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I applied via Referral and was interviewed in Apr 2024. There were 2 interview rounds.
Spark Memory management involves configuring memory allocation, monitoring memory usage, and optimizing performance.
Set memory allocation parameters in Spark configuration (e.g. spark.executor.memory, spark.driver.memory)
Monitor memory usage using Spark UI or monitoring tools like Ganglia
Optimize performance by tuning memory allocation based on workload and cluster resources
Use techniques like caching and persistence t
Spark optimization involves tuning configurations, partitioning data, using appropriate transformations, and caching intermediate results.
Tune Spark configurations based on cluster resources and workload requirements
Partition data to distribute workload evenly across nodes
Use appropriate transformations like map, filter, and reduce to minimize data shuffling
Cache intermediate results to avoid recomputation
I applied via Referral and was interviewed in Oct 2023. There was 1 interview round.
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I was interviewed in Oct 2024.
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
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...
I applied via Naukri.com and was interviewed in Oct 2024. There were 2 interview rounds.
Spark performance problems can arise due to inefficient code, data skew, resource constraints, and improper configuration.
Inefficient code can lead to slow performance, such as using collect() on large datasets.
Data skew can cause uneven distribution of data across partitions, impacting processing time.
Resource constraints like insufficient memory or CPU can result in slow Spark jobs.
Improper configuration settings, su...
posted on 16 Dec 2024
I applied via Recruitment Consulltant and was interviewed in Nov 2024. There were 2 interview rounds.
1- sql adn 10 spark questions.
posted on 8 Nov 2024
I applied via Campus Placement
Aptitude test had few quant and verbal questions then SQL MCQs and 3 Coding question
There is no one 'better' coding language, as it depends on the specific use case and requirements.
The best coding language depends on the project requirements, team expertise, and ecosystem support.
For data engineering, languages like Python, Scala, and SQL are commonly used for their data processing capabilities.
Python is popular for its simplicity and extensive libraries like Pandas and NumPy, while Scala is known fo...
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