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.
About this Course
What you will learn
Review different methods of data loading: EL, ELT and ETL and when to use what
Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
Build your data processing pipelines using Dataflow
Manage data pipelines with Data Fusion and Cloud Composer
Syllabus - What you will learn from this course
Introduction to Building Batch Data Pipelines
Executing Spark on Dataproc
Serverless Data Processing with Dataflow
- 5 stars65.30%
- 4 stars25.92%
- 3 stars6.32%
- 2 stars1.56%
- 1 star0.87%
TOP REVIEWS FROM BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD
There were too many labs with services that take 30-40 minutes just to spin up. I wouldn't have a problem with all the labs if the services took 2-5 minutes to spin up.
Great course teaching how to build batch pipelines through GCP technologies, and showing cool tools for data wrangling and analysis
A great course to help understand the various wonderful options Google Cloud has to offer to move on-premise Hadoop workload to Google Cloud Platform to leverage scalability of clusters.
Excellent course with appropriate explanation on cloud data fusion, data composer, data proc and cloud data-flow. Must learn course for all aspiring Big Data Engineers.
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