Infinite Computer Solutions Data Engineer Interview Questions and Answers

Q1. What is Databricks, Internals of it, Optimization technique
Ans. 
Databricks is a unified analytics platform that provides a collaborative environment for data scientists, engineers, and analysts.
• Databricks is built on top of Apache Spark, providing a unified platform for data engineering, data science, and business analytics.

• Internals of Databricks include a cluster manager, job scheduler, and workspace for collaboration.

• Optimization techniques in Databricks include query optimizati...read more
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Q2. SQL questions of joins,group by
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Q1. Scenario based azure data factory questions
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Q2. Project structres, pyspark dataframes related
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Q1. What Azure solutions have you worked with?
Ans. 
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
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Its mandatory test even for experience people

Q1. Related to technology
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Q1. Very good discussion towards work culture, salary and all
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Q1. Windows functions in sql
Ans. 
Window functions in SQL are used to perform calculations across a set of table rows related to the current row.
• Window functions are used to calculate values based on a specific subset of rows within a table.

• They allow for ranking, aggregation, and other calculations without grouping the rows.

• Examples of window functions include ROW_NUMBER(), RANK(), and SUM() OVER().
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Q2. Delta lake from Databricks
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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 is built on top of Apache Spark and provides ACID transactions for big data processing.

• It allows for schema enforcement and evolution, data versioning, and time travel queries.

• Delta Lake is compatible with popular data science and machine learning libraries like TensorFlow and PyTorch.
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Q1. Spark Optimizations
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Q1. What will happen if job has failed in pipeline and data processing cycle is over?
Ans. 
If a job fails in the pipeline and data processing cycle is over, it can lead to incomplete or inaccurate data.
• Incomplete data may affect downstream processes and analysis

• Data quality may be compromised if errors are not addressed

• Monitoring and alerting systems should be in place to detect and handle failures

• Re-running the failed job or implementing error handling mechanisms can help prevent issues in the future
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Q2. What is difference repartition and coalesce
Ans. 
Repartition increases the number of partitions in a DataFrame, while coalesce reduces the number of partitions without shuffling data.
• Repartition involves a full shuffle of the data across the cluster, which can be expensive.

• Coalesce minimizes data movement by only creating new partitions if necessary.

• Repartition is typically used when increasing parallelism or evenly distributing data, while coalesce is used for reduc...read more
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Q1. Write sql code to get the city1 city2 distance of table if city1 and city2 tables can repeat
Ans. 
SQL code to get the city1 city2 distance of table with repeating city1 and city2 values
• Use a self join on the table to match city1 and city2

• Calculate the distance between the cities using appropriate formula

• Consider using a subquery if needed
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Q2. How is data partitioned in pipeline
Ans. 
Data partitioning in a pipeline involves dividing data into smaller chunks for processing and analysis.
• Data can be partitioned based on a specific key or attribute, such as date, location, or customer ID.

• Partitioning helps distribute data processing tasks across multiple nodes or servers for parallel processing.

• Common partitioning techniques include range partitioning, hash partitioning, and list partitioning.

• Example: ...read more
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Q1. Tell me about your current project? Have you used any AWS services?
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Q2. Can you explain about EMR andEC2 instance?
Ans. 
EMR is a managed Hadoop framework for processing large amounts of data, while EC2 is a scalable virtual server in AWS.
• EMR stands for Elastic MapReduce and is a managed Hadoop framework for processing large amounts of data.

• EC2 stands for Elastic Compute Cloud and is a scalable virtual server in Amazon Web Services (AWS).

• EMR allows for easy provisioning and scaling of Hadoop clusters, while EC2 provides resizable compute...read more
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Q3. What type of schemas did you use for your project. (Star schema, Snowflake Schema)
Ans. 
I have experience working with both Star and Snowflake schemas in my projects.
• Star schema is a denormalized schema where one central fact table is connected to multiple dimension tables.

• Snowflake schema is a normalized schema where dimension tables are further normalized into sub-dimension tables.

• Used Star schema for simpler, smaller datasets where performance is a priority.

• Used Snowflake schema for complex, larger dat...read more
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Q4. Have you used python, pyspark in your projects?
Ans. 
Yes, I have used Python and PySpark in my projects for data engineering tasks.
• I have used Python for data manipulation, analysis, and visualization.

• I have used PySpark for big data processing and distributed computing.

• I have experience in writing PySpark jobs to process large datasets efficiently.
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Q5. Do you have any experience with serverless schema?
Ans. 
Yes, I have experience with serverless schema.
• I have worked with AWS Lambda to build serverless applications.

• I have experience using serverless frameworks like Serverless Framework or AWS SAM.

• I have designed and implemented serverless architectures using services like AWS API Gateway and AWS DynamoDB.
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