70+ Big Data Engineer Interview Questions and Answers

Explaining partitioning, bucketing, joins, optimization, broadcast variables, ORC vs Parquet, RDD vs Dataframe, project architecture and responsibilities for Big Data Engineer role.

• Partitioning is dividing data into smaller chunks for parallel processing, while bucketing is organizing data into buckets based on a hash function.

• Types of joins in Spark include inner join, outer join, left join, right join, and full outer join.

• Optimization techniques in Spark include caching, re...read more

To check if a Fibonacci number is present between 100-200

• Generate Fibonacci numbers until 200

• Check if any number is between 100-200

• Use dynamic programming to optimize Fibonacci generation

Spark can handle upserts using merge() function

• Use merge() function to handle upserts in Spark

• Specify the primary key column(s) to identify matching rows

• Specify the update column(s) to update existing rows

• Specify the insert column(s) to insert new rows

• Example: df1.merge(df2, on='id', whenMatched='update', whenNotMatched='insert')

Spark and Hadoop have different architectures. DAG is a directed acyclic graph. Stage boundaries are logical divisions in a Spark job. Hive has partitioning and bucketing.

• Spark is an in-memory processing engine while Hadoop is a distributed file system.

• DAG is a graph of stages in a Spark job.

• Stage boundaries are logical divisions in a Spark job that help optimize execution.

• Partitioning in Hive is a way to divide a table into smaller, more manageable parts based on a column.

• Bu...read more

Remove duplicate records and find 5th highest salary department wise using SQL.

• Use DISTINCT keyword to remove duplicate records.

• Use GROUP BY clause to group the records by department.

• Use ORDER BY clause to sort the salaries in descending order.

• Use LIMIT clause to get the 5th highest salary.

• Combine all the above clauses to get the desired result.

Implement fault tolerance by using checkpointing, replication, and monitoring mechanisms.

• Enable checkpointing in Spark Streaming to save the state of the computation periodically to a reliable storage like HDFS or S3.

• Use replication in Kafka to ensure that data is not lost in case of node failures.

• Monitor the health of the Kafka and Spark clusters using tools like Prometheus and Grafana to detect and address issues proactively.

Spark configuration settings can be tuned to optimize query performance by adjusting parameters like memory allocation, parallelism, and caching.

• Increase executor memory and cores to allow for more parallel processing

• Adjust shuffle partitions to optimize data shuffling during joins and aggregations

• Enable dynamic allocation to scale resources based on workload demands

• Utilize caching to store intermediate results and avoid recomputation

• Monitor and analyze query execution plans ...read more

To handle data skew and partition imbalance in Spark, strategies include using salting, bucketing, repartitioning, and optimizing join operations.

• Use salting to evenly distribute skewed keys across partitions

• Implement bucketing to pre-partition data based on a specific column

• Repartition data based on a specific key to balance partitions

• Optimize join operations by broadcasting small tables or using partitioning strategies

Higher order functions, closures, anonymous functions, map, flatmap, and tail recursion are key concepts in functional programming.

• Higher order function: Functions that can take other functions as arguments or return functions as results.

• Closure: Functions that capture variables from their lexical scope, even when they are called outside that scope.

• Anonymous function: Functions without a specified name, often used as arguments to higher order functions.

• Map: A function that ap...read more

Speculative execution in Hadoop is a feature that allows the framework to launch duplicate tasks for a job, with the goal of completing the job faster.

• Speculative execution is used when a task is taking longer to complete than expected.

• Hadoop identifies slow-running tasks and launches duplicate tasks on other nodes.

• The first task to complete is used, while the others are killed to avoid duplication of results.

• This helps in improving job completion time and overall efficiency ...read more

Interview questions for Big Data Engineer role

• Java and Python are both popular programming languages for Big Data processing, but Java is preferred for its performance and scalability

• Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity

• MongoDB is a NoSQL database that is highly scalable and flexible, making it a good choice for Big Data applications

• To reverse a linked list, iterate through the list and change the directi...read more

Partitioning in Hive is a way of dividing a large table into smaller, more manageable parts based on a specific column.

