10+ MResult Services Interview Questions and Answers

Code to print duplicate numbers in a list.

• Iterate through the list and keep track of the count of each number using a dictionary.

• Print the numbers that have a count greater than 1.

Code to print reverse of a sentence word by word.

• Split the sentence into words using space as delimiter

• Store the words in an array

• Print the words in reverse order

Cache and persist are used to store data in memory. Repartition and coalesce are used to change the number of partitions.

• Cache stores the data in memory for faster access while persist allows the user to choose the storage level.

• Repartition increases the number of partitions while coalesce decreases the number of partitions.

• Cache and persist are transformations while repartition and coalesce are actions.

• Cache and persist are used for iterative algorithms while repartition and...read more

Spark optimization techniques include partitioning, caching, and using appropriate transformations.

• Partitioning data can improve performance by reducing shuffling.

• Caching frequently used data can reduce the need for recomputation.

• Transformations like filter, map, and reduceByKey can be used to optimize data processing.

• Shuffling can be minimized by using operations like reduceByKey instead of groupByKey.

• Broadcasting small data can improve performance by reducing network traffi...read more

Hive has two types of tables - Managed and External. Managed tables are managed by Hive, while External tables are managed outside of Hive.

• Managed tables are created using 'CREATE TABLE' command and data is stored in Hive's warehouse directory

• External tables are created using 'CREATE EXTERNAL TABLE' command and data is stored outside of Hive's warehouse directory

• Managed tables are deleted when the table is dropped, while External tables are not

• Managed tables have full control...read more

RDD, Dataframe, and Dataset are data structures in Apache Spark with different characteristics and functionalities.

• RDD (Resilient Distributed Datasets) is a fundamental data structure in Spark that represents an immutable distributed collection of objects. It provides low-level APIs for distributed data processing and fault tolerance.

• Dataframe is a distributed collection of data organized into named columns. It is similar to a table in a relational database and provides a hig...read more

Spark can connect to Azure SQL Database using JDBC driver.

• Download and install the JDBC driver for Azure SQL Database.

• Set up the connection string with the appropriate credentials.

• Use the JDBC API to connect Spark to Azure SQL Database.

• Example: val df = spark.read.jdbc(jdbcUrl, tableName, connectionProperties)

• Ensure that the firewall rules for the Azure SQL Database allow access from the Spark cluster.

Developed a data pipeline to process and analyze customer behavior data.

• Used Apache Kafka for real-time data streaming

• Implemented data processing using Apache Spark

• Stored data in Hadoop Distributed File System (HDFS)

• Used Tableau for data visualization

Data skewness can be handled in Spark by using techniques like partitioning, bucketing, and broadcasting.

• Partitioning the data based on a key column can distribute the data evenly across the cluster.

• Bucketing can further divide the data into smaller buckets based on a hash function.

• Broadcasting small tables can reduce the amount of data shuffled across the network.

• Using dynamic allocation can also help in handling data skewness by allocating more resources to tasks that are t...read more

I have worked with Azure Data Factory, Azure Databricks, and Azure SQL Database.

• Azure Data Factory for data integration and orchestration

• Azure Databricks for big data processing and analytics

• Azure SQL Database for relational database management

The tech stack used includes Python, SQL, Apache Spark, Hadoop, AWS, and Docker.

• Python for data processing and analysis

• SQL for database querying

• Apache Spark for big data processing

• Hadoop for distributed storage and processing

• AWS for cloud services

• Docker for containerization