20+ Interview Questions and Answers

Tasks and stages are components of the execution plan in Spark UI.

• Tasks are the smallest unit of work in Spark, representing a single operation on a partition of data.

• Stages are groups of tasks that are executed together as part of a larger computation.

• Tasks within a stage can be executed in parallel, while stages are executed sequentially.

• Tasks are created based on the transformations and actions in the Spark application.

• Stages are created based on the dependencies between R...read more

Optimisation techniques used in the project include indexing, query optimization, caching, and parallel processing.

• Indexing: Creating indexes on frequently queried columns to improve search performance.

• Query optimization: Rewriting queries to make them more efficient and reduce execution time.

• Caching: Storing frequently accessed data in memory to reduce the need for repeated database queries.

• Parallel processing: Distributing tasks across multiple processors to speed up data p...read more

SQL query to group by employee ID and combine first name and last name with a space

• Use the GROUP BY clause to group by employee ID

• Use the CONCAT function to combine first name and last name with a space

• Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name

Different types of joins in SQL include inner join, left join, right join, and full outer join.

• Inner join: Returns rows when there is a match in both tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full outer join: Returns rows when there is a match in either table.

• Example: SELECT * FROM table1 INNER JOIN table2 ON table1.id = table2.id;

To write a file in a delta table, you can use the Delta Lake API or Spark SQL commands.

• Use Delta Lake API to write data to a delta table

• Use Spark SQL commands like INSERT INTO to write data to a delta table

• Ensure that the data being written is in the correct format and schema

Optimizing queries in BigQuery involves using partitioned tables, clustering, and optimizing joins.

• Partition tables by date or another relevant column to reduce the amount of data scanned

• Use clustering to group related rows together, reducing the amount of data scanned for queries

• Avoid unnecessary joins and denormalize data where possible to reduce query complexity

Yes, I have experience in Dataflow, Dataproc, and cloud composer.

• I have worked with Dataflow to process and analyze large datasets in real-time.

• I have used Dataproc to create and manage Apache Spark and Hadoop clusters for big data processing.

• I have experience with cloud composer for orchestrating workflows and managing data pipelines.

Types of SCD include Type 1, Type 2, and Type 3.

• Type 1 SCD: Overwrites old data with new data, no history is maintained.

• Type 2 SCD: Maintains historical data by creating new records for changes.

• Type 3 SCD: Creates separate columns to store historical and current data.

• Examples: Type 1 - Employee address updates overwrite old address. Type 2 - Employee salary changes create new record with effective date. Type 3 - Employee job title history stored in separate columns.

Cloud functions like Cloud Build and Cloud Run in GCP are serverless computing services for building and running applications in the cloud.

• Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. It automatically builds and tests your code in the cloud.

• Cloud Run is a managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It automatically scales up or down based on traffic.

• Cloud Functions i...read more

Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.

• Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.

• Parquet files support efficient compression, reducing storage space and improving read performance in Spark.

• Spark can push down filters to Parquet files, allowing for faster query execution by only reading relevant data.

• Pa...read more

DAG (Directed Acyclic Graph) in Apache Spark is used to represent a series of data processing steps and their dependencies.

• DAG in Spark helps optimize the execution of tasks by determining the order in which they should be executed based on dependencies.

• It breaks down a Spark job into smaller tasks and organizes them in a way that minimizes unnecessary computations.

• DAGs are created automatically by Spark when actions are called on RDDs or DataFrames.

• Example: If a Spark job in...read more

GCP architecture refers to the structure and components of Google Cloud Platform for building and managing applications and services.

• GCP architecture is based on a global network of data centers that provide secure, scalable infrastructure for cloud services.

• Key components include Compute Engine for virtual machines, Cloud Storage for object storage, and BigQuery for data analytics.

• GCP architecture also includes networking services like Virtual Private Cloud (VPC) for secure ...read more

Spark configuration for a job is decided based on factors like data size, cluster resources, and job requirements.

• Consider the size of the data being processed to determine the number of partitions and memory requirements.

• Evaluate the available cluster resources such as CPU cores, memory, and storage to optimize performance.

• Adjust parameters like executor memory, executor cores, and driver memory based on the complexity of the job.

• Use dynamic allocation to efficiently utilize...read more

Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.

• Constructors are defined using the __init__() method in a class.

• They are used to initialize instance variables of a class.

• Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Delta Lake provides ACID transactions, schema enforcement, and data versioning on top of data lakes.

• Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale.

• Delta Lake is optimized for big data workloads and is built on top of Apache Spark.

• Data Lake can store data from various sources like IoT devices, soc...read more

SQL is a programming language used for managing and manipulating relational databases.

• SQL stands for Structured Query Language

• It is used to communicate with databases to perform tasks such as querying data, updating data, and creating tables

• Common SQL commands include SELECT, INSERT, UPDATE, DELETE

• Example: SELECT * FROM employees WHERE department = 'IT'

Python is a high-level programming language known for its simplicity and readability.

• Python is an interpreted language, meaning code is executed line by line.

• It is dynamically typed, allowing for flexibility in variable types.

• Python is popular for web development, data analysis, artificial intelligence, and more.

• Example: print('Hello, World!') is a simple Python program to display text.

Spark applications can be deployed using various methods like standalone mode, YARN, Mesos, or Kubernetes.

• Deploy Spark application in standalone mode by submitting the application using spark-submit command

• Deploy Spark application on YARN by setting the master to yarn and submitting the application to the YARN ResourceManager

• Deploy Spark application on Mesos by setting the master to mesos and submitting the application to the Mesos cluster

• Deploy Spark application on Kubernete...read more

Python program to print 'Hello, World!'

• Use the print() function in Python to display text on the screen

• Enclose the text in single or double quotes to indicate a string

Decorators in Python are functions that modify the behavior of other functions or methods.

• Decorators are defined using the @decorator_name syntax before a function definition.

• They can be used to add functionality to existing functions without modifying their code.

• Decorators can be used for logging, timing, authentication, and more.

• Example: @staticmethod decorator in Python is used to define a static method in a class.

Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.

• Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.

• Indexes can be created using CREATE INDEX statement in SQL.

• Types of indexes include clustered indexes, non-clustered indexes, unique indexes, and composite indexes.

• Example: CREATE INDEX idx_lastname ON ...read more