Big data systems and tools like Hadoop, Spark, and NoSQL databases are being adopted by more organizations than ever before. As a result, big data skills are highly sought after for roles like data engineers, data analysts, data scientists, and software engineers. Preparing for big data interview questions ahead of time can help you stand out in the hiring process.
This article contains a set of the 20 most common big data interview questions that recruiters and hiring managers likely to ask with detailed answers. The questions cover Hadoop, Spark, data warehousing concepts, analytics tools, and more. Reviewing these questions and answers will help you walk into your next big data job interview fully prepared to showcase your knowledge and land the role you want!
Big Data Interview Questions
1- What is Big Data?
Big data refers to large, complex datasets comprised of a variety of data types and sources that traditional data processing systems struggle to handle. Key attributes that characterize big data include high volume, high velocity, and high variety. Big data systems are designed to enable scalable and flexible analysis of these huge datasets through massively parallel software frameworks running on clusters of commodity hardware.
2- What are the main characteristics of Big Data?
The three main characteristics that define big data are known as the three V’s:
- Volume: Scale of data in terabytes, petabytes and beyond
- Velocity: Rate at which new data is generated from high frequency data streams
- Variety: Different types of structured, semi-structured and unstructured data from various sources
3- What are some common sources of Big Data?
Some common sources that are generating extremely large data volumes today include:
- Social media platforms like Facebook, Twitter, Instagram
- Public web site traffic logs
- Purchase transaction records from ecommerce sites
- Sensor data from IoT devices like wearables, smart home appliances
- Server log data
- Satellite imagery datasets
- Genomics and biomedical datasets
- Machine telemetry from industrial equipment
- Call detail records from telecommunications companies
4- What technologies are commonly used in the Big Data ecosystem?
The most common technologies include:
- Apache Hadoop: Open source big data storage and distributed processing framework
- Apache Spark: Unified data processing engine for large-scale data workloads
- NoSQL databases: Distributed high performance databases like HBase, Cassandra, MongoDB
- Kafka: Distributed streaming platform
- Tableau: Data analysis and visualization software
- R and Python: Programming languages with rich libraries for analytics
- Amazon EMR: Managed Hadoop framework offered through Amazon cloud services
5- Explain the Hadoop Distributed File System (HDFS).
HDFS is the primary data storage layer of Hadoop. Key features include:
- Distributed file system designed to run on commodity hardware
- Highly fault-tolerant with replication and redundancy
- Breaks large datasets into blocks and distributes across the cluster
- Parallel streaming access high throughput access across concurrently running jobs
- Best suited for sequential reads and writes of very large files once stored
6- What are the main components of a Hadoop cluster?
The key components in a Hadoop cluster are:
- NameNode: Master node which hosts the filesystem metadata and coordinates job scheduling
- DataNodes: Worker nodes which store actual dataset chunks and run tasks
- YARN: Cluster resource manager responsible for job scheduling and working across nodes
- MapReduce: Programming framework for writing distributed processing logic
- HDFS: Hadoop Distributed Filesystem for scalable, reliable storage
7- Explain MapReduce in simple terms.
MapReduce is a programming paradigm for writing distributed data processing programs at scale. The key steps include:
- Map: This stage runs in parallel across partitioned dataset chunks, applying mapping logic to transform the input
- Combine: Local aggregation of output from mappers
- Shuffle: Transfer intermediate outputs to appropriate reducers
- Reduce: Concentrates outputs from map stage and runs reduction logic to derive final outputs
8- How is Apache Spark different from MapReduce?
While both are large-scale distributed data processing frameworks, Spark differs in major ways:
- Much faster through in-memory processing avoiding disk writes
- Unified engine supporting SQL, streaming, machine learning within one system
- General execution model beyond just map and reduce functions
- Advanced optimizations using directed acyclic graphs, caching and partitioning
- Reuse of working sets across workflows leading to faster runs when data is cached
9- What are the key capabilities of Spark?
