Azure Data Factory (ADF) is Microsoft Azure's cloud-based data integration service used to build ETL and ELT pipelines. It works through pipelines containing activities (such as copy or transformation tasks), datasets that represent your data, and linked services that define connections to sources like SQL databases, Blob Storage, or REST APIs. Triggers run pipelines on a schedule or in response to events, while Mapping Data Flows handle no-code transformations. Since it is serverless, you can visually build, monitor, and debug pipelines in the Azure portal without managing infrastructure.

Start with the fundamentals — SQL, basic Python, and an understanding of what data pipelines and ETL mean. Then create a free Azure account, follow the official quickstarts, and build hands-on: copy files from Blob Storage into a SQL database, add scheduling triggers, and experiment with data flows. From there, move to real scenarios like incremental loads, parameterization, error handling, and connecting ADF with Azure Databricks. Working on small real projects beats passive videos, and following a structured Azure Data Engineer roadmap can save you weeks of guessing what to learn next.

They solve different problems. Azure Data Factory is a low-code orchestration and ETL tool for moving and scheduling data between systems, while Azure Databricks is a Spark-based platform for heavy data transformation, big data processing, and machine learning. If you are starting out, learn ADF first to understand pipeline building and orchestration, then pick up Databricks with PySpark for transformations at scale. In real projects the two work together — ADF typically orchestrates the flow while Databricks processes the data.

Azure Databricks is used for large-scale data engineering and analytics — processing and transforming huge volumes of data with Apache Spark, building batch and streaming pipelines, managing Delta Lake tables, and running machine learning workloads. Teams turn to it when data is too large or transformations too complex for simple ETL tools. Typical use cases include data cleansing, aggregations, sessionization, and preparing clean datasets for BI dashboards and ML models.

Begin with Spark fundamentals — understand the driver, executors, jobs, stages, and the idea of lazy evaluation — before writing any code. Then start with PySpark DataFrames on a Databricks free trial or community edition and practice basic transformations, joins, and aggregations on public datasets. Progress to Delta Lake, the medallion (bronze-silver-gold) architecture, and optimization basics like partitioning, caching, and broadcast joins. An end-to-end mini project, such as ingesting raw files and serving clean tables, will cement everything.

No, Microsoft does not offer an ADF-only exam. Azure Data Factory skills are tested within broader role-based certifications — most notably the Microsoft Azure Data Engineer Associate path and the Microsoft Fabric Data Engineer certification. If your goal is to validate your ADF skills, prepare for one of these data engineer certifications, since both cover pipeline building, orchestration, and data integration topics in depth.

The closest equivalent is AWS Glue, a serverless ETL service with crawlers, jobs, and the Glue Data Catalog. For orchestration, AWS Step Functions is often compared with ADF pipelines, while Amazon EMR maps to Spark-based processing in Databricks. This ADF-to-AWS mapping is a common question in data engineer interviews, especially when companies run workloads across both clouds.

The most common data engineer interview questions revolve around SQL (joins, group by, window functions), Python, PySpark transformations, data modeling (star schema, normalization), SQL vs NoSQL, and ETL pipeline design. Tool-specific rounds cover Azure Data Factory, Databricks, and Azure Data Lake, along with scenario questions like "how would you design an incremental load?" or "how do you debug a failed pipeline?" — plus a deep-dive on projects listed on your resume.

Look at candidate interview experiences on forums, official documentation, and curated interview preparation kits that compile real data engineer interview questions and answers across SQL, PySpark, ADF, Databricks, ADLS, Python, and data modeling. A consolidated kit with explanations is far more efficient than scattered posts because you can revise everything in one place before the interview — then practice writing each answer in your own words.

At around two years, interviewers expect hands-on depth rather than theory. The data engineer interview questions for 2 years of experience usually include complex SQL queries, PySpark coding, walkthroughs of pipelines you personally built in ADF or Databricks, debugging scenarios, and basic design questions. Unlike fresher rounds, interviewers will probe what you actually built in production, how you handled failures, and how you collaborated with analysts and stakeholders.

Follow a focused plan: revise SQL and PySpark daily until you can write queries fluently, know every resume project inside out, and prepare tool-specific answers for ADF, Databricks, and Data Lake scenarios. Practice explaining your thought process out loud and do at least a few mock interviews. In the final one to two weeks, structured revision of real interview questions matters far more than cramming new topics.

Structure your answer around three things: genuine interest in working with data, evidence of aptitude (SQL, Python, or pipeline projects you have done), and the impact data engineers have on business decisions. Connect it to a concrete example — for instance, a project where building a pipeline or query solved a real problem. Avoid clichés like "I love data"; interviewers want to see that you understand what the role actually involves day to day.

Frequently asked Azure Data Factory interview questions include the difference between a pipeline and a data flow, integration runtime types, trigger types (schedule, tumbling window, event-based), copy activity performance tuning, parameterization, incremental loading using watermarks, error handling and debugging, and monitoring. You should also be ready for "ADF vs Databricks — when do you use which?" since it tests real-world design judgment.

Prepare for Spark architecture questions (driver and executors, transformations vs actions, lazy evaluation), PySpark coding on joins, aggregations, window functions, and handling nulls or duplicates, plus Delta Lake features like ACID transactions and time travel. Optimization topics — partitioning, caching, broadcast joins, and skew handling — come up regularly, along with how Databricks fits into an end-to-end pipeline orchestrated by ADF.

The data engineer salary in India depends heavily on experience, skills, city, and company. As a rough ballpark, freshers typically earn around ₹4–6 LPA, professionals with 2–4 years of experience often fall in the ₹8–14 LPA range, and senior engineers with 5+ years frequently cross ₹18–30 LPA. Skills in Azure, Databricks, PySpark, and SQL usually command a premium, so treat these as indicative ranges and check current job postings for your exact profile.