Tiger Analytics Data Engineer Interview Questions and Answers

Q1. SQL problem window function
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Q2. SQL code like join and scenerio
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SQL joins combine rows from two or more tables based on related columns, essential for data retrieval.
• INNER JOIN: Returns records with matching values in both tables. Example: SELECT * FROM A INNER JOIN B ON A.id = B.id;

• LEFT JOIN: Returns all records from the left table and matched records from the right. Example: SELECT * FROM A LEFT JOIN B ON A.id = B.id;

• RIGHT JOIN: Returns all records from the right table and matche...read more
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Q1. Design round for adf pipeline
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Designing an ADF pipeline for data processing
• Identify data sources and destinations

• Define data transformations and processing steps

• Consider scheduling and monitoring requirements

• Utilize ADF activities like Copy Data, Data Flow, and Databricks

• Implement error handling and logging mechanisms
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Q2. Azure synapses and adf adb
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Q1. Expected ctc and current ctc negotiations
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Discussing expected and current salary for negotiation purposes.
• Be honest about your current salary and provide a realistic expectation for your desired salary.

• Highlight your skills and experience that justify your desired salary.

• Be open to negotiation and willing to discuss other benefits besides salary.

• Research industry standards and salary ranges for similar positions to support your negotiation.

• Focus on the value y...read more
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Q2. Relocation and remote work until ofc open for Pune location
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I am open to relocation and remote work until the office in Pune reopens, ensuring flexibility and productivity.
• Relocation shows commitment to the role and company.

• Remote work allows for immediate contribution without geographical constraints.

• I have experience working remotely, maintaining productivity through tools like Slack and Zoom.

• Relocating to Pune would enable collaboration with the team once the office opens.
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It was good and comparatively moderate.

Q1. What is indexing in SQl
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I was asked 2 coding problems in pyspark, after that a discussion round followed.

Q1. Explain databricks dlt, and when will you use batch vs streaming?
Ans. 
Databricks DLT is a unified data management platform for batch and streaming processing.
• Databricks DLT (Delta Lake Table) is a storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Batch processing is used when data is collected over a period of time and processed in large chunks, while streaming processing is used for real-time data processing.

• Use batch processing for historical data analy...read more
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Q2. What is the difference between deltalake and delta warehouse
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Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, while Delta Warehouse is a cloud-based data warehouse service.
• Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Delta Warehouse is a cloud-based data warehouse service that provides scalable storage and analytics capabilities.

• Delta Lake is more fo...read more
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Q1. PYSPARK QUESTIONS , CLUSTER CONFIG AND SOME SCENARIOS , ADF QUESTIONS
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Q2. PYTHON AND SQL CODING QUESTIONS
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Q1. SALARY DISCUSSION
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