ETL data pipeline pattern

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Questions and Answers

Based on the provided text, what is the fundamental advantage of using Dataflow templates for recurring pipelines?

  • Dataflow templates allow for the creation of fully automated pipelines, requiring no manual intervention for deployment.
  • Dataflow templates facilitate reusable pipelines for similar tasks, reducing development effort and time. (correct)
  • Dataflow templates enable the use of pre-built pipelines, eliminating the need for developers to define their own.
  • Dataflow templates streamline the development process by combining pipeline design and deployment.

Which of the following aspects of Dataflow templates empower customization and scalability for various use cases?

  • The use of parameters to adjust pipeline behavior based on specific inputs and requirements. (correct)
  • The ability to define and manage multiple pipeline configurations within a single template.
  • The integration with Google Cloud's pre-built templates for common scenarios.

In the lifecycle of an interactive notebook session, what is the state of the kernel during code execution?

  • Busy (correct)
  • Starting
  • Unknown
  • Idle

Which of the following is NOT a configuration option defined during the creation of an interactive notebook session?

<p>Runtime version (D)</p> Signup and view all the answers

What is the primary way in which Dataproc Serverless for Spark facilitates data warehousing and analytics?

<p>Through its integration with BigQuery (C)</p> Signup and view all the answers

What occurs when an interactive notebook session is shut down due to inactivity?

<p>The kernel state becomes 'Unknown', and the session is terminated. (D)</p> Signup and view all the answers

What is the primary function of ephemeral clusters in the context of Dataproc Serverless for Spark?

<p>Enhancing code execution speed and efficiency (D)</p> Signup and view all the answers

Which of the following is NOT a benefit of Dataproc Serverless for Spark's integration with Google Cloud services?

<p>Directly accessing local file systems on the user's device (B)</p> Signup and view all the answers

Which of the following best describes the role of Vertex AI Workbench in the context of Dataproc Serverless for Spark?

<p>Enabling machine learning tasks and model development within the Spark environment (A)</p> Signup and view all the answers

Which Google Cloud service is specifically designed for data integration in hybrid and multi-cloud environments, and utilizes the open-source CDAP framework?

<p>Data Fusion (B)</p> Signup and view all the answers

The content emphasizes the use of Dataprep and Data Fusion for ETL data pipelines. What key advantage do these services offer over other ETL tools?

<p>They offer visual interfaces, making them more user-friendly for non-programmers. (D)</p> Signup and view all the answers

What is the primary characteristic of the ETL data pipeline pattern?

<p>Data is manipulated and transformed before being loaded into the destination system. (A)</p> Signup and view all the answers

While Dataproc is mentioned for batch data processing, what other Google Cloud service provides similar functionalities but focuses on serverless execution?

<p>Dataflow (C)</p> Signup and view all the answers

Which of the following is NOT a benefit of using Dataflow's unified programming model for batch and streaming data?

<p>Increased code complexity for handling both batch and streaming data (B)</p> Signup and view all the answers

In the provided code example, which function is responsible for reading messages from a Pub/Sub topic or subscription?

<p>ReadFromPubSub() (C)</p> Signup and view all the answers

In the code example, what is the purpose of the beam.Map(parse_message) step?

<p>Applying a specified transformation to each message, in this case, parsing it from JSON (A)</p> Signup and view all the answers

Based on the provided information, which of these statements accurately describes the role of templates in Dataflow?

<p>Templates are pre-configured pipelines that can be used for common data processing tasks (C)</p> Signup and view all the answers

In a typical streaming ETL workflow on Google Cloud, what is the primary role of Pub/Sub?

<p>Pub/Sub acts as a central hub for ingesting event data and distributing it to various systems. (B)</p> Signup and view all the answers

Which of the following is NOT considered a core characteristic of Pub/Sub?

<p>Data transformation: Pub/Sub provides built-in capabilities for complex data transformations and cleaning. (A)</p> Signup and view all the answers

Which of the following scenarios would most likely benefit from using Pub/Sub as a central messaging hub?

