Data Integration with Cloud Data Fusion

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Duración del curso
28 horas
Lugar de impartición
Madrid / Online
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Modalidad
Aula Virtual, Presencial
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Fecha de inicio
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Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

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  • Design and build data processing systems on Google Cloud
  • Process batch and streaming data by implementing autoscaling
    data pipelines on Dataflow
  • Derive business insights from extremely large datasets using
    BigQuery
  • Leverage unstructured data using Spark and ML APIs on Dataproc.
  • Enable instant insights from streaming data.
  • Understand ML APIs and BigQuery ML, and learn to use AutoML to
    create powerful models without coding.

Google Cloud Big Data and Machine Learning Fundamentals

Documentación Oficial de Google Cloud - Data Integration with Cloud Data Fusion

  • Formador Certificado por GCP
  • Más de 5 años de experiencia profesional
  • Más de 4 años de experiencia docente
  • Profesional activo en empresas del sector IT

Modulo 1: Introduction to Data Engineering

  • Explore the role of a data engineer
  • Analyze data engineering challenges
  • Introduction to BigQuery
  • Data lakes and data warehouses
  • Transactional databases versus data warehouses
  • Partner effectively with other data teams
  • Manage data access and governance
  • Manage data access and governance
  • Build production-ready pipelines
  • Review Google Cloud customer case study

Modulo 2: Building a Data Lake

  • Introduction to data lakes
  • Data storage and ETL options on Google Cloud
  • Building a data lake using Cloud Storage
  • Securing Cloud Storage
  • Storing all sorts of data types
  • Cloud SQL as a relational data lake

Modulo 3: Building a Data Warehouse

  • The modern data warehouse
  • Introduction to BigQuery
  • Getting started with BigQuery
  • Loading data
  • Exploring schemas
  • Schema design
  • Nested and repeated fields
  • Optimizing with partitioning and clustering

Modulo 4: Introduction to Building Batch Data Pipelines

  • EL, ELT, ETL
  • Quality considerations
  • How to carry out operations in BigQuery
  • Shortcomings
  • ETL to solve data quality issues

Modulo 5: Executing Spark on Dataproc

  • The Hadoop ecosystem
  • Run Hadoop on Dataproc
  • Cloud Storage instead of HDFS
  • Optimize Dataproc

Modulo 6: Serverless Data Processing with Dataflow

  • Introduction to Dataflow
  • Why customers value Dataflow
  • Dataflow pipelines
  • Aggregating with GroupByKey and Combine
  • Side inputs and windows
  • Dataflow templates
  • Dataflow SQL

Modulo 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

  • Building batch data pipelines visually with Cloud Data Fusion
  • Components
  • UI overview
  • Building a pipeline
  • Exploring data using Wrangler
  • Orchestrating work between Google Cloud services with Cloud Composer
  • Apache Airflow environment
  • DAGs and operators
  • Workflow scheduling
  • Monitoring and logging

Modulo 8: Introduction to Processing Streaming Data

  • Process Streaming Data

Modulo 9: Serverless Messaging with Pub/Sub

  • Introduction to Pub/Sub
  • Pub/Sub push versus pull
  • Publishing with Pub/Sub code

Modulo 10: Dataflow Streaming Features

  • Steaming data challenges
  • Dataflow windowing

Modulo 11: High-Throughput BigQuery and Bigtable Streaming Features

  • Streaming into BigQuery and visualizing results
  • High-throughput streaming with Cloud Bigtable
  • Optimizing Cloud Bigtable performance

Modulo 12: Advanced BigQuery Functionality and Performance

  • Analytic window functions
  • Use With clauses
  • GIS functions
  • Performance considerations

Modulo 13: Introduction to Analytics and AI

  • What is AI?
  • From ad-hoc data analysis to data-driven decisions
  • Options for ML models on Google Cloud

Modulo 14:Prebuilt ML Model APIs for Unstructured Data

  • Unstructured data is hard
  • ML APIs for enriching data

Modulo 15: Big Data Analytics with Notebooks

  • What’s a notebook?
  • BigQuery magic and ties to Pandas

Modulo 16: Production ML Pipelines with Kubeflow

  • Ways to do ML on Google Cloud
  • ubeflow
  • AI Hub

Modulo 17: Custom Model Building with SQL in BigQuery ML

  • BigQuery ML for quick model building
  • Supported models

Modulo 18: Custom Model Building with AutoML

  • Why AutoML?
  • AutoML Vision
  • AutoML NLP
  • AutoML tables

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