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Questions and Answers
Auto Loader requires manual updates to be activated after being configured.
Auto Loader requires manual updates to be activated after being configured.
False (B)
A function is used to trigger the arrival of another file for the active stream.
A function is used to trigger the arrival of another file for the active stream.
True (A)
Metadata indicating the source file and ingestion time enriches the raw data for troubleshooting.
Metadata indicating the source file and ingestion time enriches the raw data for troubleshooting.
True (A)
The silver layer can only be processed through Spark SQL.
The silver layer can only be processed through Spark SQL.
A static lookup table is not required for joining the bronze table.
A static lookup table is not required for joining the bronze table.
The batch jobs are triggered using availableNow syntax.
The batch jobs are triggered using availableNow syntax.
The gold layer can read streams from the gold table once it is written.
The gold layer can read streams from the gold table once it is written.
Structured Streaming assumes that data can be both appended and deleted in upstream tables.
Structured Streaming assumes that data can be both appended and deleted in upstream tables.
What is the primary purpose of the customers' static lookup table in the silver layer?
What is the primary purpose of the customers' static lookup table in the silver layer?
Which action is NOT performed in the silver layer during the data enrichment process?
Which action is NOT performed in the silver layer during the data enrichment process?
What output mode is used for writing the aggregated data to the gold table?
What output mode is used for writing the aggregated data to the gold table?
What happens to the stream when structured streaming detects changes in the upstream tables?
What happens to the stream when structured streaming detects changes in the upstream tables?
What method allows combining streaming and batch workloads in the same pipeline?
What method allows combining streaming and batch workloads in the same pipeline?
What should be done to update the gold table after processing new data files?
What should be done to update the gold table after processing new data files?
What is the purpose of registering a streaming temporary view in this pipeline?
What is the purpose of registering a streaming temporary view in this pipeline?
What is indicated by the metadata added during the enrichment of raw data?
What is indicated by the metadata added during the enrichment of raw data?
What happens to the stream after it has been created but not yet activated?
What happens to the stream after it has been created but not yet activated?
How does the streaming query respond when new data is detected in the active stream?
How does the streaming query respond when new data is detected in the active stream?
What conclusion can be drawn about the write stream for the orders_bronze table?
What conclusion can be drawn about the write stream for the orders_bronze table?
What is the first step to begin processing the dataset in the pipeline?
What is the first step to begin processing the dataset in the pipeline?
What is the role of the Auto Loader in this pipeline?
What is the role of the Auto Loader in this pipeline?
Match the following layers of the data pipeline with their primary functions:
Match the following layers of the data pipeline with their primary functions:
Match the following components with their roles in the Spark SQL environment:
Match the following components with their roles in the Spark SQL environment:
Match each data processing stage with its specific activity:
Match each data processing stage with its specific activity:
Match the data processing modes with their descriptions:
Match the data processing modes with their descriptions:
Match the following Spark configurations with their impacts:
Match the following Spark configurations with their impacts:
Match the following components of the Delta Lake pipeline with their functions:
Match the following components of the Delta Lake pipeline with their functions:
Match the following stages of data processing with their descriptions:
Match the following stages of data processing with their descriptions:
Match the following types of data files with their characteristics:
Match the following types of data files with their characteristics:
Match the following tools or functions with their roles in the pipeline:
Match the following tools or functions with their roles in the pipeline:
Match the following outcomes with their related actions in the process:
Match the following outcomes with their related actions in the process:
Match the following terms with their definitions:
Match the following terms with their definitions:
Match the following features of Delta Lake to their benefits:
Match the following features of Delta Lake to their benefits:
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Study Notes
Delta Lake Multi-hop Pipeline
- Utilizing a bookstore dataset consisting of customers, orders, and books tables to create a multi-hop pipeline.
- The process begins with running a Copy-Datasets script and checking the source directory for Parquet files.
- Three Parquet files identified, each containing 1000 records.
Auto Loader Configuration
- Auto Loader is configured to perform a schema inference on the Parquet source.
- A streaming temporary view named
orders_raw_tmpis established for data transformation with Spark SQL. - The stream is inactive until a display or write operation occurs.
Data Enrichment
- Enrichment of raw data involves adding metadata, including source file info and ingestion time.
- Active stream successfully inserts enriched data, showing metadata alongside the new data.
Writing to Delta Lake
- Data from the enriched stream is processed to write incrementally into a Delta Lake table labeled
orders_bronze. - A total of 3000 records are successfully written into the bronze table from the three initial files.
- Demonstration of triggering new data arrival into the bronze table, resulting in a total of 4000 records.
Transition to Silver Layer
- Establishment of a static lookup table from JSON files for joining data with the
orders_bronzetable. - The
customerstemporary view consists of customer IDs, emails, and profile data in JSON format. - A streaming temporary view is created against the bronze table to process data for the silver layer.
Data Enrichment and Processing in Silver Layer
- Enrichments performed include joining order data with customer info, formatting timestamps, and excluding orders without items.
- Write stream successfully processes enriched data into a silver table, confirming the write of all 4000 records.
- New data arrival triggers processing through the streams, updating the silver table to 5000 records.
Gold Layer Aggregation
- A streaming temporary view from the silver table is established to aggregate data for daily book counts per customer.