<p>A real-time application that requires immediate processing of events, such as fraud detection or order processing. (D)</p> Signup and view all the answers

What is the significance of the "At-least-once delivery" guarantee in Pub/Sub?

<p>Pub/Sub guarantees that each message will be delivered to at least one subscriber, even in the presence of failures. (B)</p> Signup and view all the answers

How does Pub/Sub contribute to a decoupled architecture in streaming ETL workflows?

<p>Pub/Sub allows publishers and subscribers to operate independently, without direct dependencies on each other. (B)</p> Signup and view all the answers

Which of the following statements accurately describes the role of Dataflow in a streaming ETL workflow?

<p>Dataflow provides a robust platform for real-time data processing, enabling transformation, aggregation, and enrichment of data. (D)</p> Signup and view all the answers

How does BigQuery benefit from the real-time data processed through the streaming ETL workflow?

<p>BigQuery enables the creation of sophisticated real-time dashboards and interactive reports, powered by the insights from processed data. (C)</p> Signup and view all the answers

In the context of the streaming ETL workflow, what is the primary purpose of Bigtable?

<p>Bigtable provides efficient storage for large datasets, particularly suitable for NoSQL workloads and real-time access. (C)</p> Signup and view all the answers

Flashcards

Dataproc Serverless for Spark

A managed service for running Spark jobs without managing infrastructure.

Ephemeral cluster

A temporary cluster created for job execution that is deleted afterwards.

Interactive notebook session

A runtime environment for coding, developing, and executing tasks interactively.

Kernel states

The status of the computing process which can be idle, busy, or unknown.

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Lifecycle of a notebook session

The stages from creation to active usage, and finally to shutdown.

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Max idle time

The maximum duration a session can stay inactive before being shut down.

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Cloud Storage

A service for storing and retrieving data in the cloud.

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Dataproc History Server

A component for tracking job execution history and metadata.

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ETL Architecture

A framework that defines the process of Extracting, Transforming, and Loading data into a database.

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Google Cloud Tools

Services and applications on Google Cloud for building ETL data pipelines.

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Dataproc

A managed Spark and Hadoop service on Google Cloud for batch processing of data.

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Dataprep

A user-friendly tool in Google Cloud for cleaning and preparing data for analysis.

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Batch Data Processing

Processing data in large volumes at once, typically on a scheduled basis.

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Streaming Data Processing

Continuous input and processing of data in real-time rather than in batches.

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Bigtable

A scalable NoSQL database service by Google that can be used in data pipelines.

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Data Fusion

A Google Cloud service for building and managing data integration pipelines.

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Streaming ETL

A process for continuous data ingestion, processing, and real-time analytics.

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Pub/Sub

A messaging service for high-volume event data ingestion and distribution.

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Dataflow

A service that processes data in real-time and allows for data transformations.

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BigQuery

A data warehousing solution for performing analytics on large datasets.

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Event Data

Real-time data generated from events, like system actions or user activities.

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Serverless

A cloud execution model where the user does not manage servers, allowing focus on code.

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Asynchronous Messaging

Communication where messages are sent without needing immediate response or acknowledgment.

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Batch data

Data processed in large groups at once, as opposed to real-time.

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Streaming data

Data processed continuously in real-time as it is produced.

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Apache Beam

An open-source framework for processing both batch and streaming data.

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Pipeline runner

A component that executes data processing workflows defined in Dataflow.

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Serverless execution

A model where cloud services manage server resources, allowing users to focus on code.

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WriteToBigQuery

A function that writes transformed messages into a BigQuery table, creating if needed and appending data.

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ReadFromPubSub

A function that retrieves messages from Google Cloud Pub/Sub for processing.

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Beam.Map()

A transformation function in Apache Beam that applies a function to each element in a collection.

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Dataflow templates

Predefined templates in Google Cloud for creating reusable and customizable data pipelines.

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Parameters in Dataflow

Custom inputs in Dataflow templates that tailor the pipeline execution for specific needs.

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Cloud Storage input

The source location in Google Cloud Storage where data files are stored for processing in Dataflow.

