Insight Global Technologies Interview Questions, Process, and Tips

Set up ETL flow for data in Lake House using Databricks

• Connect Databricks to Lake House storage (e.g. Azure Data Lake Storage)

• Define ETL process using Databricks notebooks or jobs

• Extract data from Lake House, transform as needed, and load into target destination

• Monitor and schedule ETL jobs for automated data processing

I handle failures in ADF Pipelines by setting up monitoring, alerts, retries, and error handling mechanisms.

• Implement monitoring to track pipeline runs and identify failures

• Set up alerts to notify when a pipeline fails

• Configure retries for transient failures

• Use error handling activities like Try/Catch to manage exceptions

• Utilize Azure Monitor to analyze pipeline performance and troubleshoot issues

Yes, I have worked on developing a Data Validation Framework to ensure data accuracy and consistency.

• Developed automated data validation scripts to check for data accuracy and consistency

• Implemented data quality checks to identify and resolve data issues

• Utilized tools like SQL queries, Python scripts, and Azure Data Factory for data validation

• Worked closely with data stakeholders to define validation rules and requirem

SQL query to fetch Top 3 revenue generating Products from Sales table

• Use the SELECT statement to retrieve data from the Sales table

• Use the GROUP BY clause to group the data by Product

• Use the ORDER BY clause to sort the revenue in descending order

• Use the LIMIT clause to fetch only the top 3 revenue generating Products

SQL query to fetch customers who have not transacted in last 30 days but did before

• Use a subquery to filter customers who transacted before 30 days

• Use NOT IN or NOT EXISTS to exclude customers who transacted in last 30 days

Dynamic Content in ADF allows for dynamic values to be passed between activities in Azure Data Factory.

• Dynamic Content can be used to pass values between activities, such as passing output from one activity as input to another.

• Expressions can be used within Dynamic Content to manipulate data or create dynamic values.

• Dynamic Content can be used in various ADF components like datasets, linked services, and activities.

• For...read more

I have applied optimization techniques like partitioning, caching, and cluster sizing in Databricks projects.

• Utilized partitioning to improve query performance by limiting the amount of data scanned

• Implemented caching to store frequently accessed data in memory for faster retrieval

• Adjusted cluster sizing based on workload requirements to optimize cost and performance

Distributed table in Synapse is a table that is distributed across multiple nodes for parallel processing.

• Distributed tables in Synapse are divided into distributions to optimize query performance.

• There are three distribution types: Hash distribution, Round-robin distribution, and Replicate distribution.

• Hash distribution is ideal for joining large tables on a common key, Round-robin distribution evenly distributes data...read more

You can load data from Databricks to Synapse using PolyBase or Azure Data Factory.

• Use PolyBase to load data from Databricks to Synapse by creating an external table in Synapse pointing to the Databricks data location.

• Alternatively, use Azure Data Factory to copy data from Databricks to Synapse by creating a pipeline with Databricks as source and Synapse as destination.

• Ensure proper permissions and connectivity between

Yes, I have worked on real-time data processing projects using technologies like Apache Kafka and Spark Streaming.

• Implemented real-time data pipelines using Apache Kafka for streaming data ingestion

• Utilized Spark Streaming for processing and analyzing real-time data

• Worked on monitoring and optimizing the performance of real-time data processing systems