• Partitioning improves query performance by reducing the amount of data that needs to be scanned.

• Partitions can be based on date, region, or any other relevant column.

• Hive supports both static and dynamic partitioning.

• Partitioning can be done on external tables as well.

We use Hadoop Distributed File System (HDFS) for our project.

• HDFS is a distributed file system designed to run on commodity hardware.

• It provides high-throughput access to application data and is fault-tolerant.

• HDFS is used by many big data processing frameworks like Hadoop, Spark, etc.

• It stores data in a distributed manner across multiple nodes in a cluster.

• HDFS is optimized for large files and sequential reads and writes.

Spark is faster than Hadoop MapReduce due to in-memory processing and supports multiple types of workloads.

• Spark performs in-memory processing, while Hadoop MapReduce writes to disk after each task.

• Spark supports multiple types of workloads like batch processing, interactive queries, streaming data, and machine learning, while Hadoop MapReduce is mainly for batch processing.

• Spark provides higher-level APIs in Java, Scala, Python, and R, making it easier to use than Hadoop Map...read more

Count pairs in an array that sum up to a target value.

• Iterate through the array and store the frequency of each element in a hashmap.

• For each element, check if the difference between the target and the element exists in the hashmap.

• Increment the count of pairs if the difference is found in the hashmap.

Hadoop Architecture is a distributed computing framework that allows for the processing of large data sets.

• Hadoop consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce.

• HDFS is responsible for storing data across multiple nodes in a cluster.

• MapReduce is responsible for processing the data stored in HDFS by dividing it into smaller chunks and processing them in parallel.

• Hadoop also includes other components such as YARN, which manages resources in...read more

To add data into a partitioned hive table, you can use the INSERT INTO statement with the PARTITION clause.

• Use INSERT INTO statement to add data into the table.

• Specify the partition column values using the PARTITION clause.

• Example: INSERT INTO table_name PARTITION (partition_column=value) VALUES (data);

Rank assigns a unique rank to each distinct row, while dense rank assigns consecutive ranks to rows with the same values.

• Rank function assigns unique ranks to each distinct row in the result set

• Dense rank function assigns consecutive ranks to rows with the same values

• Rank function leaves gaps in the ranking sequence if there are ties, while dense rank does not

Use 'hdfs diskbalancer' command to check disk utilisation and health in Hadoop

• Run 'hdfs diskbalancer -report' to get a report on disk utilisation

• Use 'hdfs diskbalancer -plan ' to generate a plan for balancing disk usage

• Check the Hadoop logs for any disk health issues

Binary search can be used to solve moderate problems of arrays that are first increasing and then decreasing.

• Use binary search to find the peak element in the array, which marks the transition from increasing to decreasing.

• Divide the array into two parts based on the peak element and apply binary search on each part separately.

• Handle edge cases such as when the array is strictly increasing or strictly decreasing.

• Example: ['1', '3', '5', '7', '6', '4', '2']

Core components of Spark include Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• Spark Core: foundation of the Spark platform, provides basic functionality for distributed data processing

• Spark SQL: module for working with structured data using SQL and DataFrame API

• Spark Streaming: extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams

• MLlib: machine learning library for Spark that provides scalabl...read more

Coalesce is used to reduce the number of partitions in a DataFrame or RDD, while repartition is used to increase the number of partitions.

• Coalesce is a narrow transformation that can only decrease the number of partitions.

• Repartition is a wide transformation that can increase or decrease the number of partitions.

• Coalesce is preferred over repartition when reducing the number of partitions.

• Repartition shuffles the data across the cluster, which can be an expensive operation.

• Ex...read more

Partitioning and bucketing are techniques used in Hive to improve query performance.

• Partitioning divides data into smaller, more manageable parts based on a specific column.

• Bucketing further divides data into equal-sized buckets based on a hash function.

• Partitioning and bucketing can be used together to optimize queries.

• Example: Partitioning by date column and bucketing by user ID column in a user activity log table.

