Every question worth
asking before you apply.

Yes — and honestly, your background is an advantage most freshers don't have. You already understand enterprise systems, data flows, batch processing logic, and how organizations actually work. What you need is structured guidance on modern cloud tools and thinking patterns. That's exactly what DataKern is designed for. We have students with COBOL, SAP, and mainframe backgrounds who are now building Azure data pipelines. Your experience is a starting point, not an obstacle.

If you're transitioning from a non-data IT background (Mainframe, legacy, operations, ERP), start with Data Engineering. It gives you foundational cloud, pipeline, and data platform skills that make everything else easier to understand — including AI. If you already have software or data engineering fundamentals and want to build AI applications, RAG systems, and agent workflows, go with AI Engineering. When in doubt, apply and tell us your background — we'll help you decide.

This concern comes up often, and the honest answer is: experience doesn't go away, it compounds. You're not starting from zero — you're building a new layer on top of years of real work. DataKern classes include professionals in their 30s, 40s, and beyond. What matters is your motivation to learn, not your age or how many years you've spent in a different technology.

No — but it changes what Data Engineers do. Every AI application — every chatbot, every recommendation engine, every intelligent workflow — depends on clean, reliable, well-structured data underneath. Someone has to build and maintain those pipelines, data platforms, quality checks, and governance frameworks. If anything, the rise of AI makes strong Data Engineering more valuable, not less. The demand is growing.

Yes. DataKern is also written Data Kern or Datakern, and our web address is data-kern.com. It is one training company, led by Uma Kiran Ramadugu, and it is sometimes searched for as Data Kiran. If you found us that way, you are in the right place.

Great question — and one we discuss openly in the program. Databricks is the dominant platform for Apache Spark-based data engineering in enterprise environments. It's cloud-agnostic, well-documented, and used extensively in banking, retail, and healthcare data teams. Microsoft Fabric is newer and evolving quickly — understanding Databricks first gives you the foundation to learn Fabric faster. We also cover why you might choose Synapse for certain workloads. The goal is to understand tradeoffs, not memorize one platform.

Yes. ADF is part of the Azure Data Engineering curriculum. You'll learn how to orchestrate pipelines using ADF, connect it with ADLS and Databricks, handle triggers, monitoring, and parameterization. It's covered alongside the broader pipeline architecture — not just as isolated feature training.

Fabric is covered in context. Since it's a rapidly evolving platform, we explain what it is, how it relates to what you're already learning, and where it fits in the Azure ecosystem. We don't recommend jumping straight to Fabric without understanding the Data Engineering fundamentals it builds on.

Because they develop different capabilities. Data Engineering is about building data platforms, pipelines, and the foundations that make data usable at scale. AI Engineering is about building AI applications — RAG systems, agents, evaluation pipelines, and production AI workflows. They overlap, but pipeline thinking and AI application engineering deserve distinct focus. Doing both at once usually means understanding neither well.

Each program includes 2–3 guided projects during the program, plus a capstone project you design in a domain of your choice. Data Engineering projects include an enterprise retail data platform and a banking transaction pipeline. AI Engineering projects include a document knowledge assistant and a multi-tool AI agent. For the capstone, you can choose banking, retail, healthcare, logistics, or any domain relevant to your career goals. Projects are reviewed end-to-end, not just submitted.

DataKern is taught live online by a practising Data & AI architect in the Netherlands, so there is no classroom to travel to, whichever city you are in. Students join from India, the Netherlands and elsewhere. The sessions are live, in small groups, with direct feedback.

All our training is 100% live and online, meaning you get the best practical training right where you are. Whether you're searching for courses near you in India, the EU, or globally, our remote format offers the personal attention of a physical classroom with the convenience of learning from home.

Yes. Every session is live and interactive. Live sessions are the core of the DataKern experience — you can ask questions, challenge ideas, share your screen, and get direct feedback on your thinking and your code. There's no pre-recorded course with a certificate at the end. The sessions are the work.

