Crack your data engineering interview 200 questions

This comprehensive guide is designed to help aspiring data engineers prepare for technical interviews. It covers key technologies and concepts, including Hadoop, Apache Spark, Apache Sqoop, Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and ETL processes. Each section provides detailed questions and answers to build a strong foundation and boost confidence

All About Data Engineering

Crack Your Data Engineering Interview - 200

Questions & Answers

Prepared for Aspiring Data Engineers

May 2025

1 Introduction

This comprehensive guide is designed to help aspiring data engineers prepare for technical interviews It covers key technologies and concepts, including Hadoop, Apache Spark, Apache Sqoop, Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and ETL processes Each section provides detailed questions and an-swers to build a strong foundation and boost confidence

2 Hadoop Interview Questions

2.1 What is Hadoop MapReduce?

Answer: Hadoop MapReduce is a programming framework for processing large

datasets in parallel across a Hadoop cluster It consists of two phases: the

Map phase, which processes input data into key-value pairs, and the Reduce

phase, which aggregates the results

2.2 What are the differences between a relational database and HDFS?

Answer: The differences are:

1 Data Types: RDBMS handles structured data with a predefined schema;

HDFS stores structured, semi-structured, and unstructured data

2 Processing: RDBMS has limited processing capabilities; HDFS

sup-ports distributed parallel processing via MapReduce or other frame-works

3 orbitingSchema: RDBMS uses schema-on-write, requiring a fixed schema before data insertion; HDFS uses schema-on-read, allowing flexible schema definition during analysis

4 Read/Write Speed: RDBMS offers fast reads due to indexing; HDFS

provides fast writes as no schema validation occurs during writes

5 Cost: RDBMS requires licensed software; HDFS is open-source and

cost-effective

6 Use Case: RDBMS is ideal for OLTP (Online Transaction Processing);

HDFS is suited for data analytics and OLAP (Online Analytical Process-ing)

2.3 What is HDFS and YARN?

Answer: HDFS (Hadoop Distributed File System) is Hadoop’s storage layer,

stor-ing data as blocks across a distributed cluster It follows a master-slave

ar-chitecture, with the NameNode managing metadata and DataNodes storing

data blocks

YARN (Yet Another Resource Negotiator) is Hadoop’s resource management

and job scheduling framework The ResourceManager allocates resources and schedules tasks, while NodeManagers execute tasks on individual nodes.

2.4 What are active and passive NameNodes?

Answer: In a high-availability Hadoop cluster, there are two NameNodes:

• Active NameNode: The primary NameNode that manages the

clus-ter’s metadata and handles client requests

• Passive NameNode: A standby NameNode that maintains

synchro-nized metadata and takes over if the active NameNode fails, ensuring high availability

2.5 Why do we use HDFS for large datasets but not for many small files? Answer: HDFS is optimized for large datasets because the NameNode stores

metadata in RAM, which limits the number of files it can efficiently handle Many small files generate excessive metadata, overwhelming the NameN-ode’s memory and degrading performance

3 Apache Spark Interview Questions

3.1 What is Apache Spark?

Answer: Apache Spark is a distributed computing framework for real-time data

analytics It uses in-memory processing, making it up to 100 times faster than Hadoop MapReduce for large-scale data processing Spark supports multiple languages (Python, Scala, Java) and integrates with Hadoop, Hive, and other data sources

3.2 What is an RDD?

Answer: RDD (Resilient Distributed Dataset) is Spark’s core data structure,

rep-resenting an immutable, distributed collection of objects partitioned across

a cluster RDDs are fault-tolerant, using a lineage graph to recover lost data

by recomputing transformations

3.3 What are Transformations and Actions in Spark?

Answer: Spark operations are categorized as:

• Transformations: Lazy operations (e.g., map, filter, join) that define

a new RDD without immediate execution They build a DAG (Directed Acyclic Graph)

• Actions: Operations (e.g., collect, count, save) that trigger computation

and return results or write data to storage

3.4 What is the difference between narrow and wide transformations? Answer: • Narrow Transformations: Operations where each input

parti-tion contributes to only one output partiparti-tion (e.g., map, filter) They are executed in-memory without shuffling

