Exam Certified Data Engineer Associate topic 1 question 30 discussion

B is the correct answer. Checkpointing is a method that is part of structured streaming.

Spark Structured Streaming is for incremental loading. Checkpointing is for failover.

Databricks uses Apache Spark Structured Streaming to back numerous product associated with ingestion workloads, including: - Auto Loader - COPY INTO - Delta Live Tables pipelines - Materialized views and streaming tables in Databricks SQL Source: https://docs.databricks.com/en/ingestion/streaming.html

B. Spark Structured Streaming: Spark Structured Streaming is a key underlying technology for Auto Loader to process streaming data. However, checkpointing is the specific mechanism that allows Auto Loader to track incremental progress. While Structured Streaming is essential for real-time data processing, checkpointing is the mechanism used to track what data has been processed.

B. Spark Structured Streaming Auto Loader uses Spark Structured Streaming to incrementally and efficiently process new data as it arrives, enabling scalable and reliable data ingestion in Databricks.

The answer should be A. Auto Loader is used by Structured Streaming to process data incrementaly, not the other way around.

Correct is B

B is correct

B is orrect

B. Spark Structured Streaming The Auto Loader process in Databricks is typically used in conjunction with Spark Structured Streaming to process data incrementally. Spark Structured Streaming is a real-time data processing framework that allows you to process data streams incrementally as new data arrives. The Auto Loader is a feature in Databricks that works with Structured Streaming to automatically detect and process new data files as they are added to a specified data source location. It allows for incremental data processing without the need for manual intervention.

ans:A How does Auto Loader track ingestion progress? As files are discovered, their metadata is persisted in a scalable key-value store (RocksDB) in the checkpoint location of your Auto Loader pipeline. This key-value store ensures that data is processed exactly once. In case of failures, Auto Loader can resume from where it left off by information stored in the checkpoint location and continue to provide exactly-once guarantees when writing data into Delta Lake. You don’t need to maintain or manage any state yourself to achieve fault tolerance or exactly-once semantics. https://docs.databricks.com/ingestion/auto-loader/index.html

ans:B How does Auto Loader track ingestion progress? As files are discovered, their metadata is persisted in a scalable key-value store (RocksDB) in the checkpoint location of your Auto Loader pipeline. This key-value store ensures that data is processed exactly once. In case of failures, Auto Loader can resume from where it left off by information stored in the checkpoint location and continue to provide exactly-once guarantees when writing data into Delta Lake. You don’t need to maintain or manage any state yourself to achieve fault tolerance or exactly-once semantics. https://docs.databricks.com/ingestion/auto-loader/index.html

B Auto Loader uses Spark Structured Streaming to process data incrementally. Spark Structured Streaming is a streaming engine that can be used to process data as it arrives. This makes it ideal for processing data that is being generated in real time. Option A: Checkpointing is a technique used to ensure that data is not lost in case of a failure. It is not used to process data incrementally. Option C: Data Explorer is a data exploration tool that can be used to explore data. It is not used to process data incrementally. Option D: Unity Catalog is a metadata management tool that can be used to store and manage metadata about data assets. It is not used to process data incrementally. Option E: Databricks SQL is a SQL engine that can be used to query data. It is not used to process data incrementally.

Option B