All live classes are held on Saturdays only, making it easy to balance with a full-time job or university studies. Each Saturday session runs for 2.5 hours with live coding, architectural discussions, and direct feedback. The Crash Course consists of 6 sessions (6 Saturdays), and the Full Course consists of 12 sessions (12 Saturdays). Currently, Data Engineering and AI Engineering batches are progressing, and the next batch begins in January 2027.

Our next live Masterclasses for Data Engineering and AI Engineering are expected in Mid-December / Mid-January (held on a Saturday/weekend). Keep looking at this space, or submit an application selecting the Masterclass option to be notified as soon as dates are announced.

Plan for 5–8 hours per week total: the 2.5-hour live session on Saturday, plus 3–5 hours during the week to complete your hands-on exercises and project work at your own pace. The Saturday live session is interactive — everything else can be completed during your free time.

Sessions are recorded and available to enrolled students for review. If you miss a session, you can watch the recording. That said, the value of live sessions comes from participation — asking questions, working through problems together, and getting feedback. Recordings are a safety net, not a substitute for showing up.

Yes — every session is taught by Uma Kiran Ramadugu, founder of DataKern and a Data & AI Architect working at a bank in the Netherlands. There's no rotating faculty, no guest lecturers filling in, no outsourced content. You get the same person every session: someone still actively building the systems you're learning about.

You start with an Azure free account, which includes a $200 credit valid for 30 days and a set of always-free services. After the free period, you switch to pay-as-you-go. For the practical exercises in this program, your actual cloud costs are typically €5–15 per month — not hundreds. We walk you through setting up cost alerts, resource cleanup habits, and budget guardrails from day one so you're never surprised by a bill. Azure account management is part of the curriculum, not an afterthought.

Data Engineering: Azure Data Factory, Azure Data Lake Storage (ADLS Gen2), Azure Databricks, PySpark, Delta Lake, Azure Key Vault, Azure DevOps, Git, Python. AI Engineering: Python, LangChain, LangGraph, ChromaDB, Azure OpenAI / OpenAI API, FastAPI, MLflow, Arize Phoenix, Jupyter. We use tools that engineers actually use in production delivery — not tutorial-only frameworks.

Programs are offered in three tiers: Masterclass (€10), Crash Course (€55), and Full Course (€99). We accept payments in INR, EUR, and USD. Corporate training starts from €2,000 depending on the format. We keep pricing accessible — the Masterclass is especially affordable as a starting point.

Full details are in the Refund Policy page. In short: we offer a refund if you withdraw before the program begins. Once the program has started, refunds are assessed on a case-by-case basis for genuine exceptional circumstances. We don't offer refunds simply because the program was harder than expected — please apply only if you're genuinely committed to the work.

We review every application personally. If you're not selected for the upcoming batch — because the batch is already full or we think a different track fits better — we'll tell you clearly and discuss alternatives. You won't be left without a response.

Course completion certificates are coming soon — they will be tied to successful project delivery, not just attendance. This is intentional: we want the certificate to mean something. A certificate you earned by building and explaining a real system is worth more than one you received for showing up. We'll announce details for each batch before it starts.

Yes. DataKern works with organizations to build practical Data & AI capability through workshops, bootcamps, and structured team programs. Whether you need a 1-day executive briefing on AI adoption or a full 8-week team program on Data Engineering, we can design it. Start with a free 30-minute discovery call.

Yes. Every corporate engagement starts with understanding your team's current skill level, your existing technology stack, and your specific business goals. We don't run the same generic workshop for every client. If your team is on AWS instead of Azure, or focused on financial data instead of retail, the content adapts.

1-day executive workshop, 2-day hands-on workshop, 5-day intensive bootcamp, 4–8 week team program, architecture review and advisory, or a custom engagement. Online, onsite at your location, or hybrid. All formats begin with a discovery call to understand what will actually work for your team.