Large Spark datasets can be handled by partitioning, caching, optimizing transformations, and tuning resources.

• Partitioning data to distribute workload evenly across nodes

• Caching frequently accessed data to avoid recomputation

• Optimizing transformations to reduce unnecessary processing

• Tuning resources like memory allocation and parallelism for optimal performance

Technologies related to big data include Hadoop, Spark, Kafka, and NoSQL databases.

• Hadoop - Distributed storage and processing framework for big data

• Spark - In-memory data processing engine for big data analytics

• Kafka - Distributed streaming platform for handling real-time data feeds

• NoSQL databases - Non-relational databases for storing and retrieving large volumes of data

Tuples are immutable and fixed in size, while lists are mutable and can change in size.

• Tuples are created using parentheses, while lists are created using square brackets.

• Tuples are faster than lists for iteration and accessing elements.

• Tuples are used for heterogeneous data types, while lists are used for homogeneous data types.

Lists are mutable ordered collections, tuples are immutable ordered collections, and sets are mutable unordered collections.

• Lists are mutable and ordered, allowing for duplicate elements. Example: [1, 2, 3, 3]

• Tuples are immutable and ordered, allowing for duplicate elements. Example: (1, 2, 3, 3)

• Sets are mutable and unordered, not allowing for duplicate elements. Example: {1, 2, 3}

SQL query to retrieve the highest employee salary

• Use the SELECT statement to retrieve the maximum salary from the employee table

• Use the MAX() function to find the highest salary value

• Combine the MAX() function with the SELECT statement to get the desired result

Spark internal working and optimization techniques

• Spark uses Directed Acyclic Graph (DAG) for optimizing workflows

• Lazy evaluation helps in optimizing transformations by combining them into a single stage

• Caching and persistence of intermediate results can improve performance

• Partitioning data can help in parallel processing and reducing shuffle operations

HDFS stands for Hadoop Distributed File System, a distributed file system designed to store and manage large amounts of data across multiple machines.

• HDFS is a key component of the Hadoop ecosystem, providing high-throughput access to application data.

• It is designed to be fault-tolerant, scalable, and reliable.

• HDFS divides files into blocks and stores multiple copies of each block across different nodes in a cluster.

• It allows for parallel processing of data across the cluster...read more

To create a pivot table in SQL from a non-pivot table, you can use the CASE statement with aggregate functions.

• Use the CASE statement to categorize data into columns

• Apply aggregate functions like SUM, COUNT, AVG, etc. to calculate values for each category

• Group the data by the columns you want to pivot on

Find the smallest subarray in an array that has a given target sum.

• Use a sliding window approach to find the subarray with the target sum.

• Keep track of the current sum of elements in the window and adjust the window size accordingly.

• Start with a window of size 1 and expand it until the sum is greater than or equal to the target sum.

• Shrink the window from the left side while updating the smallest subarray length until the sum is less than the target sum.

Spark memory optimisation techniques

• Use broadcast variables to reduce memory usage

• Use persist() or cache() to store RDDs in memory

• Use partitioning to reduce shuffling and memory usage

• Use off-heap memory to avoid garbage collection overhead

• Tune memory settings such as spark.driver.memory and spark.executor.memory

Spark Architecture is a distributed computing framework that provides high-level APIs for in-memory computing.

• Spark Architecture consists of a cluster manager, worker nodes, and a driver program.

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object.

• It supports various data sources like HDFS, Cassandra, HBase, etc.

• Spark Architectur...read more

Hadoop serialisation techniques are used to convert data into a format that can be stored and processed in Hadoop.

• Hadoop uses Writable interface for serialisation and deserialisation of data

• Avro, Thrift, and Protocol Buffers are popular serialisation frameworks used in Hadoop

• Serialisation can be customised using custom Writable classes or external libraries

• Serialisation plays a crucial role in Hadoop performance and efficiency

Cloud in big data refers to using cloud computing services to store, manage, and analyze large volumes of data.

