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Top Interview Questions
Big Data refers to extremely large and complex sets of data that cannot be effectively captured, stored, processed, or analyzed using traditional data management tools and techniques. In today’s digital world, data is generated at an unprecedented rate from diverse sources such as social media platforms, mobile devices, sensors, business transactions, websites, and Internet of Things (IoT) devices. Big Data plays a crucial role in helping organizations gain insights, improve decision-making, enhance customer experiences, and create competitive advantages.
Big Data is not just about large volumes of data; it also involves managing and analyzing data that is fast-moving, diverse in format, and uncertain in nature. Traditional databases and data processing systems struggle to handle such data efficiently. As a result, new technologies, architectures, and analytical approaches have emerged to address the challenges posed by Big Data.
The concept of Big Data is commonly described using the five Vs:
Volume – Refers to the massive amount of data generated every second. Organizations collect terabytes and petabytes of data from multiple sources, including customer interactions, transaction records, and machine-generated logs.
Velocity – Indicates the speed at which data is generated, processed, and analyzed. Real-time or near-real-time data processing is often required in applications such as fraud detection, stock trading, and online recommendations.
Variety – Represents the different types of data, including structured data (databases, spreadsheets), semi-structured data (XML, JSON), and unstructured data (text, images, videos, audio).
Veracity – Refers to the quality and reliability of data. Big Data often contains inconsistencies, missing values, or inaccuracies, making data validation and cleansing critical.
Value – Highlights the importance of extracting meaningful insights from data. The true worth of Big Data lies in the actionable intelligence it provides, not just in its size.
A typical Big Data architecture is designed to handle large-scale data ingestion, storage, processing, and analysis. It usually consists of the following layers:
Data Sources: Data is collected from various sources such as databases, social media platforms, sensors, applications, and logs.
Data Ingestion: Tools like Apache Kafka, Flume, or Sqoop are used to collect and transfer data into the system.
Data Storage: Distributed storage systems such as Hadoop Distributed File System (HDFS), cloud storage, or NoSQL databases store large volumes of data efficiently.
Data Processing: Frameworks like Apache Hadoop MapReduce and Apache Spark process data in batch or real-time modes.
Data Analytics and Visualization: Analytical tools and dashboards help users explore data and generate insights for decision-making.
Several technologies and frameworks support Big Data processing:
Apache Hadoop: An open-source framework that enables distributed storage and processing of large datasets across clusters of computers.
Apache Spark: A fast, in-memory data processing engine that supports batch processing, streaming, machine learning, and graph analytics.
NoSQL Databases: Databases such as MongoDB, Cassandra, and HBase are designed to handle unstructured and semi-structured data.
Data Warehouses and Data Lakes: Modern data warehouses and data lakes store raw and processed data for analytics and reporting.
Cloud Platforms: Cloud services like AWS, Azure, and Google Cloud provide scalable infrastructure and managed Big Data services.
Big Data is widely used across industries to solve complex problems and uncover valuable insights:
Healthcare: Analyzing patient records, medical images, and wearable device data to improve diagnosis, treatment, and preventive care.
Finance: Detecting fraud, managing risk, and analyzing market trends in real time.
Retail and E-commerce: Understanding customer behavior, personalizing recommendations, and optimizing inventory management.
Manufacturing: Monitoring equipment performance and predicting failures using sensor data.
Telecommunications: Improving network performance and customer retention through usage analytics.
Government and Smart Cities: Enhancing public services, traffic management, and resource utilization.
The adoption of Big Data offers numerous advantages:
Better Decision-Making: Data-driven insights lead to more informed and accurate business decisions.
Cost Optimization: Identifying inefficiencies and optimizing processes reduces operational costs.
Improved Customer Experience: Personalized services and targeted marketing enhance customer satisfaction.
Innovation: Big Data enables organizations to develop new products, services, and business models.
Competitive Advantage: Companies that effectively leverage Big Data can outperform their competitors.
Despite its benefits, Big Data presents several challenges:
Data Security and Privacy: Protecting sensitive data from breaches and ensuring compliance with regulations.
