| 1. 2.1 What is Apache Beam .mp4 | 6.3 MB | ||
| 1. 3.1 Using Apache Beam In Google Colab.mp4 | 17.6 MB | ||
| 1. 4.1 GCP Setup.mp4 | 37.3 MB | ||
| 1.1 2.1 - link.txt | 0 B | ||
| 1.1 3.1 - Setup Colab.py | 0 B | ||
| 1.1 Activate APIs.html | 512 B | ||
| 10. 3.10 ParDo – Customizing Beam Functions.mp4 | 29 MB | ||
| 10.1 3.10 - ParDo.py | 1.2 KB | ||
| 2. 2.2 Apache Beam Architecture Overview.mp4 | 11.3 MB | ||
| 2. 3.2 Read Inputs.mp4 | 28.7 MB | ||
| 2. 4.2 Creating Service Account and a Bucket on GCP.mp4 | 44.1 MB | ||
| 2.1 3.2 - Create.py | 204.8 B | ||
| 2.2 3.2 - Read Transform.py | 307.2 B | ||
| 3. 2.3 Apache Beam Pipeline Flow.mp4 | 31 MB | ||
| 3. 3.3 Write Outputs.mp4 | 16.7 MB | ||
| 3. 4.3 Setup Apache Beam Local (SDK).mp4 | 40.4 MB | ||
| 3.1 2.3 - link.txt | 102.4 B | ||
| 3.1 3.3 - Write Transform.py | 307.2 B | ||
| 4. 3.4 beam.Map beam.FlatMap.mp4 | 25.8 MB | ||
| 4. 4.4 Direct Runner Execution + Saving to GCS.mp4 | 54.1 MB | ||
| 4.1 3.4 - FlatMap.py | 204.8 B | ||
| 4.1 4.4 - Batch DirectRunner + GCS.py | 1.4 KB | ||
| 4.2 3.4 - Map.py | 307.2 B | ||
| 4.3 Poem.txt | 204.8 B | ||
| 5. 3.5 beam.Filter.mp4 | 21 MB | ||
| 5. 4.5 Creating a Dataflow Template.mp4 | 88.6 MB | ||
| 5.1 3.5 - Filter Lambda.py | 307.2 B | ||
| 5.1 4.5 - Batch Dataflow + GCS.py | 1.8 KB | ||
| 5.2 3.5 - Filter Lista.py | 307.2 B | ||
| 6. 3.6 beam.Flatten.mp4 | 13.6 MB | ||
| 6. 4.6 Executing Batch Job in Dataflow.mp4 | 71.4 MB | ||
| 6.1 3.6 - Flatten.py | 512 B | ||
| 7. 3.7 beam.CombinePerKey.mp4 | 16.8 MB | ||
| 7. 4.7 Creating Dataflow Template to write in Big Query.mp4 | 114.5 MB | ||
| 7.1 3.7 - CombinePerKey.py | 512 B | ||
| 7.1 4.7 - Batch Dataflow + BigQuery.py | 3.3 KB | ||
| 8. 3.8 beam.combiners.Count.PerKey().mp4 | 8 MB | ||
| 8. 4.8 Executing Batch Job to write in Big Query.mp4 | 56.4 MB | ||
| 8.1 3.8 - Combiners.Count.Perkey.py | 512 B | ||
| 9. 3.9 beam.CoGroupByKey.mp4 | 16.4 MB | ||
| 9.1 3.9 - CoGroupByKey.py | 1.1 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 43 total files | |||
Data Engineering with Google Dataflow and Apache Beam
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 749 MB | Duration: 2h 20m
First steps to Extract, Transform and Load data using Apache Beam and Deploy Pipelines on Google Dataflow
What you'll learn
Apache Beam
ETL
Python
Google Cloud
DataFlow
Google Cloud Storage
Description
This course wants to introduce you to the Apache Foundation's newest data pipeline development framework: The Apache Beam, and how this feature is becoming popular in partnership with Google Dataflow. In a summary, we want to cover the following topics:
1. Understand your inner workings
2. What are your benefits
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