• Cloud computing allows for scalable and flexible storage of big data

• It provides on-demand access to computing resources for processing big data

• Examples include AWS, Google Cloud, and Microsoft Azure

Spark basics include RDDs, transformations, actions, and optimizations like caching and partitioning.

• RDDs (Resilient Distributed Datasets) are the fundamental data structure in Spark

• Transformations like map, filter, and reduceByKey are used to process data in RDDs

• Actions like count, collect, and saveAsTextFile trigger execution of transformations

• Optimization techniques include caching frequently accessed data and partitioning RDDs for parallel processing

• Example: Caching an RD...read more

Hive metastore is a central repository that stores metadata for Hive tables, including schema and location.

• Hive metastore is used to manage metadata for Hive tables.

• It stores information about the schema, location, and other attributes of tables.

• The metastore can be configured to use different databases, such as MySQL or PostgreSQL.

• It allows for sharing metadata across multiple Hive instances.

• The metastore can be accessed using the Hive metastore API or through the Hive comma...read more

Functions in SQL are built-in operations that can be used to manipulate data or perform calculations within a database.

• Functions in SQL can be used to perform operations on data, such as mathematical calculations, string manipulation, date/time functions, and more.

• Examples of SQL functions include SUM(), AVG(), CONCAT(), UPPER(), LOWER(), DATE_FORMAT(), and many others.

• Functions can be used in SELECT statements, WHERE clauses, ORDER BY clauses, and more to manipulate data as ...read more

Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

• Apache Spark is designed for speed and ease of use in processing large amounts of data.

• It can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.

• Spark provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general execution graphs.

• It al...read more

Hive Architecture is a data warehousing infrastructure built on top of Hadoop for querying and analyzing large datasets.

• Hive uses a language called HiveQL which is similar to SQL for querying data stored in Hadoop.

• It organizes data into tables, partitions, and buckets to optimize queries and improve performance.

• Hive metastore stores metadata about tables, columns, partitions, and their locations.

• Hive queries are converted into MapReduce jobs to process data in parallel across...read more

Partition in Hive is a way to organize data in a table into multiple directories based on the values of one or more columns.

• Partitions help in improving query performance by allowing Hive to only read the relevant data directories.

• Partitions are defined when creating a table in Hive using the PARTITIONED BY clause.

• Example: CREATE TABLE table_name (column1 INT, column2 STRING) PARTITIONED BY (column3 STRING);

Vectorization is the process of converting data into a format that can be easily processed by a computer's CPU or GPU.

• Vectorization allows for parallel processing of data, improving computational efficiency.

• It involves performing operations on entire arrays or matrices at once, rather than on individual elements.

• Examples include using libraries like NumPy in Python to perform vectorized operations on arrays.

• Vectorization is commonly used in machine learning and data analysis ...read more

Spark architecture is a distributed computing framework that consists of a cluster manager, a distributed storage system, and a processing engine.

• Spark architecture is based on a master-slave architecture.

• The cluster manager is responsible for managing the resources of the cluster.

• The distributed storage system is used to store data across the cluster.

• The processing engine is responsible for executing the tasks on the data stored in the cluster.

• Spark architecture supports var...read more

Design a system for a web surfing utility

• Use a web crawler to gather data from websites

• Implement a search engine to retrieve relevant information

• Utilize a recommendation system to suggest related content

• Include user authentication and personalized settings

• Ensure scalability and performance for handling large amounts of data

Python has built-in functions for manipulating lists and strings.

• Lists are mutable and can be modified using various methods like append(), insert(), remove(), etc.

• Strings are immutable and can be manipulated using slicing, concatenation, and various string methods.

• List comprehension and string formatting are powerful tools for working with lists and strings.

• Python also has powerful libraries like NumPy and Pandas for working with large datasets.

Creating triggers in a database involves defining the trigger, specifying the event that will activate it, and writing the code to be executed.

• Define the trigger using the CREATE TRIGGER statement

• Specify the event that will activate the trigger (e.g. INSERT, UPDATE, DELETE)

• Write the code or actions to be executed when the trigger is activated

• Test the trigger to ensure it functions as intended