Data Quality: Ensuring accuracy, consistency, and reliability across large datasets.
Scalability: Managing rapidly growing data volumes while maintaining performance.
Skill Shortage: Lack of skilled professionals such as data engineers, data scientists, and analysts.
Integration Complexity: Combining data from diverse sources and systems can be difficult.
The future of Big Data is closely tied to advancements in artificial intelligence (AI), machine learning (ML), and cloud computing. As data continues to grow, organizations will increasingly rely on automated analytics, real-time processing, and intelligent systems to extract insights. Technologies such as edge computing, data mesh, and augmented analytics are shaping the next generation of Big Data solutions.
Answer:
Big Data refers to extremely large volumes of data that cannot be processed efficiently using traditional databases and data-processing tools. This data can be structured, semi-structured, or unstructured and is generated from sources such as social media, sensors, mobile devices, transactions, and logs. Big Data technologies help store, process, and analyze this data to extract valuable insights.
Answer:
Big Data is commonly described using the 5 V’s:
Volume – Huge amounts of data (terabytes to petabytes).
Velocity – Speed at which data is generated and processed (real-time or near real-time).
Variety – Different data formats (text, images, videos, JSON, XML).
Veracity – Quality and reliability of data.
Value – Useful insights derived from data.
Answer:
Big Data helps organizations:
Make better business decisions
Understand customer behavior
Improve operational efficiency
Detect fraud and security threats
Enable predictive analytics and AI models
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Structured Data – Organized data in rows and columns (e.g., RDBMS tables).
Semi-Structured Data – Partial structure (e.g., JSON, XML, CSV).
Unstructured Data – No predefined format (e.g., images, videos, social media posts).
Answer:
Apache Hadoop is an open-source Big Data framework used to store and process large datasets across distributed systems. It provides fault tolerance, scalability, and high availability.
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Hadoop has three main components:
HDFS (Hadoop Distributed File System) – Storage layer
YARN (Yet Another Resource Negotiator) – Resource management
MapReduce – Data processing framework
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HDFS is a distributed file system that stores data across multiple machines. It breaks large files into blocks and distributes them across nodes to ensure fault tolerance and high availability.
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MapReduce is a programming model used to process large datasets in parallel.
Map – Processes input data and produces key-value pairs
Reduce – Aggregates and processes the output from Map tasks
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YARN manages cluster resources and schedules jobs. It allows multiple data-processing engines (like Spark, Hive) to run on Hadoop efficiently.
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Apache Spark is a fast, in-memory Big Data processing engine. It supports batch processing, real-time streaming, machine learning, and graph processing.
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| Hadoop | Spark |
|---|---|
| Disk-based processing | In-memory processing |
| Slower | Much faster |
| Uses MapReduce | Uses DAG engine |
| Good for batch | Supports batch & streaming |
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Apache Hive is a data warehouse tool used for querying and analyzing large datasets stored in Hadoop using SQL-like language (HiveQL).
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Apache Pig is a high-level scripting platform for analyzing large datasets. It uses Pig Latin, which is easier than writing MapReduce programs.
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HBase is a NoSQL column-oriented database that runs on HDFS. It is used for real-time read/write access to Big Data.
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NoSQL databases are non-relational databases designed to handle large volumes of unstructured or semi-structured data with high scalability.
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Key-Value Stores – Redis
Document Stores – MongoDB
Column-Family Stores – HBase, Cassandra
Graph Databases – Neo4j
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Data replication means storing multiple copies of data blocks across different nodes to ensure fault tolerance. Default replication factor is 3.
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Data locality means moving computation closer to where data resides instead of moving data across the network, improving performance.
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Big Data Analytics involves analyzing large datasets to discover patterns, trends, and insights using tools like Hadoop, Spark, and machine learning algorithms.
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Batch processing processes large volumes of data at once after a specific time interval (e.g., daily sales reports).
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Real-time processing handles data instantly as it arrives (e.g., fraud detection, live streaming analytics).
