30+ Interview Questions and Answers

Use broadcast join or partition join in pyspark to join two large tables efficiently.

• Use broadcast join for smaller table and partition join for larger table.

• Broadcast join - broadcast the smaller table to all worker nodes.

• Partition join - partition both tables on the join key and join them.

• Example: df1.join(broadcast(df2), 'join_key')

• Example: df1.join(df2, 'join_key').repartition('join_key')

SQL query to retrieve student names with marks > 45 in each subject

• Use GROUP BY and HAVING clauses to filter students with marks > 45 in each subject

• Join Student table with Marks table on student_id to get marks for each student

• Select student names from Student table based on the conditions

Use dropDuplicates() function in pyspark to remove duplicates in a data frame.

• Use dropDuplicates() function on the data frame to remove duplicates based on all columns.

• Specify subset of columns to remove duplicates based on specific columns.

• Use the distinct() function to remove duplicates and keep only distinct rows.

Set spark configuration with appropriate memory and cores for efficient processing of 2 GB data

• Increase executor memory and cores to handle larger data size

• Adjust spark memory overhead to prevent out of memory errors

• Optimize shuffle partitions for better performance

Use dbutils.notebook.run() command to run a child notebook in a parent notebook

• Use dbutils.notebook.run() command with the path to the child notebook and any parameters needed

• Ensure that the child notebook is accessible and has necessary permissions

• Handle any return values or errors from the child notebook appropriately

Join DF's, count events, filter users with 0 events

• Use join operation to combine DF1 and DF2 on UserID

• Group by UserID and count the number of events

• Filter out users with 0 events

Re-Partition and Coalesce are methods used to control the number of partitions in a dataset in Apache Spark.

• Re-Partition is used to increase or decrease the number of partitions in a dataset by shuffling the data across the cluster.

• Coalesce is used to decrease the number of partitions in a dataset without shuffling the data, which can improve performance.

• Re-Partition is typically used when there is a need to increase parallelism or balance the data distribution, while Coalesc...read more

SQL is a programming language used to manage and manipulate relational databases. Normalizing is the process of organizing data in a database to minimize redundancy.

• SQL stands for Structured Query Language

• It is used to create, modify, and query relational databases

• Normalization is the process of breaking down a database into smaller, more manageable tables to reduce redundancy and improve data integrity

• There are different levels of normalization, such as first normal form (1N...read more

Extract Pincode from Address Field in Dataframe using Pyspark

• Use pyspark.sql.functions regexp_extract() function to extract pincode from address field

• Create a new column in the dataframe to store the extracted pincode

• Specify the regular expression pattern for pincode extraction

• Example: df.withColumn('pincode', regexp_extract(df['address'], '\b\d{6}\b', 0))

The activity used for creating email notification is sending an email.

• Use SMTP (Simple Mail Transfer Protocol) to send emails

• Set up an email server or use a third-party email service provider

• Include the recipient's email address, subject, and message content

• Can be automated using tools like Python's smtplib library or email marketing platforms like Mailchimp

Broadcast join is a type of join operation in distributed computing where one smaller dataset is broadcasted to all nodes for efficient processing.

• Reduces data shuffling by sending smaller dataset to all nodes

• Useful when one dataset is significantly smaller than the other

• Improves performance by reducing network traffic and processing time

A database is a collection of data that is organized in a way that allows for efficient retrieval and manipulation of data.

• A database is used to store and manage data.

• It allows for easy retrieval and manipulation of data.

• Examples of databases include MySQL, Oracle, and MongoDB.

• Databases are used in various industries such as finance, healthcare, and e-commerce.

Components of Data factory pipeline include datasets, activities, linked services, triggers, and pipelines.

• Datasets: Define the data structure and location for input and output data.

• Activities: Define the actions to be performed on the data such as data movement, data transformation, or data processing.

• Linked Services: Define the connections to external data sources or destinations.

• Triggers: Define the conditions that determine when a pipeline should be executed.

