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Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

Introduction to Building Batch Data Pipelines

This module reviews different methods of data loading: EL, ELT and ETL and when to use what

Module introduction
EL, ELT, ETL
Quality considerations
How to carry out operations in BigQuery
Shortcomings
ETL to solve data quality issues
QUIZ
Introduction to Building Batch Data Pipelines

Executing Spark on Dataproc

This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.

Module introduction
The Hadoop ecosystem
Running Hadoop on Dataproc
Cloud Storage instead of HDFS
Optimizing Dataproc
Optimizing Dataproc storage
Optimizing Dataproc templates and autoscaling
Optimizing Dataproc monitoring
Lab Intro: Running Apache Spark jobs on Dataproc
LAB: Running Apache Spark jobs on Cloud Dataproc: This lab focuses on running Apache Spark jobs on Cloud Dataproc.
Summary
QUIZ

Serverless Data Processing with Dataflow

This module covers using Dataflow to build your data processing pipelines

Module introduction
Introduction to Dataflow
Why customers value Dataflow
Building Dataflow pipelines in code
Key considerations with designing pipelines
Transforming data with PTransforms
Lab Intro: Building a Simple Dataflow Pipeline
LAB: A Simple Dataflow Pipeline (Python) 2.5: In this lab, you learn how to write a simple Dataflow pipeline and run it both locally and on the cloud.
LAB: Serverless Data Analysis with Dataflow: A Simple Dataflow Pipeline (Java): In this lab you will open a Dataflow project, use pipeline filtering, and execute the pipeline locally and on the cloud using Java.
Aggregate with GroupByKey and Combine
Lab Intro: MapReduce in Beam
LAB: MapReduce in Beam (Python) 2.5: In this lab, you learn how to use pipeline options and carry out Map and Reduce operations in Dataflow.
LAB: Serverless Data Analysis with Beam: MapReduce in Beam (Java): In this lab you will identify Map and Reduce operations, execute the pipeline, use command line parameters.
Side inputs and windows of data
Lab Intro: Practicing Pipeline Side Inputs
LAB: Serverless Data Analysis with Dataflow: Side Inputs (Python): In this lab you will try out a BigQuery query, explore the pipeline code, and execute the pipeline using Python.
LAB: Serverless Data Analysis with Dataflow: Side Inputs (Java): In this lab you will try out a BigQuery query, explore the pipeline code, and execute the pipeline using Java.
Creating and re-using pipeline templates
Summary
QUIZ

Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.

Module introduction
Introduction to Cloud Data Fusion
Components of Cloud Data Fusion
Cloud Data Fusion UI
Build a pipeline
Explore data using wrangler
Lab Intro: Building and executing a pipeline graph in Cloud Data Fusion
LAB: Building and Executing a Pipeline Graph with Data Fusion 2.5: This tutorial shows you how to use the Wrangler and Data Pipeline features in Cloud Data Fusion to clean, transform, and process taxi trip data for further analysis.
Orchestrate work between Google Cloud services with Cloud Composer
Apache Airflow environment
DAGs and Operators
Workflow scheduling
Monitoring and Logging
Lab Intro: An Introduction to Cloud Composer
LAB: An Introduction to Cloud Composer 2.5: In this lab, you create a Cloud Composer environment using the GCP Console. You then use the Airflow web interface to run a workflow that verifies a data file, creates and runs an Apache Hadoop wordcount job on a Dataproc cluster, and deletes the cluster.
QUIZ

Building Batch Data Pipelines on Google Cloud Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

Building Batch Data Pipelines on Google Cloud Training in Norway

Upcoming Trainings

Building Batch Data Pipelines on Google Cloud Training in Norway

Outline

Upcoming Trainings