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Apache Kafka is a distributed messaging system used for real-time data streaming between applications.
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ETL stands for Extract, Transform, Load. It extracts data from sources, transforms it into a usable format, and loads it into a data warehouse or Big Data system.
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Data ingestion is the process of collecting and importing data into Big Data systems from various sources.
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Fault tolerance ensures the system continues to function even if hardware or software failures occur, mainly achieved through replication and distributed architecture.
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Scalability is the ability to increase system capacity by adding more nodes rather than upgrading existing hardware.
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Cloud Big Data uses cloud platforms (AWS, Azure, GCP) to store and process large datasets with flexibility and cost efficiency.
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Recommendation systems
Fraud detection
Social media analytics
Healthcare analytics
Financial risk analysis
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SQL and basic programming (Java/Python)
Hadoop & Spark basics
Linux commands
Data concepts and analytics basics
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Data security
Data quality
Storage and processing costs
Managing complex architectures
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Data partitioning is the process of dividing large datasets into smaller, manageable parts called partitions. Each partition is processed independently, which improves performance and parallelism. In Hadoop and Spark, partitioning helps distribute data across multiple nodes.
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A data block is the minimum unit of storage in HDFS.
Default block size: 128 MB
Large block sizes reduce the number of disk seeks and improve performance for large files.
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The NameNode is the master node of HDFS. It manages:
File system metadata
File permissions
Block locations
It does not store actual data.
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DataNodes store the actual data blocks in HDFS. They perform read/write operations and report their status to the NameNode.
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The Secondary NameNode periodically takes checkpoints of metadata. It does not replace the NameNode but helps reduce recovery time during failures.
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A Hadoop cluster is a group of machines (nodes) connected together to store and process Big Data.
Master nodes – NameNode, ResourceManager
Worker nodes – DataNodes, NodeManagers
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Schema-on-read means data is structured when it is read, not when stored. Hadoop follows this approach, allowing flexible data storage.
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| Schema-on-Read | Schema-on-Write |
|---|---|
| Applied at read time | Applied at write time |
| Flexible | Rigid |
| Used in Hadoop | Used in RDBMS |
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Apache Sqoop is used to transfer data between RDBMS and Hadoop efficiently.
Example: Importing data from MySQL into HDFS.
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Apache Flume is used to collect and move large amounts of log data into HDFS from multiple sources.
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Serialization converts data into a format suitable for storage or transmission.
Common formats: Avro, Parquet, ORC
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Avro is a row-based serialization format that supports schema evolution and is commonly used for data exchange.
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Parquet is a columnar storage format optimized for analytics and query performance. It reduces storage and improves read speed.
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ORC (Optimized Row Columnar) is a columnar format mainly used with Hive for fast query execution.
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Compression reduces data size to save storage and improve processing speed.
Examples: Snappy, Gzip, LZO
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RDD (Resilient Distributed Dataset) is Spark’s core data structure. It is:
Immutable
Distributed
Fault tolerant
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Transformations – Create new RDDs (map, filter)
Actions – Return results (count, collect)
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A DataFrame is a distributed collection of data organized into named columns, similar to a table in SQL.
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Spark SQL allows querying structured data using SQL and integrates with Hive metastore.
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Spark Streaming processes real-time data streams in micro-batches.
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Data skew occurs when data is unevenly distributed across partitions, causing performance issues.
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Cluster computing involves multiple computers working together as a single system to process Big Data efficiently.
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Big Data security includes authentication, authorization, encryption, and data governance.
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Kerberos is an authentication protocol used in Hadoop for secure access.
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A data lake stores raw structured and unstructured data in its native format.
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| Data Lake | Data Warehouse |
|---|---|
| Raw data | Processed data |
| Schema-on-read | Schema-on-write |
| Low cost | Higher cost |
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Machine learning in Big Data uses algorithms to analyze large datasets and make predictions using tools like Spark MLlib.
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MLlib is Spark’s machine learning library.
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Data governance defines rules, policies, and standards for data usage, security, and quality.
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A data pipeline is a set of processes that move data from source to destination automatically.
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Data lineage tracks the origin, movement, and transformation of data.
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Hadoop recovers from failures using data replication and task re-execution.
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Hadoop runs duplicate tasks for slow-running jobs to improve performance.
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Big Data testing validates data accuracy, performance, and scalability of Big Data systems.
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Data sources
Data ingestion
Storage (HDFS, Data Lake)
Processing (Spark, Hive)
Analytics and visualization
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Confusing Hadoop with Spark
Ignoring data formats
Lack of SQL knowledge
Not understanding use cases
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An e-commerce company analyzes customer purchase data to recommend products and improve sales.
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Java
Python
Scala
SQL
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Metadata is data about data, such as file size, format, and schema.
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SQL helps query and analyze large datasets easily using Hive and Spark SQL.
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In 4 years of experience, a candidate might work with:
YARN for resource management and multi-tenancy
High Availability (HA) NameNode setup
HDFS Federation for scaling metadata
Data compression using Snappy, ORC, or Parquet
Security via Kerberos authentication and Ranger/Atlas
HDFS snapshots for backup and recovery
Answer:
| Feature | Hadoop 1.x | Hadoop 2.x |
|---|---|---|
| Resource Management | MapReduce only | YARN (supports multiple engines) |
| Scalability | Limited | High, supports 10k+ nodes |
| High Availability | No | Yes, Active/Standby NameNode |
| Cluster Utilization | Low | Better through YARN |
| Processing Engines | MapReduce | MapReduce, Spark, Tez, Storm |
Answer:
HDFS Federation allows multiple NameNodes to manage namespaces independently, improving scalability. Needed when a single NameNode cannot handle millions of files in a large cluster.
Answer:
YARN has:
ResourceManager (RM) – Master, allocates resources
NodeManager (NM) – Manages resources and tasks on nodes
ApplicationMaster (AM) – Manages a single application
Container – Execution unit with allocated CPU/memory
YARN separates resource management from processing, enabling multiple frameworks to run on Hadoop.
Answer:
Adjust block size based on file size
Enable compression for storage efficiency
Use combiner functions to reduce network I/O
Proper partitioning to avoid data skew
Memory tuning for MapReduce and YARN containers
Avoid small files; use HAR files or sequence files
Answer:
HDFS is optimized for large files; small files increase NameNode memory usage. Solutions:
Use SequenceFile or HAR files
Merge small files using Spark/Hive
Use HBase for random access storage
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Spark builds a Directed Acyclic Graph (DAG) for all transformations. The DAG scheduler:
Divides jobs into stages
Optimizes task execution
Supports fault tolerance
Unlike Hadoop MapReduce, Spark executes DAGs in-memory for faster processing.
Answer:
Lazy evaluation means Spark does not execute transformations immediately; it builds a DAG and executes only on an action (e.g., collect, count).
