Covers basic numerical and graphical summaries with practical examples, from University of Washington.

✅ 15 Data Engineering Interview Questions for Freshers 🛠️📊

These are core questions freshers face in 2025 interviews—per recent guides from DataCamp and GeeksforGeeks, ETL and pipelines remain staples, with added emphasis on cloud tools like AWS Glue for scalability. Your list nails the basics; practice explaining with real examples to shine!

1) What is Data Engineering?
Answer: Data Engineering involves designing, building, and managing systems and pipelines that collect, store, and process large volumes of data efficiently.

2) What is ETL?
Answer: ETL stands for Extract, Transform, Load — a process to extract data from sources, transform it into usable formats, and load it into a data warehouse or database.

3) Difference between ETL and ELT?
Answer: ETL transforms data before loading it; ELT loads raw data first, then transforms it inside the destination system.

4) What are Data Lakes and Data Warehouses?
Answer:
⦁ Data Lake: Stores raw, unstructured or structured data at scale.
⦁ Data Warehouse: Stores processed, structured data optimized for analytics.

5) What is a pipeline in Data Engineering?
Answer: A series of automated steps that move and transform data from source to destination.

6) What tools are commonly used in Data Engineering?
Answer: Apache Spark, Hadoop, Airflow, Kafka, SQL, Python, AWS Glue, Google BigQuery, etc.

7) What is Apache Kafka used for?
Answer: Kafka is a distributed event streaming platform used for real-time data pipelines and streaming apps.

8) What is the role of a Data Engineer?
Answer: To build reliable data pipelines, ensure data quality, optimize storage, and support data analytics teams.

9) What is schema-on-read vs schema-on-write?
Answer:
⦁ Schema-on-write: Data is structured when written (used in data warehouses).
⦁ Schema-on-read: Data is structured only when read (used in data lakes).

10) What are partitions in big data?
Answer: Partitioning splits data into parts based on keys (like date) to improve query performance.

11) How do you ensure data quality?
Answer: Data validation, cleansing, monitoring pipelines, and using checks for duplicates, nulls, or inconsistencies.

12) What is Apache Airflow?
Answer: An open-source workflow scheduler to programmatically author, schedule, and monitor data pipelines.

13) What is the difference between batch processing and stream processing?
Answer:
⦁ Batch: Processing large data chunks at intervals.
⦁ Stream: Processing data continuously in real-time.

14) What is data lineage?
Answer: Tracking the origin, movement, and transformation history of data through the pipeline.

15) How do you optimize data pipelines?
Answer: By parallelizing tasks, minimizing data movement, caching intermediate results, and monitoring resource usage.

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Notes on HDFS, MapReduce, YARN, Hadoop vs. traditional systems and much more... from Columbia University.

🌐 Data Engineering Tools & Their Use Cases 🛠️📊

🔹 Apache Kafka ➜ Real-time data streaming and event processing for high-throughput pipelines
🔹 Apache Spark ➜ Distributed data processing for batch and streaming analytics at scale
🔹 Apache Airflow ➜ Workflow orchestration and scheduling for complex ETL dependencies
🔹 dbt (Data Build Tool) ➜ SQL-based data transformation and modeling in warehouses
🔹 Snowflake ➜ Cloud data warehousing with separation of storage and compute
🔹 Apache Flink ➜ Stateful stream processing for low-latency real-time applications
🔹 Estuary Flow ➜ Unified streaming ETL for sub-100ms data integration
🔹 Databricks ➜ Lakehouse platform for collaborative data engineering and ML
🔹 Prefect ➜ Modern workflow orchestration with error handling and observability
🔹 Great Expectations ➜ Data validation and quality testing in pipelines
🔹 Delta Lake ➜ ACID transactions and versioning for reliable data lakes
🔹 Apache NiFi ➜ Data flow automation for ingestion and routing
🔹 Kubernetes ➜ Container orchestration for scalable DE infrastructure
🔹 Terraform ➜ Infrastructure as code for provisioning DE environments
🔹 MLflow ➜ Experiment tracking and model deployment in engineering workflows

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You don't need to learn Python more than this for a Data Engineering role

➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity

➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming

➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops

➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance

➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes

➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()

➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks

➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics

➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets

➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases

⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses

⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion