Build the data foundation enterprise AI depends on.
Learn to turn databases, files, APIs, events and enterprise documents into governed data products for analytics and AI. Build with Python, PostgreSQL, Power BI, PySpark, Delta Lake, Airflow, Databricks and Microsoft Fabric — then prove freshness, quality, lineage and recoverability.
Data Engineering is the discipline of designing, building and operating systems that ingest, transform, validate, govern and serve data. A Data Engineer makes data available in the right form, at the required time, with evidence that it is trustworthy and authorised.
A stale dashboard can mislead a report. A stale or unauthorised AI knowledge source can produce a confident, actionable and wrong answer. AI raises the quality bar — so structured data, unstructured knowledge and evaluation data are treated as connected engineering concerns.
You see how a Python parser becomes a Bronze ingestion component, how SQL logic becomes a data product, how streaming changes operations and how governed knowledge supports an AI application.
Python, SQL, PostgreSQL, dimensional modelling and Power BI.
PySpark, file design, Delta, medallion layers and data products.
Orchestration, batch, CDC, streaming, quality, contracts, lineage and security.
Databricks and Microsoft Fabric implementation.
Knowledge pipelines, evaluation data, capstone, operations and handoff.
Twelve modules. One traceable evidence chain.
Build the engineering habits behind reliable pipelines — moving from one-off scripts to typed, tested, packaged and observable components.
Develop the relational reasoning required to ingest, validate, model, query and optimise operational data before moving to distributed systems.
Learn how analytical consumers experience data, so you can create clear facts, dimensions, metrics and governed serving layers.
Move from single-machine processing to distributed data engineering while preserving schema discipline, testability and performance reasoning.
Build reliable analytical tables on object storage and turn layered transformations into discoverable, owned data products.
Turn transformations into repeatable, dependency-aware and releasable workflows that can recover from partial failure.
Engineer stateful data movement where ordering, duplicates, late arrival, deletes, replay and latency materially affect the result.
Engineer trust across producer, pipeline and consumer boundaries — and make data failures visible before they become business or AI failures.
Implement the programme’s durable pipeline, governance and DataOps concepts on the Databricks Data Intelligence Platform.
Implement a unified Microsoft data solution spanning ingestion, lakehouse, warehouse, real-time analytics, governance and Power BI consumption.
Build the authorised, current and traceable knowledge and evaluation-data foundation behind RAG systems, copilots and agents.
Integrate the programme into one production-shaped data platform and defend the architecture, evidence, failure recovery, governance and handoff.
A tool earns its place only when learners can answer what it solves, what it assumes, how it fails, what evidence proves it worked, what would replace it and what it costs in portability.
Leaders need governed analytics. Operations teams need fresher events. An Enterprise AI Operations Copilot needs authorised, current and traceable knowledge. You build the structured layers, the knowledge layer and the trust controls that make all three defensible.
You implement the complete assessed path on one approved primary platform — Databricks or Microsoft Fabric — and demonstrate the other through its guided module. The portfolio must include a portability record naming the portable Python, SQL, Spark and Delta logic, the vendor-specific orchestration and governance, migration risks, non-equivalent services, and data-egress implications. We don't promise two full production-grade platform builds.
Ingest schedules, vehicle events and service alerts. Reconcile event time, handle late telemetry and publish route-reliability products.
Capture order and inventory changes, process updates and deletes, and publish an inventory-availability product.
Use fully synthetic records to model access, masking, retention and aggregate reporting.
Diagnose a "green pipeline, wrong data" event caused by an upstream semantic change.
Build an access-aware document pipeline with versioning, withdrawal and freshness checks.
Implement one small medallion flow on both Databricks and Fabric.
Approved AI tools may be used for explanation, code review, test ideation and documentation where the assessment permits. You must declare material AI assistance, verify generated code and SQL, avoid sending confidential data, explain all submitted work, reproduce critical logic without hidden agent execution, and retain individual competence for gated assessments.
Progression is Pass, Pass with distinction, Remediation required, or Not yet demonstrated — the last when one or more critical gates remain unmet.