Benefits:
Optimized execution
Reduced data shuffling
Faster processing
Answer:
Use salting of keys for joins
Repartition using repartition() or coalesce()
Filter skewed partitions separately
Use broadcast joins for small lookup tables
Answer:
| Feature | Narrow | Wide |
|---|---|---|
| Shuffle required | No | Yes |
| Examples | map, filter | reduceByKey, join |
| Performance | Fast | Slower, network-heavy |
Answer:
Adjust executor memory and cores
Increase parallelism (spark.default.parallelism)
Use broadcast variables for small tables
Cache frequently used RDDs/DataFrames
Optimize joins and shuffle operations
Use columnar formats (Parquet/ORC)
Answer:
| Feature | Spark Streaming | Kafka Streams |
|---|---|---|
| Processing | Micro-batch | True streaming |
| Latency | Seconds | Milliseconds |
| Language | Scala, Python, Java | Java, Scala |
| Use Case | Batch + Streaming | Real-time analytics |
Answer:
Use YARN ResourceManager UI for job status
Check logs in HDFS (stderr, stdout)
Use Spark UI for DAG visualization
Use Ganglia/Prometheus for cluster monitoring
Answer:
Ingestion: Kafka/Flume/Sqoop
Storage: HDFS, S3, or HBase
Processing: Spark (batch/streaming)
Analytics: Hive, Spark SQL, MLlib
Visualization: Tableau, Power BI, or custom dashboards
Answer:
Master – Manages regions
RegionServer – Handles read/write requests
HFiles – Stores data
Use Cases:
Time-series data
Real-time analytics
IoT data storage
| Feature | HBase | Hive |
|---|---|---|
| Data type | NoSQL | SQL-like |
| Access | Random | Batch queries |
| Speed | Low latency | High latency |
| Use case | Real-time | Reporting |
Answer:
Use RDD lineage to recompute lost partitions
Enable checkpointing for long-running jobs
Use HDFS replication to store persistent data
Answer:
Enable Kerberos authentication
Use HDFS permissions and ACLs
Use Apache Ranger for fine-grained authorization
Encrypt data at rest and in transit
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Apache Oozie – Workflow scheduler for Hadoop jobs
Airflow – DAG-based orchestration for ETL pipelines
NiFi – Real-time data flow management
Answer:
Add new columns with default values
Use nullable fields
Maintain backward/forward compatibility with Parquet or Avro
Answer:
Data validation – Compare source and processed data
Performance testing – Measure job execution time
Integration testing – End-to-end pipeline testing
Boundary testing – Handle nulls, empty files, corrupt records
Answer:
Use partitioning and bucketing
Use columnar formats (ORC/Parquet)
Enable vectorized query execution
Minimize cross-joins and cartesian joins
Use Tez or Spark execution engine
Answer:
When one table is small enough to fit in memory, broadcast it to all worker nodes to avoid shuffling large datasets, improving performance.
Answer:
Checkpointing saves RDDs/DataFrames to HDFS to prevent recomputation in case of failures.
Used in long lineage DAGs or streaming applications.
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Lambda architecture handles batch + real-time processing:
Batch layer – Stores all data, performs batch analytics
Speed layer – Handles real-time stream processing
Serving layer – Combines batch and real-time views for querying
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Kappa architecture processes all data as real-time streams; batch layer is not used. Simplifies maintenance and reduces redundancy.
Answer:
Tableau, Power BI
Superset, Kibana
Zeppelin, Jupyter notebooks
Custom dashboards via Python/Scala
Answer:
Ingestion: Flume/Kafka for streaming logs
Storage: HDFS/S3 with compression
Processing: Spark batch jobs or streaming pipeline
Analytics: Hive/Spark SQL, aggregations
Visualization: Dashboard with alerting
Answer:
Identify data skew and repartition
Cache intermediate results
Adjust executor memory and cores
Optimize joins using broadcast variables
Use columnar formats and filter early
Handling large-scale cluster tuning
Optimizing real-time pipelines
Ensuring data security & compliance
Managing multi-tenant environments
Debugging complex workflows
Answer:
Hadoop HA ensures NameNode failure does not stop the cluster.
Active NameNode serves requests, Standby NameNode keeps metadata synchronized.
Uses Zookeeper to manage automatic failover.
Implementation steps:
Configure two NameNodes in HA mode
Use shared storage (NFS or Quorum Journal Manager)
Enable automatic failover using Zookeeper
Answer:
Counters are user-defined or system metrics in MapReduce to monitor job progress.
Examples:
Number of processed records
Number of failed records
Number of skipped corrupt files
Useful for debugging and performance monitoring.
Answer:
Check job logs in ResourceManager UI
Analyze TaskTracker/NodeManager logs
Use counters to track data processed
Identify skewed input splits
Check memory or disk issues
Answer:
Partitioning divides data across nodes to enable parallel processing.
Narrow transformations do not cause shuffle; wide transformations (join, reduceByKey) cause data shuffle, which can slow performance.
Optimizations:
Use partitionBy or salting
Reduce shuffles with broadcast joins
Coalesce or repartition strategically
Answer:
Runs duplicate tasks for slow-running jobs to finish faster.
