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Start Date: 03/03/2022
Price 2,094 ILS
DURATION 1 Day
Course Overview
This course takes a look at the Big Data landscape and provides basic understanding of Big Data concepts, technologies and their applications. By providing real usage examples it allows participants to understand better how Data could be used to better serve their businesses while introducing few implementation best practices.
This course is aimed to provide CTOs, Architects, Technical leaders and Team leaders an insight of the way data at large scale could be used within their organizations and how commonly it is done. It should serve as an entry point to examine what is possible. The course requires knowledge of traditional computer systems architectures
Who should attend?
This course is aimed to provide CTOs, Architects, Technical leaders and Team leaders an insight of the way data at large scale could be used within their organizations and how commonly it is done.Prerequisite:
The course requires experience and knowledge of traditional computer systems architectures
Course Outline:
1. Introduction
• Brief History
• Big Data / Data Science / Analytics
• Collection of data from different sources (internal/external)
• Open Source Tools
2. Big Data
• Sample Usages
– Scaling Data
– IoT
– IoT
• Volume Velocity Variety
• Structured vs. Unstructured Data
• Immutable Data
• System Requirements
• NoSQL
• Ability to access and process data
• Stream Processing
3. Analytics
• Sample Usages
– Page Rank
– Marketing
– Marketing
• Decoupled Systems – ETL
• Data Lake
• Analytics vs, traditional warehouse
4. Data Science
• Sample Usages
– Vision (OCR, face, logo, label)
– NLP (syntax analysis, sentiments,…)
– NLP (syntax analysis, sentiments,…)
• Machine Learning
– Supervised Learning
– Unsupervised Learning
– Clustering
– Unsupervised Learning
– Clustering
• Deep Learning
5. Tools
• Processing Frameworks
• Processing Frameworks
– Hadoop
– Spark
– Stream Processing
– Apache BEAM
– Spark
– Stream Processing
– Apache BEAM
• Storage Tools
• Search Engines
• Analytics
• VIsualization
• Machine Learning
• Deep Learning
• Software Infrastructures
6. Big Data on the Cloud
• Evolution
• Storage
• Analytics
• Machine Learning
• Serverless Computing
7. Conclusion
• Sample Architectures