Seven tracks each own a slice of the enterprise AI value chain. Each is individually employable and individually incomplete — enterprises don't fail at AI because a discipline is missing, they fail at the seams between them. The FDE is the single named human accountable for the whole outcome.
Senior, lead, architect and principal roles require production experience, wider system ownership and organisational judgement beyond course completion.
Not "I used eight tools." Rather: I built a governed data product, handled schema change and late events, proved reconciliation and access, implemented it on an enterprise platform, operationalised AI knowledge freshness and recovered it under failure.
Professional Data Engineering experience, previous Spark/Databricks/Fabric exposure, advanced mathematics, ML model development and a CS degree are all not required.
The titles differ by employer — the role cluster above covers them. What does not differ is the ask: someone who can make data arrive correctly, on time, with lineage, and keep it that way when the schema changes.
Teams building and supporting lakehouse and warehouse products, where the pipeline is what other people build their business on.
Central groups owning ingestion, modelling and serving for the whole organisation, with contracts across dozens of consumers.
Migration and modernisation programmes where reconciliation evidence decides whether a cutover is accepted.
Regulated reporting where lineage, access control and reproducibility are audited rather than assumed.
High-volume operational data where freshness, late-arriving records and replay correctness matter more than elegance.
Groups making enterprise data AI-ready: access-aware retrieval corpora, semantics and quality contracts an agent can trust.
Categories reflect how these roles are commonly scoped and advertised across the market. Availability, titles and requirements differ by employer, region and year, and many roles expect prior industry experience — see the evidence methodology.
Individual outcomes reflect each graduate's prior experience and market conditions. The programme does not guarantee a role — see the evidence methodology.
Enterprise scale is where AI agents earn their keep — an agent plugged into the real service path, orchestrating work across teams as one fabric. That is the bar I teach to, every class.
Manikanta is the founder of KDigital and brings 15 years of enterprise platform architecture, where he led rollouts for Fortune-500 banks, telcos and insurers. Most recently he architected production agent deployments that replaced traditional triage tiers with governed, autonomous case handling.
His architecture modules give you two things other programmes do not: a founding architect who has shipped enterprise data platforms from inside the Fortune 500, and a curriculum rewritten every release — so when interviewers ask about contracts, semantics or AI-ready corpora, you have already designed them.
Operations is where AI stops being a demo and starts being the nervous system of the enterprise — telemetry you trust, alerting that is quiet on purpose, and agents resolving incidents before on-call gets paged.
Ravi is Chief Technologist at KDigital, where he leads the operations-engineering practice. After eight years building and running production DevOps pipelines, he stepped into the Chief Technologist seat to wire observability, evaluation and incident response into the way operations teams actually work.
His reliability modules are built from real incident post-mortems, not slide decks. Expect to leave with working orchestration patterns, replay and reconciliation drills, alerting that stays quiet and a runbook you can hand over.
No placement guarantees, unverified salary figures or borrowed logos appear on this page. Judge the programme on what you can inspect — and ask us for anything on this list.
If the answer you need isn't here, book a 20-minute advisor call. No slides, no pitch — just your questions.
Campuses in Hyderabad and Bengaluru, plus live online cohorts running on Indian and US timezones.
Learn Data. Build Pipelines. Ground the Intelligence.
Adjacent tracks that share modules, faculty and the same evidence standard.
Build the pipelines, contracts and quality gates that agentic systems depend on for a trustworthy record.
Level: Beginner to advanced. Delivery: Online, Blended, Onsite. Related roles: Data Engineer, Analytics Engineer, Platform Data Engineer. Credential: KDigital Academy programme completion credential, awarded on defended portfolio evidence.
The discipline of designing, building and operating systems that ingest, transform, validate, govern and serve data. A Data Engineer makes data available in the right form, at the required time, with evidence that it is trustworthy and authorised.
It is designed as a foundation-to-job-ready path, but the main programme has a coding readiness bar. Learners who need Python, SQL, Git, API or command-line foundations can take the recommended 4–6 week bridge.
The recommended design is 24 weeks part-time with approximately 190–220 total learning hours and 8–10 hours of weekly commitment. Your cohort’s published duration and schedule are confirmed at enrolment.