• Pipelines: De...read more

Enable Hive support in Spark for seamless integration of Hive tables and queries.

• Set 'spark.sql.catalogImplementation' to 'hive' in SparkConf

• Include 'spark-hive' dependency in the Spark application

• Ensure Hive configuration files are available in the classpath

• Use HiveContext or enable Hive support in SparkSession

Lineage graph is used to track the flow of data from source to destination, helping in understanding data dependencies and impact analysis.

• Helps in understanding data dependencies and relationships

• Tracks the flow of data from source to destination

• Aids in impact analysis and troubleshooting

• Useful for data governance and compliance

• Can be visualized to easily comprehend complex data pipelines

Code to count frequency of elements in a list of strings.

• Use a dictionary to store the frequency of each element in the list.

• Iterate through the list and update the count in the dictionary.

• Return the dictionary with element frequencies.

df.explain() in pyspark is used to display the physical plan of the DataFrame operations.

• df.explain() is used to show the execution plan of the DataFrame operations in pyspark.

• It helps in understanding how the operations are being executed and optimized by Spark.

• The output of df.explain() includes details like the logical and physical plans, optimizations applied, and stages of execution.

Lazy evaluation is a strategy used by Spark to delay the execution of transformations until an action is called.

• Lazy evaluation improves performance by optimizing the execution plan

• Transformations in Spark are not executed immediately, but rather recorded as a lineage graph

• Actions trigger the execution of the transformations and produce a result

• Lazy evaluation allows Spark to optimize the execution plan by combining and reordering transformations

• Example: val data = spark.read...read more

Combining my electrical engineering background with IT skills allows me to work on cutting-edge technologies and solve complex problems.

• Interest in technology and data analysis sparked during electrical engineering studies

• Realized the potential of combining electrical engineering knowledge with IT for innovative solutions

• Opportunities in data engineering field align with my career goals

Joins in Spark using PySpark are used to combine data from two different DataFrames based on a common key.

• Joins are performed using the join() function in PySpark.

• Common types of joins include inner join, outer join, left join, and right join.

• Example: df1.join(df2, df1.key == df2.key, 'inner')

Databricks and Azure Synapse Notebook are both cloud-based platforms for data engineering and analytics.

• Databricks is primarily focused on big data processing and machine learning, while Azure Synapse Notebook is part of a larger analytics platform.

• Databricks provides a collaborative environment for data scientists and engineers to work together, while Azure Synapse Notebook is integrated with other Azure services for seamless data integration and analysis.

• Databricks offers b...read more

BQ stands for BigQuery, a fully managed, serverless, and highly scalable cloud data warehouse provided by Google Cloud.

• Advantages of BigQuery include fast query performance due to its distributed architecture

• Scalability to handle large datasets without the need for infrastructure management

• Integration with other Google Cloud services like Dataflow, Dataproc, and Data Studio

• Support for standard SQL queries and real-time data streaming

• Cost-effectiveness with a pay-as-you-go pri...read more

Data skewness can be handled by partitioning data, using sampling techniques, optimizing queries, and using parallel processing.

• Partitioning data based on key values to distribute workload evenly

• Using sampling techniques to estimate skewed data distribution

• Optimizing queries by using appropriate indexes and query optimization techniques

• Using parallel processing to distribute workload across multiple nodes

Query to find the 2nd highest salary in a database table.

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

• Use the LIMIT clause to retrieve the second row.

• Consider handling cases where there may be ties for the highest salary.

Left join returns all records from the left table and the matching records from the right table.

• Inner join returns only the matching records from both tables.

• Left join includes all records from the left table, even if there are no matches in the right table.

• Inner join excludes the non-matching records from both tables.

• Left join is used to retrieve all records from one table and the matching records from another table.

• Inner join is used to retrieve only the records that have m...read more

External tables store data outside the database while internal tables store data within the database.

