Bir eğitmen tarafından verilen bu bir günlük eğitimde katılımcılara Google Bulut Platformu'nun büyük veri yetenekleri tanıtılmaktadır. Sunumlar, demolar ve uygulamalı laboratuvarlardan oluşan bu eğitim sayesinde katılımcılar, Google Bulut platformu hakkında genel bir bilgi sahibi olmakta ve veri işleme ve makine öğrenimi yetenekleri hakkında da ayrıntılı bilgiler edinmektedir. Bu eğitim, Google Bulut Platformu'ndaki büyük veri çözümlerinin kolaylığını, esnekliğini ve gücünü gösterir.
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Önkoşullar
To get the most of out of this course, participants should have:
- Basic proficiency with common query language such as SQL.
- Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language such Python.
- Familiarity with machine learning and/or statistics.
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Outline
Module 1: Introducing Google Cloud Platform
- Google Platform Fundamentals Overview.
- Google Cloud Platform Data Products and Technology.
- Usage scenarios.
- Lab: Sign up for Google Cloud Platform.
Module 2: Compute and Storage Fundamentals
- CPUs on demand (Compute Engine).
- A global filesystem (Cloud Storage).
- CloudShell.
- Lab: Set up a Ingest-Transform-Publish data processing pipeline.
Module 3: Data Analytics on the Cloud
- Stepping-stones to the cloud.
- Cloud SQL: your SQL database on the cloud.
- Lab: Importing data into CloudSQL and running queries.
- Spark on Dataproc.
- Lab: Machine Learning Recommendations with SparkML.
Module 4: Scaling Data Analysis
- Fast random access.
- Datalab.
- BigQuery.
- Lab: Build machine learning dataset.
- Machine Learning with TensorFlow.
- Lab: Train and use neural network.
- Fully built models for common needs.
- Lab: Employ ML APIs
Module 5: Data Processing Architectures
- Message-oriented architectures with Pub/Sub.
- Creating pipelines with Dataflow.
- Reference architecture for real-time and batch data processing.
Module 6: Summary
- Why GCP?
- Where to go from here
- Additional Resources
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