Data Pipeline Design Interview Questions

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Data Engineer

Asked at Databricks • 7 months ago
What's the difference between a data lakehouse and a data warehouse?
Data Engineer
Data Pipeline Design
3 answers I was asked this
"Data lake and warehouse are both places that allow an organization to store large amounts of data. When swimming in a lake, one would imagine that they come across all sorts of stuff - floating twigs, fish in the water, stones, chemicals and sometimes may be even a snake. Similarly, a data lake stores all forms of data that the company has without any indexing. The data is available at any time but needs to be first cleaned up and reorganized before it can be used for any type of analysis. A"
Kshitij I. - "Data lake and warehouse are both places that allow an organization to store large amounts of data. When swimming in a lake, one would imagine that they come across all sorts of stuff - floating twigs, fish in the water, stones, chemicals and sometimes may be even a snake. Similarly, a data lake stores all forms of data that the company has without any indexing. The data is available at any time but needs to be first cleaned up and reorganized before it can be used for any type of analysis. A"See full answer
Data Engineer
Data Pipeline Design
Design an ETL Pipeline for a ML Platform for AWS
Data Pipeline Design
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Data Pipeline Design
Design Netflix's Clickstream Data Pipeline
Data Pipeline Design
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Data Pipeline Design
Asked at Databricks • 7 months ago
How would you handle slow query performance for a single-user SQL endpoint in Databricks, where all sequentially run queries are affected?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Design an ETL Pipeline for Slack for School
Data Pipeline Design
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Data Pipeline Design

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Asked at Google • 8 months ago
When is Hadoop better than PySpark?
Data Engineer
Data Pipeline Design
1 answer I was asked this
"Hadoop is better than PySpark when you are dealing with extremely large scale, batch oriented, non-iterative workloads where in-memory computing isn't feasible/ necessary, like log storage or ETL workflows that don't require high response times. It's also better in situations where the Hadoop ecosystem is already deeply embedded and where there is a need for resource conscious, fault tolerant computation without the overhead of Spark's memory constraints. In these such scenarios, Hadoop's disk-b"
Joshua R. - "Hadoop is better than PySpark when you are dealing with extremely large scale, batch oriented, non-iterative workloads where in-memory computing isn't feasible/ necessary, like log storage or ETL workflows that don't require high response times. It's also better in situations where the Hadoop ecosystem is already deeply embedded and where there is a need for resource conscious, fault tolerant computation without the overhead of Spark's memory constraints. In these such scenarios, Hadoop's disk-b"See full answer
Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
How would you handle scheduling dependencies between two nightly Jobs to ensure the second Job does not fail if the first Job runs longer than expected?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
How would you handle a task in a nightly job that fails unexpectedly during 10 percent of the runs?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
What is a Medallion Architecture?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
When should you use Delta Live Tables over standard data pipelines built on Spark and Delta Lake?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
What is delta lake?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design
Asked at Databricks • 7 months ago
When should you use a job cluster instead of an all-purpose cluster?
Data Engineer
Data Pipeline Design
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Data Engineer
Data Pipeline Design