Upcoming Events at Austin
Description
Data Engineering on Google Cloud Platform (4 days) This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on l
Event Details
Data Engineering on Google Cloud Platform
(4 days)
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. A personal laptop is required for all workshops and will not be provided.
Objectives
This course teaches participants the following skills:
Design and build data processing systems on Google Cloud Platform
Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
Derive business insights from extremely large datasets using Google BigQuery
Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Enable instant insights from streaming data
Audience
This class is intended for experienced developers who are responsible for managing big data transformations including:
Extracting, Loading, Transforming, cleaning, and validating data
Designing pipelines and architectures for data processing
Creating and maintaining machine learning and statistical models
Querying datasets, visualizing query results and creating reports
Prerequisites
To get the most of out of this course, participants should have:
Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience
Basic proficiency with common query language such as SQL
Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python
Familiarity with Machine Learning and/or statistics
Course Outline
Module 1: Google Cloud Dataproc Overview
Creating and managing clusters.
Leveraging custom machine types and preemptible worker nodes.
Scaling and deleting Clusters.
Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2: Running Dataproc Jobs
Running Pig and Hive jobs.
Separation of storage and compute.
Lab: Running Hadoop and Spark
(4 days)
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. A personal laptop is required for all workshops and will not be provided.
Objectives
This course teaches participants the following skills:
Design and build data processing systems on Google Cloud Platform
Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
Derive business insights from extremely large datasets using Google BigQuery
Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Enable instant insights from streaming data
Audience
This class is intended for experienced developers who are responsible for managing big data transformations including:
Extracting, Loading, Transforming, cleaning, and validating data
Designing pipelines and architectures for data processing
Creating and maintaining machine learning and statistical models
Querying datasets, visualizing query results and creating reports
Prerequisites
To get the most of out of this course, participants should have:
Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience
Basic proficiency with common query language such as SQL
Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python
Familiarity with Machine Learning and/or statistics
Course Outline
Module 1: Google Cloud Dataproc Overview
Creating and managing clusters.
Leveraging custom machine types and preemptible worker nodes.
Scaling and deleting Clusters.
Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2: Running Dataproc Jobs
Running Pig and Hive jobs.
Separation of storage and compute.
Lab: Running Hadoop and Spark