• Wide Transformations: Operations requiring data shuffling across

partitions (e.g., groupBy, reduceByKey), which are more expensive due

to network and disk I/O

3.5 What is lazy evaluation in Spark?

Answer: Lazy evaluation means Spark delays executing transformations until

an action is called This allows Spark to optimize the execution plan by combining transformations into a single stage, reducing computation and I/O overhead

4 MapReduce Interview Questions

4.1 What is MapReduce?

Answer: MapReduce is a programming model for processing large datasets in

parallel across a Hadoop cluster It consists of a Map phase, which trans-forms input data into key-value pairs, and a Reduce phase, which

aggre-gates the mapped data to produce final results

4.2 Why can’t we perform aggregation in the mapper?

Answer: Aggregation cannot be performed in the mapper because sorting and

shuffling occur only in the reduce phase Mappers run independently on different nodes, and their outputs are not globally coordinated until the reducer processes them

4.3 What is a Combiner?

Answer: A Combiner is a mini-reducer that performs local aggregation on the

mapper’s output before it is sent to the reducer It reduces network traf-fic and improves performance by minimizing the data shuffled across the cluster

4.4 What is the role of the JobTracker?

Answer: The JobTracker (in Hadoop 1.x) manages resources and schedules

MapRe-duce jobs It assigns tasks to TaskTrackers, monitors their progress, and handles task failures by reassigning them to other nodes

4.5 What is the difference between HDFS block and InputSplit?

Answer: • HDFS Block: A physical division of data stored in HDFS, typically

128 MB in Hadoop 2.x, used for storage efficiency

• InputSplit: A logical division of data for MapReduce processing,

de-termining how data is split for parallel map tasks InputSplits may not align with HDFS block boundaries

5 Apache Sqoop Interview Questions

5.1 What is Apache Sqoop?

Answer: Apache Sqoop is a tool for transferring data between Hadoop and

rela-tional databases (RDBMS) It supports parallel import/export, incremental loads, and integration with Hive, HBase, and Accumulo, using MapReduce for data transfer

5.2 What are the best features of Apache Sqoop?

Answer: Key features include:

• Parallel import/export for high performance

• Connectors for major RDBMS (e.g., MySQL, Oracle)

• Support for full and incremental data loads

• Kerberos security integration

• Direct loading into Hive or HBase

• Data compression for efficient transfers

5.3 How can you import large objects like BLOB and CLOB in Sqoop? Answer: Sqoop does not support direct import of BLOB and CLOB objects

In-stead, use JDBC-based imports without the –direct argument to handle large objects, leveraging the database’s JDBC driver

5.4 What is the purpose of the Sqoop-merge tool?

Answer: The Sqoop-merge tool combines two datasets, overwriting entries in

an older dataset with newer ones It preserves the latest version of records, ensuring data consistency during incremental imports

5.5 How can you control the number of mappers in Sqoop?

Answer: The number of mappers can be controlled using the –num-mappers

or -m parameter in the Sqoop command For example, sqoop import – num-mappers 4 sets four mappers

6 Azure Data Factory Interview Questions

6.1 What is Azure Data Factory?

Answer: Azure Data Factory (ADF) is a cloud-based data orchestration service

for building ETL and ELT pipelines It supports data migration, transfor-mation, and autotransfor-mation, integrating with various data sources in the cloud and on-premises

6.2 What are the main components of Azure Data Factory?

Answer: • Pipeline: A logical grouping of activities to perform a task.

• Activities: Processing steps (e.g., copy, transform, control flow).

• Datasets: Data structures representing input/output data.

• Linked Services: Connection strings to data stores or compute

re-sources

• Integration Runtime: Compute infrastructure for executing

activi-ties

• Triggers: Mechanisms to schedule pipeline execution.

6.3 What is the Copy Activity in Azure Data Factory?

Answer: The Copy Activity moves data between source and destination data

stores, supporting transformations like serialization, compression, and col-umn mapping It is widely used for ETL and data migration tasks

6.4 What are the types of Integration Runtime in ADF?

Answer: • Azure IR: Managed by Azure, used for cloud-based data

move-ment and transformations

• Self-Hosted IR: Installed on-premises or in a private network to access

local data sources

• Azure-SSIS IR: Runs SQL Server Integration Services (SSIS) packages

in the cloud

6.5 How can you schedule a pipeline in ADF?

Answer: Pipelines can be scheduled using:

• Schedule Trigger: Invokes pipelines on a wall-clock schedule.

