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.
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课程信息
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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
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Introduction
Introduction to Building Batch Data Pipelines
Executing Spark on Dataproc
Serverless Data Processing with Dataflow
审阅
- 5 stars65.26%
- 4 stars25.99%
- 3 stars6.29%
- 2 stars1.57%
- 1 star0.88%
来自BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD的热门评论
Great course teaching how to build batch pipelines through GCP technologies, and showing cool tools for data wrangling and analysis
Some parts of the course where not explained in full detail, especially some qwuick labs where questions were not tested or even provided with answers
Good contents. Some lab questions do not have answers. Hope provide them to know how I understand the knowledge.
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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