Innominds Software Big Data Engineer Interview Questions and Answers

Q1. Data warehousing related questions
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Q2. SQL scenario based questions
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Q3. Project experience
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I have experience working on projects involving data pipeline development, ETL processes, and data warehousing.
• Developed ETL processes to extract, transform, and load data from various sources into a data warehouse

• Built data pipelines to automate the flow of data between systems and ensure data quality and consistency

• Optimized database performance and implemented data modeling best practices

• Worked on real-time data pro...read more
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Q4. Python Based questions
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Q5. AWS features and questions
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Q1. Similar to first round but in depth questions relatively
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Q2. Asked about career goals and stuff
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Q1. General work related conversation
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Q2. Salary discussion
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Q1. Tell about your current project
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Q2. What is distribution in spark
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Distribution in Spark refers to how data is divided across different nodes in a cluster for parallel processing.
• Data is partitioned across multiple nodes in a cluster to enable parallel processing

• Distribution can be controlled using partitioning techniques like hash partitioning or range partitioning

• Ensures efficient utilization of resources and faster processing times
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Q3. How much data can be processed in AWS Glue
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AWS Glue can process petabytes of data per hour
• AWS Glue can process petabytes of data per hour, depending on the configuration and resources allocated

• It is designed to scale horizontally to handle large volumes of data efficiently

• AWS Glue can be used for ETL (Extract, Transform, Load) processes on massive datasets
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Q1. What is distribution in spark ?
Ans. 
Distribution in Spark refers to how data is divided across different nodes in a cluster for parallel processing.
• Distribution in Spark determines how data is partitioned across different nodes in a cluster

• It helps in achieving parallel processing by distributing the workload

• Examples of distribution methods in Spark include hash partitioning and range partitioning
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Q2. How much data can be processed in AWS glue
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AWS Glue can process petabytes of data per hour.
• AWS Glue can process petabytes of data per hour, making it suitable for large-scale data processing tasks.

• It can handle various types of data sources, including structured and semi-structured data.

• AWS Glue offers serverless ETL (Extract, Transform, Load) capabilities, allowing for scalable and cost-effective data processing.

• It integrates seamlessly with other AWS services...read more
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Q3. What is spark and pyspark
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Spark is a fast and general-purpose cluster computing system, while PySpark is the Python API for Spark.
• Spark is a distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

• PySpark is the Python API for Spark that allows developers to write Spark applications using Python.

• Spark and PySpark are commonly used for big data processing, machine...read more
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Q1. Python Lambda Function
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Q2. What are pods in Kubernetes
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Pods are the smallest deployable units in Kubernetes, consisting of one or more containers.
• Pods are used to run and manage containers in Kubernetes

• Each pod has its own unique IP address within the Kubernetes cluster

• Pods can contain multiple containers that share resources and are scheduled together

• Pods are ephemeral and can be easily created, destroyed, or replicated

• Pods can be managed and scaled using Kubernetes contr
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Q1. BQ store procedure
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A stored procedure is a set of SQL statements that can be saved and reused in a database.
• Stored procedures can improve performance by reducing network traffic and improving security.

• They can be used to encapsulate business logic and complex queries.

• Stored procedures can accept input parameters and return output parameters or result sets.
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Q2. GCP archiotecture
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Q1. How to create DBT project
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To create a DBT project, you need to set up a project directory, create models, define sources, and run tests.
• Set up a project directory with a dbt_project.yml file

• Create models in the models directory using SQL files

• Define sources in the sources.yml file

• Run tests using dbt test command
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Q2. Explain materializations in dbt
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Materializations in dbt are pre-computed tables that store the results of dbt models for faster query performance.
• Materializations are created using the 'materialized' parameter in dbt models.

• Common types of materializations include 'view', 'table', and 'incremental'.

• Materializations help improve query performance by reducing the need to recompute data on every query.

• Materializations can be refreshed manually or automa
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Q3. What are dbt snapshots?
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dbt snapshots are a way to capture the state of your data model at a specific point in time.
• dbt snapshots are used to create point-in-time snapshots of your data model

• They allow you to track changes in your data over time

• Snapshots can be used for auditing, debugging, or creating historical reports
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Q1. Explain dashboards created by u
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I have created interactive dashboards using Tableau to visualize and analyze data for various projects.
• Utilized Tableau to connect to data sources and create interactive visualizations

• Designed dashboards with filters, drill-down capabilities, and dynamic elements

• Included key performance indicators (KPIs) and trend analysis in the dashboards

• Used color coding and data labels to enhance data interpretation

• Shared dashboard
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Q2. Role in ur current organization
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Q1. Basic questions on azure databricks
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Q2. Question on spark
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Coding test for all the student attending the first round, give your best practice every day all the best in your interview

Q1. What are your interest
Ans. 
My interests include data analysis, problem-solving, and continuous learning.
• Data analysis

• Problem-solving

• Continuous learning
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Q2. Java related question
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