Prevents straggler tasks from delaying the job.
Controlled by configuration: mapreduce.map.speculative and mapreduce.reduce.speculative.
Answer:
Spark SQL uses Catalyst Optimizer for query optimization.
Performs logical plan generation → optimization → physical plan → code generation.
Improves execution speed for DataFrames and SQL queries.
Answer:
Broadcast Variables: Read-only variables shared across all nodes to avoid sending large data multiple times.
Accumulators: Write-only counters used for aggregation (e.g., counting errors).
Answer:
Broadcast small tables
Apply salting on the skewed key
Filter skewed keys and handle them separately
Use repartitionByRange
Answer:
Tungsten is Spark’s memory and CPU optimization framework:
Uses off-heap memory
Optimizes code generation for CPU efficiency
Reduces garbage collection overhead
Answer:
Use Spark UI for DAG visualization, stage/task metrics
Enable event logs for offline analysis
Adjust executor memory, cores, shuffle partitions
Cache frequently used datasets
Answer:
Kafka is a distributed streaming platform. Components:
Producer: Sends messages to Kafka topics
Consumer: Reads messages from topics
Broker: Kafka server storing messages
Zookeeper: Manages cluster coordination
Topic & Partition: Logical and physical division of messages
Answer:
Partitions allow parallelism and scalability.
Offset is a unique sequential ID of a message in a partition.
Consumers track offsets for fault-tolerant consumption.
Answer:
Use idempotent producers to avoid duplicates
Enable transactional producers for atomic writes
Consumers commit offsets after processing
Answer:
Similar to triggers/stored procedures in RDBMS.
Observer: Intercepts CRUD operations
Endpoint: Performs batch processing on server side
Reduces network overhead by moving computation to servers
Answer:
Use row keys wisely to avoid hotspotting
Enable block cache for frequently read data
Use bloom filters to reduce disk access
Tune memstore and block sizes
Answer:
Use partitioning and bucketing
Store data in ORC/Parquet formats
Use vectorized queries
Avoid cartesian joins, use map-side joins
Enable Cost-Based Optimizer (CBO)
Answer:
ACID in Hive ensures atomicity, consistency, isolation, durability for inserts, updates, deletes.
Requires transactional tables with ORC format.
Enables compaction to reduce delta files.
Answer:
ZooKeeper is a coordination service for distributed applications.
Roles:
Manage leader election
Maintain configuration synchronization
Support HA failover
Answer:
Ingest data using Kafka or Flume
Process streams using Spark Streaming or Flink
Apply business rules/thresholds
Push alerts via email, Slack, or dashboards
Maintain logs for audit
Answer:
Increase block size to reduce the number of splits
Use CombineFileInputFormat to merge small files
Optimize mapper and reducer code
Enable compression to reduce disk I/O
Answer:
Ingestion: Kafka/S3/Flume
Parsing: Use Spark DataFrames or Datasets
Storage: Parquet format for analytics
Processing: Partition and cache data, avoid shuffles
ETL/aggregation: Use Spark SQL
Visualization: Tableau or Superset
Answer:
Enable Kerberos authentication
Configure HDFS permissions and ACLs
Use Ranger/Atlas for fine-grained authorization
Enable data encryption at rest and in transit
Monitor logs for suspicious activities
Answer:
| Feature | Lambda | Kappa |
|---|---|---|
| Layers | Batch + Speed + Serving | Only stream layer |
| Complexity | High | Simpler |
| Use Case | Historical + Real-time | Stream-only pipelines |
| Tools | Spark, Kafka | Kafka + Spark/Flink |
Answer:
Adjust block size (128–256 MB)
Set replication factor based on reliability
Enable compression (Snappy, LZO)
Co-locate compute and storage nodes for data locality
Avoid small files problem
Answer:
Data lineage tracks origin, transformations, and movement of data.
Helps in debugging, auditing, and compliance.
Tools: Apache Atlas, Talend, Informatica.
