Data Engineering on Google Cloud Eğitimi

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