Koch Business Solutions Senior Data Engineer Interview Questions and Answers

Q1. Coding in pyspark
Ans. 
Pyspark is a Python library for big data processing using Spark framework.
• Pyspark is used for processing large datasets in parallel.

• It provides APIs for data manipulation, querying, and analysis.

• Example: Using pyspark to read a CSV file and perform data transformations.
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Q2. Databricks optimisation technique
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Databricks optimisation techniques improve performance and efficiency of data processing on the Databricks platform.
• Use cluster sizing and autoscaling to optimize resource allocation based on workload

• Leverage Databricks Delta for optimized data storage and processing

• Utilize caching and persisting data to reduce computation time

• Optimize queries by using appropriate indexing and partitioning strategies
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Q3. Aqe details in databricks
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Databricks is a unified data analytics platform that provides a collaborative environment for data engineers.
• Databricks is built on top of Apache Spark and provides a workspace for data engineering tasks.

• It allows for easy integration with various data sources and tools for data processing.

• Databricks provides features like notebooks, clusters, and libraries for efficient data engineering workflows.
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Q1. Project worked on
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Developed a real-time data processing system for analyzing customer behavior
• Designed and implemented data pipelines using Apache Kafka and Spark

• Optimized data processing algorithms to handle large volumes of streaming data

• Collaborated with data scientists to integrate machine learning models into the system
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Q2. Snowflake questions on performance inhancement
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Q1. Tell me about your project.
Ans. 
Developed a real-time data processing system for analyzing customer behavior on an e-commerce platform.
• Used Apache Kafka for real-time data streaming

• Implemented data pipelines using Apache Spark for processing large volumes of data

• Designed and optimized data models in PostgreSQL for storing and querying customer data
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Q2. What are the Types of SCD?
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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 - Employ
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Q3. Delta Lake vs Data Lake?
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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...read more
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Q4. How to write a file in a delta table?
Ans. 
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
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Q5. What are optimisation techniques used in the project?
Ans. 
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: Distri...read more
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Q6. What is the difference between Tasks, and stages? About Spark UI?
Ans. 
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 applicat...read more
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Q7. Role of DAG ins aprk ?
Ans. 
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 a...read more
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Q8. What transformation techniques you have used in your project?
Ans. 
I have used various transformation techniques such as data cleaning, normalization, aggregation, and feature engineering in my projects.
• Data cleaning to remove missing values and outliers

• Normalization to scale numerical features

• Aggregation to summarize data at different levels

• Feature engineering to create new relevant features
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Q1. About my project?
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Q2. Some scenario-based questions related to project and work?
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Q3. How do I keep myself updated with the latest technologies?
Ans. 
I keep myself updated by regularly attending conferences, workshops, online courses, and reading industry blogs.
• Attend conferences and workshops related to data engineering

• Take online courses on platforms like Coursera, Udemy, or DataCamp

• Read industry blogs and follow thought leaders on social media

• Join online communities and forums to discuss latest technologies and trends
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Q1. Questions related to spark, Scala
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Q2. Questions related to Higher order questions, kafka
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Q1. What are your accomplishments
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I have successfully led the development of a real-time data processing system, resulting in a 30% increase in efficiency.
• Led the development of a real-time data processing system

• Achieved a 30% increase in efficiency

• Implemented data quality checks to ensure accuracy
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Q2. What are your challenges you have faced
Ans. 
I have faced challenges in optimizing data pipelines, handling large volumes of data, and ensuring data quality.
• Optimizing data pipelines to improve efficiency and performance

• Handling large volumes of data to prevent bottlenecks and ensure scalability

• Ensuring data quality by implementing data validation processes and error handling mechanisms
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Q1. Questions on Snowflake Stages, copy command, Warehouses, roles Questions on sql
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Q1. Managerial round
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Q2. Questions based on past project work
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Q1. Questions based on architectural discussion How tables will be designed based on the given scenario
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Q1. What is vertices and edges on a dag?
Ans. 
Vertices are nodes and edges are connections between nodes in a directed acyclic graph (DAG).
• Vertices represent the tasks or operations in a DAG.

• Edges represent the dependencies between tasks or operations.

• Vertices can have multiple incoming edges and outgoing edges.

• Edges can be weighted to represent the cost or time required to complete a task.

• Examples of DAGs include data processing pipelines and task scheduling syst
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Q2. How do u calculate the resources based on the number of cores and memory given..ex - 16 cores with 64 gb... What is overhead, driver memory etc
Ans. 
Calculating resources based on cores and memory given with overhead and driver memory
• Calculate the total memory available by multiplying the number of cores with memory per core

• Deduct the overhead memory required for the operating system and other processes

• Allocate driver memory for each executor based on the workload

• Consider the memory requirements for other services like Hadoop, Spark, etc.

• Example: For 16 cores with ...read more
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Questions related to GCP and cloud functions

Q1. Introduce yourself, what role are you working as and what are your responsibilities.
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Q2. Could you please explain GCP architecture?
Ans. 
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 networ...read more
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Q3. How can you optimize your queries for efficiency in BQ?
Ans. 
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
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Q4. Do you have experience in Dataflow, Dataproc, cloud composer?
Ans. 
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.
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Q5. What are the different joins in SQL? Please give an example to elaborate.
Ans. 
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 ...read more
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Q6. Explain cloud functions like cloud build, cloud run in GCP.
Ans. 
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 automati...read more
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