Data Engineering on Google Cloud Eğitimi

Bir eğitmen tarafından verilen bu dört günlük eğitimde katılımcılara Google Bulut Platformu’nda veri işleme sistemlerini tasarlama ve oluşturma konusunda giriş seviyesinde eğitim verilmektedir.

To get the most of out of this course, participants should have:

• Completed Google Cloud Fundamentals: Big Data & Machine Learning OR have equivalent experience
• 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

Bu eğitim, aşağıdaki işlemler de dahil olmak üzere büyük veri dönüşümlerini yönetmekten sorumlu deneyimli geliştiriciler için tasarlanmıştır:

• Verileri ayıklama, yükleme, dönüştürme, temizleme ve doğrulama
• Veri işleme için veri hatlarını ve mimarileri tasarlama
• Makine öğrenimi ve istatistiksel modeller oluşturma ve sürdürme
• Veri kümelerini sorgulama, sorgu sonuçlarını görselleştirme ve rapor oluşturma

• Design and build data processing systems on Google Cloud Platform
• Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
• Derive business insights from extremely large datasets using Google BigQuery
• Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
• Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
• Enable instant insights from streaming data

Day 1: Serverless Data Analysis

• Module 1: Serverless data analysis with BigQuery
• Module 2: Serverless, autoscaling data pipelines with Dataflow

Day 2: Leveraging unstructured data

• Module 3: Google Cloud Dataproc Overview
• Module 4: Running Dataproc Jobs
• Module 5: Integrating Dataproc with Google Cloud Platform
• Module 6: Making Sense of Unstructured Data with Google’s Machine Learning APIs

Day 3: Serverless Machine Learning

• Module 7: Getting started with Machine Learning
• Module 8: Building ML models with Tensorflow
• Module 9: Scaling ML models with CloudML
• Module 10: Feature Engineering
• Module 11: ML architectures

Day 4: Resilient streaming systems

• Module 12: Need for real-time streaming analytics
• Module 13: Architecture of streaming pipelines
• Module 14: Stream data and events into PubSub
• Module 15: Build a stream processing pipeline
• Module 16: High throughput and low-latency with Bigtable
• Module 17: Building Dashboards