Power BI is Microsoft's business intelligence tool that lets you connect to data from Excel, databases, or cloud sources, clean it, and turn it into interactive dashboards and reports. In simple terms, it helps you answer business questions visually instead of reading raw spreadsheets. It needs no heavy coding to start, which is why it is usually the first tool aspiring analysts and data engineers learn.

Download Power BI Desktop (it is free), pick a simple dataset like sales data, and clean it in Power Query before building basic charts. Learn in this order: Power Query for cleaning, simple visuals, filters and slicers, and a few basic DAX measures. Building one complete dashboard end to end teaches you more than weeks of passive video watching, and you don't need coding or cloud setup at this stage.

Free videos are fine for exploring, but most beginners get stuck between watching and doing — that is where a structured Power BI basics course or guided mentorship helps, because you get a clear sequence, doubt-solving, and feedback on your dashboards. Whichever route you pick, make sure you build 2–3 portfolio dashboards on real datasets, since recruiters shortlist candidates based on projects, not certificates of completion.

Interviewers typically start with fundamentals: the difference between Power Query and DAX, measure vs calculated column, filters vs slicers, types of merges and joins, handling nulls, and basic modelling like star schema. They usually follow up with "walk me through a dashboard you built", so clear fundamentals plus one well-explained project are usually enough for fresher-level roles.

Azure Data Factory is Microsoft's cloud service for data integration. It is used to automatically move data from different sources — databases, files, APIs — transform it if needed, and load it into a central store like Azure Data Lake or a warehouse on a schedule. Whenever a company needs daily automated pipelines that collect and deliver data reliably, that orchestration is typically done in Azure Data Factory.

First, understand how to access Azure Data Factory: create a free Azure account, open the Azure portal, create a Data Factory resource, and launch Data Factory Studio in your browser. Then use the Copy Data tool to move one file from storage into a table. Once that works, learn the four core building blocks — linked services, datasets, activities, and triggers — and try a small pipeline that runs on a daily schedule. That single flow covers most of what beginners need.

The Azure Data Factory cost for practice is very low because it is pay-as-you-go — you pay only when pipelines run and for the small amount of data moved, so practice pipelines usually cost a few rupees or less per run. New Azure accounts also come with free credits for the first 30 days, and you can set a spending alert in the portal. Just delete your resource group after each practice session so nothing keeps running in the background.

They do different jobs: Azure Data Factory moves and orchestrates data between systems, while Databricks processes and transforms large datasets using Spark. Companies typically use both together — ADF brings the data in, Databricks does the heavy processing. If you are starting fresh, learn Azure Data Factory basics first because pipelines are easier to grasp, then move to Databricks, which also sets you up for its certification path.

No — Azure Data Factory does not have a standalone exam of its own. It is covered inside Microsoft's broader Azure data certifications: Azure Data Fundamentals (DP-900) touches the concepts, and the data engineer–level Azure certification tests hands-on pipeline building. So your ADF skills get validated through these certification exams rather than through a dedicated ADF badge.

Expect questions on linked services vs datasets, pipelines vs activities, control flow vs data flow, trigger types (schedule, tumbling window, event-based), copy activity vs mapping data flows, and how to implement incremental loads. Scenario questions are very common too — for example, designing a pipeline that picks up a daily file and loads it into a data lake. Being able to explain one real pipeline you built yourself handles most of the round.

Pick the right exam for your level — for entry-level data engineering that is the Associate certification — then create your account on Databricks' official training portal, book an online proctored exam slot, and pay the exam fee. Prepare using the free self-paced Databricks Academy learning paths and practice hands-on on the Databricks Community Edition, because the exam tests practical Spark, SQL, and Delta Lake skills, not just theory.

It is the entry-level Databricks certification exam for data engineers, covering Spark architecture and DataFrames, SQL, Delta Lake, building pipelines with Databricks workflows (jobs), incremental processing, and data quality basics. It is a proctored multiple-choice exam, and with regular hands-on practice it is a moderate, very passable test. Most people struggle only when they prepare purely from videos and never actually build pipelines themselves.

Databricks charges its exam fees in US dollars worldwide, so the Databricks certification cost in India is roughly the same as everywhere — around US $200 for Associate-level exams, which works out to roughly ₹17,000–18,000 depending on the exchange rate at the time of payment. Check the exact current fee on the official scheduling portal before booking, and budget for a possible retake while planning.

Getting a Databricks certification for free is uncommon but not impossible — Databricks periodically runs promotions, community events, and challenges that offer free or discounted exam vouchers, and some employers and academic partners also sponsor vouchers. The learning itself is free: self-paced Academy courses and the Community Edition don't cost anything. So a realistic plan is to learn for free and watch for voucher offers before paying the full fee.

Databricks certifications are valid for two years from the date you earn them. To renew, you retake the current version of the same exam before it expires — there is no separate shorter renewal exam. Since the platform evolves quickly, brushing up on the latest Databricks features before the retake is usually enough if you already use the tools regularly.