Databricks Certified Data Engineer - Associate
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A data engineering team is tasked with optimizing both the cost and performance of their data pipeline in a cloud environment. They are considering implementing incremental processing to achieve these goals. The team is particularly concerned with minimizing costs associated with data processing while ensuring that the pipeline can scale to handle increasing volumes of data without significant latency. Given this scenario, which of the following best describes incremental processing and its implementation to meet the team's objectives? (Choose two options that best apply.)
A data engineering team is tasked with optimizing both the cost and performance of their data pipeline in a cloud environment. They are considering implementing incremental processing to achieve these goals. The team is particularly concerned with minimizing costs associated with data processing while ensuring that the pipeline can scale to handle increasing volumes of data without significant latency. Given this scenario, which of the following best describes incremental processing and its implementation to meet the team's objectives? (Choose two options that best apply.)
Explanation:
Incremental processing optimizes data pipeline performance and cost by focusing on new or changed data only, avoiding the need to reprocess the entire dataset. This approach is particularly effective in scenarios where data changes are incremental. Implementing it with mechanisms like timestamps or CDC ensures that the pipeline is both cost-efficient and scalable, as it minimizes the volume of data processed in each run and reduces latency by focusing on relevant data changes.