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

I have utilized optimization techniques such as indexing, caching, and parallel processing in my projects.

• Implemented indexing on large datasets to improve query performance

• Utilized caching to store frequently accessed data and reduce load times

• Implemented parallel processing to speed up data processing tasks

Using SQL's RANK function with PARTITION BY to assign ranks based on col1 values.

• RANK() function assigns a unique rank to each distinct value in the partition.

• Identical values receive the same rank, and the next rank is skipped.

• Example: For values 100, 100, the rank is 1 for both.

• For values 200, 200, the rank is also 1 for both, continuing this pattern.

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

Cache is temporary storage used to store frequently accessed data for quick retrieval, while persistence refers to storing data permanently.

• Cache is temporary and volatile, while persistence is permanent and non-volatile

• Cache is typically faster to access than persistence

• Examples of cache include browser cache, CPU cache, and in-memory cache systems like Redis

• Examples of persistence include databases like MySQL, PostgreSQL, and file systems like HDFS

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.

Convert a list of dictionaries to CSV in Python

• Use the csv module to write to a file or StringIO object

• Use the keys of the first dictionary as the header row

• Loop through the list and write each dictionary as a row

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

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

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

Spark is a fast and distributed data processing engine that can perform in-memory processing.

• Spark is faster than Hadoop because it can perform in-memory processing, reducing the need to write intermediate results to disk.

• Spark uses DAG (Directed Acyclic Graph) for processing tasks, which optimizes the workflow and minimizes data shuffling.

• Spark allows for iterative computations, making it suitable for machine learning algorithms that require multiple passes over the data.

• Spa...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.

C programming language has various operators for performing operations on variables and values.

• Arithmetic Operators: +, -, *, /, % (e.g., a + b)

• Relational Operators: ==, !=, >, <, >=, <= (e.g., a > b)

• Logical Operators: &&, ||, ! (e.g., a && b)

• Bitwise Operators: &, |, ^, ~, <<, >> (e.g., a & b)

• Assignment Operators: =, +=, -=, *=, /= (e.g., a += b)

• Increment/Decrement Operators: ++, -- (e.g., a++)

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.

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

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);

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

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 <path>' 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