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Sectional 4 (Incremental Data Processing), 29. Delta Lake Multi-Hop Pipeline
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Sectional 4 (Incremental Data Processing), 29. Delta Lake Multi-Hop Pipeline

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@EnrapturedElf

Questions and Answers

Auto Loader requires manual updates to be activated after being configured.

False

A function is used to trigger the arrival of another file for the active stream.

True

Metadata indicating the source file and ingestion time enriches the raw data for troubleshooting.

True

The silver layer can only be processed through Spark SQL.

<p>False</p> Signup and view all the answers

A static lookup table is not required for joining the bronze table.

<p>False</p> Signup and view all the answers

The batch jobs are triggered using availableNow syntax.

<p>True</p> Signup and view all the answers

The gold layer can read streams from the gold table once it is written.

<p>False</p> Signup and view all the answers

Structured Streaming assumes that data can be both appended and deleted in upstream tables.

<p>False</p> Signup and view all the answers

What is the primary purpose of the customers' static lookup table in the silver layer?

<p>To enable joining with order data</p> Signup and view all the answers

Which action is NOT performed in the silver layer during the data enrichment process?

<p>Transforming data into gold layer format</p> Signup and view all the answers

What output mode is used for writing the aggregated data to the gold table?

<p>Complete</p> Signup and view all the answers

What happens to the stream when structured streaming detects changes in the upstream tables?

<p>The stream stops if the table is updated</p> Signup and view all the answers

What method allows combining streaming and batch workloads in the same pipeline?

<p>Setting availableNow as a trigger option</p> Signup and view all the answers

What should be done to update the gold table after processing new data files?

<p>Rerun the final query as a batch job</p> Signup and view all the answers

What is the purpose of registering a streaming temporary view in this pipeline?

<p>To perform data transformation in Spark SQL</p> Signup and view all the answers

What is indicated by the metadata added during the enrichment of raw data?

<p>The source file and time of ingestion</p> Signup and view all the answers

What happens to the stream after it has been created but not yet activated?

<p>It requires a display or write stream operation to activate</p> Signup and view all the answers

How does the streaming query respond when new data is detected in the active stream?

<p>It adds the new records to the existing dataset</p> Signup and view all the answers

What conclusion can be drawn about the write stream for the orders_bronze table?

<p>It must be manually initiated to start processing data</p> Signup and view all the answers

What is the first step to begin processing the dataset in the pipeline?

<p>Running the Copy-Datasets script</p> Signup and view all the answers

What is the role of the Auto Loader in this pipeline?

<p>To automate the loading of data from a directory</p> Signup and view all the answers

Match the following layers of the data pipeline with their primary functions:

<p>Bronze layer = Raw data ingestion and storage Silver layer = Data enrichment and processing Gold layer = Aggregation and analysis Streaming layer = Real-time data processing</p> Signup and view all the answers

Match the following components with their roles in the Spark SQL environment:

<p>Temporary view = Enables SQL queries on dynamic data Stream write = Writes processed data continuously Aggregation = Combines data based on specified criteria Trigger availableNow = Processes all available data immediately</p> Signup and view all the answers

Match each data processing stage with its specific activity:

<p>Ingestion = Loading data from sources into storage Enrichment = Adding additional context to the data Aggregation = Summarizing data into useful metrics Streaming = Handling data in real-time as it arrives</p> Signup and view all the answers

Match the data processing modes with their descriptions:

<p>Complete output mode = Rewrites the entire aggregation each time Append output mode = Adds new records to the existing dataset Trigger once = Processes data continuously until completed Trigger availableNow = Handles any available data in micro batches</p> Signup and view all the answers

Match the following Spark configurations with their impacts:

<p>IgnoreChanges option = Allows reading changed data in streams Complete mode = Requires full data rewrite on updates Streaming query termination = Stops when no new data is available Batch job trigger = Runs a snapshot at a specific time</p> Signup and view all the answers

Match the following components of the Delta Lake pipeline with their functions:

<p>Auto Loader = Configures a stream read on source files Temporary view = Facilitates data transformation in Spark SQL Bronze table = Stores enriched raw data Write stream = Processes and writes data to Delta Lake</p> Signup and view all the answers

Match the following stages of data processing with their descriptions:

<p>Enrichment phase = Enhances raw data with metadata Active stream = Receives updates in real-time Incremental write = Adds new records to the Delta Lake table Streaming query = Monitors for changes in data</p> Signup and view all the answers

Match the following types of data files with their characteristics:

<p>Parquet files = Columnar storage format for big data Temporary view = A virtual table for SQL queries Delta Lake table = ACID-compliant storage format Streaming data = Continuously flowing data for real-time processing</p> Signup and view all the answers

Match the following tools or functions with their roles in the pipeline:

<p>Spark SQL = Enables querying of structured data Display operation = Activates the created stream Record count = Verifies data entries in the table Function to trigger file arrival = Initiates the processing of new data files</p> Signup and view all the answers

Match the following outcomes with their related actions in the process:

<p>Writing to the bronze table = Logs initial data into Delta Lake Cancelling the stream = Halts the data processing temporarily Data transformation = Changes raw data into a usable format Triggering new file arrival = Prompts immediate data update in the stream</p> Signup and view all the answers

Match the following terms with their definitions:

<p>Schema inference = Automatically determines data structure Stream read = Continuously reads incoming data Metadata = Data providing information about other data Batch jobs = Processes data in fixed-size chunks</p> Signup and view all the answers

Match the following features of Delta Lake to their benefits:

<p>ACID compliance = Ensures data integrity during transactions Real-time updates = Allows immediate access to new data Schema evolution = Supports changes in data structure over time Data versioning = Maintains historical records of data changes</p> Signup and view all the answers

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_tmp is 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_bronze table.
  • The customers temporary 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 availableNow option, 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 ignoreChanges are 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_tmp is 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_bronze table.
  • The customers temporary 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 availableNow option, 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 ignoreChanges are 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_tmp is 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_bronze table.
  • The customers temporary 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 availableNow option, 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 ignoreChanges are 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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Description

This quiz explores the concepts and steps involved in creating a Delta Lake multi-hop data pipeline using a bookstore dataset. Participants will learn about the Auto Loader and how to configure it for streaming data reads from Parquet files. Join us to enhance your understanding of data engineering techniques with Delta Lake.

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