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BigQuery output table

The destination table in BigQuery where processed data is saved after transformation.

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Common scenarios templates

Google-provided templates for frequent data processing tasks to simplify implementation.

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Bigtable Data Model

A wide-column data model that supports flexible schema design with column families.

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Row Keys

Efficient indexes in Bigtable that provide quick access to data.

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High Throughput

The capability to process a large amount of data efficiently in Bigtable.

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Low Latency

The ability to retrieve data quickly, minimizing delay in Bigtable operations.

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Study Notes

Extract, Transform, and Load (ETL) Data Pipeline Pattern

  • The ETL pattern focuses on adjusting or transforming data before loading it into BigQuery.
  • Google Cloud offers various services for distributed data processing.
  • Dataprep, a user-friendly tool, is ideal for data wrangling.
  • Data Fusion supports multi-cloud environments with a drag-and-drop interface, pre-built transformations, and custom plugins.
  • Dataproc handles Hadoop and Spark workloads offering flexibility with serverless Spark options.
  • Dataproc simplifies cluster management with workflow templates, autoscaling, and ephemeral cluster options.
  • Dataflow leverages Apache Beam and facilitates batch and streaming data processing.
  • Dataflow uses a unified programming model (Java, Python, Go) simplifying development
  • Dataflow smoothly integrates with Google Cloud services like Pub/Sub, BigQuery and others.
  • Cloud Storage, BigQuery, Dataflow and Pub/Sub are Google Cloud services

Dataprep by Trifacta

  • A serverless, no-code solution for building data transformation flows.
  • Connects to a variety of data sources.
  • Provides pre-built transformation functions.
  • Enables users to chain functions into recipes for seamless execution.
  • Offers scheduling, monitoring and data transformation capabilities.

Data Fusion

  • A GUI-based tool for enterprise data integration.
  • Connects to various data sources (on-premise and cloud).
  • Leverages a drag-and-drop interface, pre-built transformations, and allows for custom plugins.
  • Runs on powerful Hadoop/Spark clusters for efficient data processing.
  • Easy creation of data pipelines using visual tools.

Dataproc

  • A managed service for Apache Hadoop and Spark workloads on Google Cloud.
  • Enables seamless running of Hadoop and Spark workloads on Google Cloud.
  • Allows using HDFS data stored in Cloud Storage.
  • Provides transformations using Spark jobs and storing results in various destinations (Cloud Storage, BigQuery, Bigtable).
  • Offers workflow templates, and autoscaling options for flexible cluster management.

Dataproc Serverless for Spark

  • Eliminates cluster management for faster deployment.
  • Offers automatic scaling, pay-per-execution pricing.
  • Enables writing and executing code without managing infrastructure.
  • Ideal for interactive notebooks and various Spark use-cases.
  • Supports serverless execution mode for batches and interactive notebook sessions.

Dataflow

  • Processes both batch and streaming data using an Apache Beam programming model.
  • Supports Java, Python and Go languages.
  • Seamlessly integrates with other Google Cloud services.
  • Provides features like pipeline runners, serverless execution, templates, and notebooks.

Bigtable

  • Suitable for streaming data pipelines requiring millisecond latency analytics.
  • Wide-column data model with column families.
  • Enables flexible schema design and quick data access using row keys.
  • Ideal for handling large datasets in time-series data, loT, financial data, and machine learning contexts.

ETL Processing Options

  • Dataprep - Suitable for data wrangling tasks, serverless option
  • Data Fusion - Ideal for data integration, particularly in hybrid/multi-cloud environments.
  • Dataproc - Supports both batch and streaming ETL with serverless execution
  • Dataflow - Recommended for various ETL scenarios, supports batch and streaming with an unified Apache Beam programming model.

Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard

  • This lab involves creating a Dataflow streaming data pipeline for a real-time dashboard.
  • Creating jobs from templates.
  • Stream data into BigQuery.
  • Monitoring the pipeline within BigQuery.
  • Evaluating data using SQL.
  • Visualizing key metrics via Looker Studio.

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M5 The Extract, Transform, and Load Data Pipeline Pattern PDF

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