• Tumbling Window Trigger: Runs pipelines at periodic intervals,

re-taining state

• Event-Based Trigger: Executes pipelines in response to events, like

file arrivals in Blob Storage

7 Azure Synapse Analytics Interview Questions

7.1 What is Azure Synapse Analytics?

Answer: Azure Synapse Analytics is an integrated analytics service combining

enterprise data warehousing and big data analytics It supports SQL-based analytics (Synapse SQL), Spark-based processing, and data integration pipelines

7.2 What are the types of Synapse SQL pools?

Answer: • Serverless SQL Pool: A pay-per-query model for ad-hoc analytics

on data lakes

• Dedicated SQL Pool: A provisioned model for enterprise data

ware-housing with optimized performance

7.3 What is Delta Lake in Azure Synapse?

Answer: Delta Lake is an open-source storage layer that adds ACID transactions

to Spark-based big data workloads In Azure Synapse, it supports reliable data lakes with Scala, PySpark, and NET, ensuring data consistency and scalability

7.4 What is the OPENROWSET function in Synapse Analytics?

Answer: The OPENROWSET function allows reading data from diverse sources

(e.g., flat files, RDBMS) as a table in Synapse SQL It is used to query files in Azure Data Lake Storage without loading them into a database

7.5 How do you create a pipeline in Azure Synapse Analytics?

Answer: Steps to create a pipeline:

1 Open Synapse Studio and navigate to the Integrate hub

2 Click “Pipeline” to create a new pipeline

3 Drag activities from the Activities panel into the pipeline designer

4 Configure activities, datasets, and linked services

5 Publish the pipeline and test it for errors

8 Azure Databricks Interview Questions

8.1 What is Azure Databricks?

Answer: Azure Databricks is a cloud-based platform built on Apache Spark,

op-timized for big data analytics and machine learning It provides collabora-tive notebooks, scalable clusters, and integration with Azure services

8.2 What are the types of clusters in Azure Databricks?

Answer: • Interactive Cluster: Used for running notebooks in interactive

mode

• Job Cluster: Used for scheduled or automated jobs, terminating after

completion

8.3 What is a PySpark DataFrame?

Answer: A PySpark DataFrame is a distributed collection of structured data

or-ganized in named columns, similar to a relational database table It is op-timized for large-scale data processing and supports SQL queries

8.4 How do you import data into Delta Lake?

Answer: Data can be imported into Delta Lake using:

• Auto Loader: Automatically ingests new files as they arrive in a data

lake

• COPY INTO: A SQL command to load data into Delta tables.

• Spark API: Read data with Spark, transform it, and write it to Delta

Lake format

8.5 What is Databricks Connect?

Answer: Databricks Connect allows developers to connect local IDEs (e.g.,

Py-Charm, IntelliJ) to an Azure Databricks cluster It enables running and de-bugging Spark code locally while leveraging the cluster’s compute power

9 ETL Pipeline Interview Questions

9.1 What is the role of impact analysis in ETL systems?

Answer: Impact analysis examines metadata to determine how changes to a

data-staging object (e.g., a table) affect downstream processes It ensures that modifications do not disrupt data warehouse loading or dependent workflows

9.2 What SQL commands validate data completeness in ETL?

Answer: Use INTERSECT and MINUS queries:

• SOURCE MINUS TARGET and TARGET MINUS SOURCE identify mismatched rows

• If INTERSECT count is less than the source count, duplicates exist

9.3 How does ETL differ from OLAP tools?

Answer: • ETL Tools: Extract, transform, and load data into data warehouses

or marts

• OLAP Tools: Analyze data in warehouses/marts to generate business

reports and insights

9.4 Why is filtering data before joining more efficient in ETL?

Answer: Filtering data early reduces the number of rows processed,

minimiz-ing I/O, memory usage, and data transfer time Joinminimiz-ing filtered data avoids processing unnecessary records, improving ETL performance

9.5 What are ETL mapping sheets?

Answer: ETL mapping sheets detail the mapping between source and

destina-tion tables, including column mappings and lookup references They sim-plify writing complex data verification queries during ETL testing

10 Conclusion

This guide provides a robust foundation for data engineering interviews, cov-ering critical technologies and concepts To excel, practice these questions, ex-plore hands-on scenarios, and deepen your understanding of distributed sys-tems, cloud platforms, and data pipelines