quality gates
- Root-cause thinking for recurring quality issues
- Building a simple data-quality scorecard
06 Data Integration and Pipelines
Understand how data moves and where each integration pattern fits.
- ETL and ELT, and the trade-off between them
- Batch, streaming, and change-data-capture at a high level
- APIs and files as integration interfaces
- Idempotency, reconciliation, and handling failure
- Sketching a pipeline from a source to an analytical store
07 Governance, Security and Access Basics
Speak the language of governance, ownership, and controlled access.
- Data ownership and stewardship, and why they matter
- Access control, least privilege, and sensitive data
- Lineage: tracing data from source to consumption
- Privacy and regulatory basics every practitioner should know
- How governance enables, rather than blocks, self-service
08 Capstone: Build a Trusted Data FoundationCapstone
Assemble the skills into a small but complete, documented data foundation.
- Choose a subject area and profile its source data
- Design conceptual, logical, and physical models for it
- Define metadata, a glossary, and quality checks
- Sketch the integration from source to an analytical model
- Capstone: present a documented, quality-checked data model as a portfolio artifact
Who it's for
Built for Data professionals building a rigorous core
Career-changers into data
Professionals entering data who need a rigorous, non-superficial base.
Self-taught practitioners
Those with practical skills who want to close the gaps in fundamentals.
Analysts and engineers
People who work with data daily and want the modeling and quality depth.
Future architects
Anyone on the path to architecture who needs the core rock-solid first.
Program formats
How you learn
Self-paced
Work through it at your own pace, with lifetime access to every module and the capstone.
Mentor-led cohort
A guided cohort with live sessions, reviews, and a peer group working the same path.
Private corporate
A closed cohort for your team, tailored to your platforms, domains, and priorities.
Portfolio-building
Every module produces an artifact; the capstone assembles them into a portfolio deliverable.
For teams
Bring it to your team
Run Data Foundation as a private, closed cohort tailored to your platforms, domains, and priorities, as part of building the architecture capability your organization needs.
Related programs
Explore the path
FAQ
Data Foundation - answered
Who is Data Foundation for?
Anyone who needs a rigorous, non-superficial base in data: career-changers, self-taught practitioners closing gaps, analysts and engineers, and future architects who want the fundamentals solid before moving up the path.
Do I need prior experience?
No. It builds from first principles, though it moves at a pace that assumes you are a motivated professional. It is the natural first step in the data-architecture path.
Is it hands-on?
Yes. You profile real data, write SQL, build models, and produce a documented capstone artifact you can show.
How does it fit the architecture path?
It is step one. Everything later, from data products and platforms to enterprise architecture, assumes the modeling, quality, and metadata command this program builds.
Self-paced or cohort?
Both. Self-paced with lifetime access is standard; mentor-led and private corporate cohorts are available.
Is there a certificate?
Yes, a Durga Analytics certificate of completion with a digital badge, plus the capstone artifact itself.
Take the next step on the path
Enrol, enquire, or explore the full IC-to-Head of Data Architecture path.