• External tables reference data stored outside the database, such as in HDFS or S3, while internal tables store data within the database itself.

• External tables are typically used for data that is not managed by the database system, while internal tables are used for data that is managed by the database system.

• External tables are often used for data that needs to be shared acros...read more

Joins in C are used to combine records from two or more tables based on a related column between them.

• Joins in C are typically implemented using nested loops or hash tables.

• Common types of joins include inner join, outer join, left join, and right join.

• Example: Performing an inner join between two tables to retrieve only the matching records.

Spark architecture is a distributed computing framework that consists of a driver program, cluster manager, and worker nodes.

• Spark driver program coordinates the execution of tasks and maintains the overall state of the application.

• Cluster manager allocates resources for the application and monitors its execution.

• Worker nodes execute the tasks assigned by the driver program and store data in memory or disk.

• Spark architecture supports various data processing workloads like bat...read more

Triggers in Azure are used to initiate an action based on a specific event or condition.

• Triggers can be used in Azure Functions to execute code in response to events such as changes in data or messages in a queue.

• Azure Logic Apps also use triggers to start workflows based on events such as receiving an email or a new file being added to a storage account.

• Triggers can be configured to run on a schedule or based on a specific condition being met.

• Examples of triggers include HTT...read more

One optimization technique in PySpark is using partitioning to distribute data evenly across nodes.

• Use partitioning to distribute data evenly across nodes

• Avoid shuffling data unnecessarily

• Cache intermediate results to avoid recomputation

Python code to calculate factorial of a number

• Use a recursive function to calculate the factorial

• Base case: if n is 0 or 1, return 1

• Recursive case: return n * factorial(n-1)

• Example: def factorial(n): return 1 if n == 0 or n == 1 else n * factorial(n-1)

Azure is a cloud computing platform and service provided by Microsoft.

• Azure offers a wide range of services including virtual machines, storage, databases, and analytics.

• It provides scalability, reliability, and security for businesses to build, deploy, and manage applications.

• Azure supports various programming languages and frameworks, allowing developers to use their preferred tools.

• It offers AI and machine learning capabilities through services like Azure Machine Learning ...read more

To find the second largest element in an array

• Sort the array in descending order

• Return the element at index 1

Snowflake architecture is a cloud-based data warehousing solution that separates storage and compute resources for scalability and performance.

• Snowflake uses a unique architecture with three layers: storage, compute, and services.

• Storage layer stores data in a columnar format for efficient querying.

• Compute layer processes queries independently, allowing for elastic scalability.

• Services layer manages metadata, security, and query optimization.

• Snowflake's architecture enables a...read more

Spark optimization techniques improve performance and efficiency of Spark jobs.

• Use partitioning to distribute data evenly across nodes

• Cache intermediate results to avoid recomputation

• Use broadcast variables for small lookup tables

• Optimize shuffle operations by reducing data shuffling

• Tune memory settings for better performance

Small file problem in Spark refers to inefficiency when processing multiple small files in Spark jobs.

• Small files can lead to inefficient resource utilization and slow job execution in Spark.

• One solution is to combine small files into larger files before processing.

• Another solution is to use coalesce or repartition to reduce the number of partitions in RDDs.

• Avoid using HDFS block size smaller than the default block size (128 MB) to prevent small file problem.

Spark is a distributed computing system that provides an interface for programming clusters with implicit data parallelism.

• Spark is built on the concept of Resilient Distributed Datasets (RDDs), which are fault-tolerant collections of objects.

• It supports various programming languages such as Scala, Java, Python, and R.

• Spark provides high-level APIs for distributed data processing, including transformations and actions.

• It offers in-memory computing capabilities, enabling faste...read more

Project structure refers to the organization of files, folders, and resources within a project.

• Project structure should be logical and easy to navigate

• Common structures include separating code into modules, organizing files by type (e.g. scripts, data, documentation), and using version control

• Example: A data engineering project may have folders for data extraction, transformation, loading, and documentation