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04

Building Batch Data Pipelines on Google Cloud

04

Building Batch Data Pipelines on Google Cloud

magic_button Cloud Dataproc Data Pipeline ETL
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13 hours Intermediate universal_currency_alt 11 Credits

Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) 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 get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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Course Info
Objectives
  • 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
Prerequisites

Experience with data modeling and ETL (extract, transform, load) activities.

Experience with developing applications by using a common programming language such as Python or Java.

Audience
Developers responsible for designing pipelines and architectures for data processing.
Available languages
English, español (Latinoamérica), 日本語, français, and português (Brasil)
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