- The aggregated data is written into a gold table called
daily_customer_books. - The stream processes available data using a
trigger availableNowoption, stopping automatically after all micro-batch data has been consumed.
Data Handling and Limitations
- Structured Streaming is designed for appending data; changes or overwrites in upstream tables invalidate streaming.
- Options like
ignoreChangesare available, but they come with limitations.
Final Data Processing
- Remaining data files are added to the source directory, prompting propagation from the source through the bronze and silver layers to the gold layer.
- A final query is re-run to update the gold table, confirming new book counts for customers.
Stream Management
- All active streams are halted using a for loop, concluding the notebook operations.
Delta Lake Multi-hop Pipeline
- Utilizing a bookstore dataset consisting of customers, orders, and books tables to create a multi-hop pipeline.
- The process begins with running a Copy-Datasets script and checking the source directory for Parquet files.
- Three Parquet files identified, each containing 1000 records.
Auto Loader Configuration
- Auto Loader is configured to perform a schema inference on the Parquet source.
- A streaming temporary view named
orders_raw_tmpis established for data transformation with Spark SQL. - The stream is inactive until a display or write operation occurs.
Data Enrichment
- Enrichment of raw data involves adding metadata, including source file info and ingestion time.
- Active stream successfully inserts enriched data, showing metadata alongside the new data.
Writing to Delta Lake
- Data from the enriched stream is processed to write incrementally into a Delta Lake table labeled
orders_bronze. - A total of 3000 records are successfully written into the bronze table from the three initial files.
- Demonstration of triggering new data arrival into the bronze table, resulting in a total of 4000 records.
Transition to Silver Layer
- Establishment of a static lookup table from JSON files for joining data with the
orders_bronzetable. - The
customerstemporary view consists of customer IDs, emails, and profile data in JSON format. - A streaming temporary view is created against the bronze table to process data for the silver layer.
Data Enrichment and Processing in Silver Layer
- Enrichments performed include joining order data with customer info, formatting timestamps, and excluding orders without items.
- Write stream successfully processes enriched data into a silver table, confirming the write of all 4000 records.
- New data arrival triggers processing through the streams, updating the silver table to 5000 records.
Gold Layer Aggregation
- A streaming temporary view from the silver table is established to aggregate data for daily book counts per customer.
- The aggregated data is written into a gold table called
daily_customer_books. - The stream processes available data using a
trigger availableNowoption, stopping automatically after all micro-batch data has been consumed.
Data Handling and Limitations
- Structured Streaming is designed for appending data; changes or overwrites in upstream tables invalidate streaming.
- Options like
ignoreChangesare available, but they come with limitations.
Final Data Processing
- Remaining data files are added to the source directory, prompting propagation from the source through the bronze and silver layers to the gold layer.
- A final query is re-run to update the gold table, confirming new book counts for customers.
Stream Management
- All active streams are halted using a for loop, concluding the notebook operations.
Delta Lake Multi-hop Pipeline
- Utilizing a bookstore dataset consisting of customers, orders, and books tables to create a multi-hop pipeline.
- The process begins with running a Copy-Datasets script and checking the source directory for Parquet files.
- Three Parquet files identified, each containing 1000 records.
Auto Loader Configuration
- Auto Loader is configured to perform a schema inference on the Parquet source.
- A streaming temporary view named
orders_raw_tmpis established for data transformation with Spark SQL. - The stream is inactive until a display or write operation occurs.
Data Enrichment
- Enrichment of raw data involves adding metadata, including source file info and ingestion time.
- Active stream successfully inserts enriched data, showing metadata alongside the new data.
Writing to Delta Lake
- Data from the enriched stream is processed to write incrementally into a Delta Lake table labeled
orders_bronze. - A total of 3000 records are successfully written into the bronze table from the three initial files.
- Demonstration of triggering new data arrival into the bronze table, resulting in a total of 4000 records.
Transition to Silver Layer
- Establishment of a static lookup table from JSON files for joining data with the
orders_bronzetable. - The
customerstemporary view consists of customer IDs, emails, and profile data in JSON format. - A streaming temporary view is created against the bronze table to process data for the silver layer.
Data Enrichment and Processing in Silver Layer
- Enrichments performed include joining order data with customer info, formatting timestamps, and excluding orders without items.
- Write stream successfully processes enriched data into a silver table, confirming the write of all 4000 records.
- New data arrival triggers processing through the streams, updating the silver table to 5000 records.
Gold Layer Aggregation
- A streaming temporary view from the silver table is established to aggregate data for daily book counts per customer.
- The aggregated data is written into a gold table called
daily_customer_books. - The stream processes available data using a
trigger availableNowoption, stopping automatically after all micro-batch data has been consumed.
Data Handling and Limitations
- Structured Streaming is designed for appending data; changes or overwrites in upstream tables invalidate streaming.
- Options like
ignoreChangesare available, but they come with limitations.
Final Data Processing
- Remaining data files are added to the source directory, prompting propagation from the source through the bronze and silver layers to the gold layer.
- A final query is re-run to update the gold table, confirming new book counts for customers.
Stream Management
- All active streams are halted using a for loop, concluding the notebook operations.
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