DataMetica Data Engineer Interview Questions and Answers for Freshers

Q1. Components of Databricks
Ans. 
Databricks is a unified data analytics platform that includes components like Databricks Workspace, Databricks Runtime, and Databricks Delta.
• Databricks Workspace: Collaborative environment for data science and engineering teams.

• Databricks Runtime: Optimized Apache Spark cluster for data processing.

• Databricks Delta: Unified data management system for data lakes.
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Q2. How to read a json file
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To read a JSON file, use a programming language's built-in functions or libraries to parse the file and extract the data.
• Use a programming language like Python, Java, or JavaScript to read the JSON file.

• Import libraries like json in Python or json-simple in Java to parse the JSON data.

• Use functions like json.load() in Python to load the JSON file and convert it into a dictionary or object.

• Access the data in the JSON fi...read more
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Q3. Second highest salary SQL
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To find the second highest salary in SQL, use the MAX function with a subquery or the LIMIT clause.
• Use the MAX function with a subquery to find the highest salary first, then use a WHERE clause to exclude it and find the second highest salary.

• Alternatively, use the LIMIT clause to select the second highest salary directly.

• Make sure to handle cases where there may be ties for the highest salary.
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Q4. How to configure the spark while creating the cluster
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Spark cluster configuration involves setting up memory, cores, and other parameters for optimal performance.
• Specify the number of executors and executor memory

• Set the number of cores per executor

• Adjust the driver memory based on the application requirements

• Configure shuffle partitions for efficient data processing

• Enable dynamic allocation for better resource utilization
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Q1. Spark and airflow related questions
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Q2. AWS services and it's related questions
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Q1. Copy activity in ADF
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Q2. Delta table, unity catalog , delta live table in Azure databricks
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Q1. Copy activity, Lookup, get metadata, if else, for each activity in ADF
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Q2. Conceptual ETL Questions like coalesce and repartition, cache, persist etc.
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Half hour with spark python azure databricks

Q1. Architecture databricks
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Q2. Sql related questions
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Q1. What activities you have used in data factory?
Ans. 
I have used activities such as Copy Data, Execute Pipeline, Lookup, and Data Flow in Data Factory.
• Copy Data activity is used to copy data from a source to a destination.

• Execute Pipeline activity is used to trigger another pipeline within the same or different Data Factory.

• Lookup activity is used to retrieve data from a specified dataset or table.

• Data Flow activity is used for data transformation and processing.
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Q2. How will you execute second notebook from first notebook?
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To execute a second notebook from the first notebook, you can use the %run magic command in Jupyter Notebook.
• Use the %run magic command followed by the path to the second notebook in the first notebook.

• Ensure that the second notebook is in the same directory or provide the full path to the notebook.

• Make sure to save any changes in the second notebook before executing it from the first notebook.
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Q3. Difference between data lake storage and blob storage?
Ans. 
Data lake storage is optimized for big data analytics and can store structured, semi-structured, and unstructured data. Blob storage is for unstructured data only.
• Data lake storage is designed for big data analytics and can handle structured, semi-structured, and unstructured data

• Blob storage is optimized for storing unstructured data like images, videos, documents, etc.

• Data lake storage allows for complex queries and ...read more
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Q1. Python, sql, datawarehousing concepts, GCP
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Q1. How to build data pipeline
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Building a data pipeline involves extracting, transforming, and loading data from various sources to a destination for analysis.
• Identify data sources and determine the data to be collected

• Extract data from sources using tools like Apache NiFi or Apache Kafka

• Transform data using tools like Apache Spark or Python scripts

• Load data into a destination such as a data warehouse or database

• Schedule and automate the pipeline fo...read more
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Q2. IICS
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