- Spark SQL – Query structured data inside Spark programs using SQL syntax
- Spark Streaming – Stream processing framework with results in sub-second latency
- MLlib – Distributed machine learning library above Spark
- GraphX – Graph algorithms and graph processing
- Spark R – Supports executing R data analysis alongside Spark processing
10- Explain Resilient Distributed Datasets (RDDs).
RDDs are the core programming abstraction in Spark for distributed dataset processing. Key traits:
- Resilient – Can reconstruct lost data using lineage
- Distributed – Data broken into partitions across nodes
- Collection of objects – Represent a dataset as objects in native languages
- Lazily evaluated – Compute not triggered until action invoked
- Can persist in memory – Much faster reuse for iterative algorithms
11- What are the functions of Spark Driver?
The Spark Driver is responsible for:
- Maintaining information about Spark Application
- Responding to user program or input
- Analyzing, distributing, scheduling for executors to run
- Tracking status and results with help of Cluster Manager
- Providing interfaces to outside world to submit jobs
- Monitoring work across executors and re-running failed tasks
12- What is YARN?
YARN stands for Yet Another Resource Negotiator. It is Hadoop’s cluster resource management system responsible for:
- Managing and scheduling compute resources on cluster
- Handling jobs submitted by Spark Driver
- Working with MapReduce and Spark executors to run all tasks
- Ensuring high cluster utilization across frameworks like Hive, Pig and more
- Enabling dynamic scaling by allocating containers across available hardware
13- Explain data partitioning in Spark?
Data partitioning refers to breaking large datasets into smaller partitions which are spread across worker nodes with considerations of:
- Number of partitions
- Partition function like hash/range partitioning
- Ensure roughly equal sized partitions
- Minimize data shuffled across network
- Optimize for required processing workflow
14- What are benefits of using Parquet file format?
Benefits of the Parquet columnar format for big data analytics include:
- Efficient compression – Smaller files saving storage costs
- Fast retrieval of columns – Avoid scanning irrelevant data
- Built-in indexes for queries – Speed up analytic workflows
- Splittable for parallel processing – Improves throughput
- Interoperability across ecoystem – Spark, MapReduce, Hive
15- How can you minimize data shuffling in Spark jobs?
Methods to reduce shuffles across stages which add overhead:
- Set number of reducers correctly
- Reuse RDDs across Stages using persist()
- Broadcast large RDD if used in all partitions
- Use coalesce to reduce partitions
- Configure proper partitioning from beginning
16- What are the functions of Spark Executors?
Key responsibilities include:
- Execute code assigned by spark driver
- Report state and results to driver node
- Read & write datasets from storage
- Cache and spill when out of memory
- Carry out assigned operations on partitioned data
17- How can you trigger automatic clean-ups in Spark to manage memory limits?
Saving intermediate RDDs to disk instead of memory once thresholds are reached can help manage memory constraints automatically:
- Set spark.cleaner.ttl to duration for which RDD should be persisted in memory
- Configure spark.storage.threshold for length of idle time before cleanup
- Allow spark.cleaner.referenceTracking to better optimize cleanup
18- What are the key benefits using Spark over MapReduce?
- Ease of use – Concise APIs in Scala, Java, Python
- Speed – Up to 100x faster through in-memory computing
- Near Real Time Processing – Super low latency with Spark Streaming
- Unified Platform – One engine for ETL, SQL, ML, Graph workloads
- Flexible – On-prem or cloud deployments and integrations with data lakes, message queues
19- What are some key components of a Spark Streaming architecture?
Core components enabling real-time stream analytics:
- Data ingestion from message queues, file systems
- Submit long running Spark jobs with mini-batches
- Transformations using map, reduce, joins
- Machine learning model predictions
- Push results to databases, dashboards and applications
20- How does Spark handle failures?
Key mechanisms to ensure recoveries:
- Lineage graph tracks RDD operations
- Can regenerate lost data partitions
- Driver can restart failed executors
- Intermediate data persists in memory or disk
- Checkpointing for long workflows
- Big Data Main Concepts
- Big Data Programming Languages
- Big Data Analytics Tools
- Can Big Data Predict The Future?
- Can I Learn Big Data Without Java?
- Can Big Data Protect A Firm From Competition?