Catalogue | Kaïna-com Academy

Prérequis

Un niveau d'anglais business moyen est requise car la formation sera dispensée en anglais.

Public

Chefs de projet, chefs de produit et Managers, Développeurs et Architectes qui veulent connaître le Big Data.

Objectifs

Aujourd'hui, les entreprises ont la capacité de collecter une grande quantité de données. Le traitement d'une grande quantité de données nécessite de nouvelles technologies capables de collecter, de nettoyer, de traiter et de stocker efficacement une quantité importante d'informations.
De nombreuses entreprises sont parvenues à la conclusion que le fait de ne pas utiliser ces données collectées entraîne en réalité une perte importante d’argent. Le marché du Big Data devrait dépasser les 200 milliards de dollars cette année.
Ce cours fournit la base de l'environnement, de l'architecture, du processus et des outils disponibles Big Data et NoSQL DB. Le cours présentera également les méthodologies Big Data et les recommandations de déploiement.

Introduction

· Definition: Big Data, NoSQL

· The need for Big Data technology

· Tradition technologies Vs Big Data technologies

· Big Data project requirements

· Big Data Project workflow

Big Data Architecture

· Big Data project definitions

· Data sources & development resources

· Big Data technologies evaluation

Data Collection & Ingestion

· Streaming Concept

· Apache Kafka

· Apache Flume

· ELK package

Hadoop – Introduction

· What is Hadoop?

· Hadoop Architecture

· Hadoop File System (HDFS)

· Hadoop MapReduce

· Apache YARN

· Apache Oozie, ZooKeeper

· Project non-functional support

Project decision – Hadoop deployment

· Hadoop Distribution

· Hadoop as a service

· Can Big Data project switch environments?

· Hadoop deployment requirements

· Hadoop Performance Best Practices

Hadoop project POC

· POC environment

· Using Apache Pig! & Apache Sqoop for POC

Large-scale data processing framework

· Apache Storm

· Apache Spark

Project development cycle & deployment – Spark

· Big Data – Development methodologies

· ETL development cycle & deployment

· Tests Cycle

Big Data DB types

· Key-Values Stores

· Column Family Stores (Wide Column Stores)

· Document Databases

· Graph Databases

· Mathematical Graph as a DB

Big Data & RDBMS

· Product logic

· Apache Hive

· Apache Impala

Big Data Northbound Interfaces

· Big Data to OLAP

· BI Visualization

· Scaling BI over Big Data

Big Data – system ATP

· Big Data – system ATP

The End

· Trends & Conclusions

· Q&A

· Course’s Evaluation

Raviv Knoller

Lecturer at LOGTEL

A propos

Mr. Raviv Knoller is a senior lecturer at LOGTEL, he's lecturing since 1997.
He is the founder and CEO of BEYE (Big Data & Analytics), previously he was a founder and CEO of Ads-Vantage and participated in the SCTE 130 standardization committee, and before that he was the CTO of Vintage Ventures Company.
Before the job at Vintage Mr. Knoller worked at Scopus as business development manager in the marketing division. During this period, he was active at the DVB Simulcrypt standardization committee.
Up to his job at Scopus, Mr. Knoller was VP R&D of Wydeband (subsidiary of Comverse and Radware) - Company which involved in Content Middleware for Video and advanced services over DSL.
In Orckit (DSL Company) Mr. Knoller was a team leader in R&D, was active in ADSL Forum, and before that in Lannet he developed embedded real-time software for LAN and ATM switches.
Mr. Knoller has B.Sc and M.Sc from Bar-Ilan University, and in addition MBA from Tel-Aviv University - Executives track.

Big Data

Prérequis

Public

Objectifs

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