The continual increase of digital data in organizations has led to the emergence of "Big Data". This concept covers issues of storing and preserving vast quantities of data as well as the issues of how to draw on the potential value that such masses of data represent. This seminar presents the specific problems of Big Data and the potential technical solutions, from data management to different types of processing.
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2d - 14h
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The continual increase of digital data in organizations has led to the emergence of "Big Data". This concept covers issues of storing and preserving vast quantities of data as well as the issues of how to draw on the potential value that such masses of data represent. This seminar presents the specific problems of Big Data and the potential technical solutions, from data management to different types of processing.
Teaching objectives
At the end of the training, the participant will be able to:
Learn the main concepts of Big Data.
Identify the economic issues
Evaluate the pros and cons of Big Data.
Understand the main problems and potential solutions
Identify the main methods and areas of application for Big Data
Intended audience
CIOs, technical directors, project managers, architects, IS managers.
Prerequisites
Basic knowledge of technical architectures.
Course schedule
Introduction
The origins of Big Data: A world of digital data, e-health, timeline.
The four-V's definition: Origins of the data.
A breakthrough: Changes in quantity, quality, and habits.
Servers, disks, networks, and use of SSD drives, importance of network infrastructure.
Cloud architectures and more traditional architectures.
Benefits and difficulties.
The TCO. Power consumption: Servers (IPNM), drives (MAID).
Object storage: principle and benefits.
Object storage compared to traditional NAS and SAN storage.
Software architecture.
Storage management location levels.
Software-Defined Storage.
Centralized architecture (Hadoop File System).
Peer-to-peer and hybrid architectures.
Interfaces and connectors: S3, CDMI, FUSE, etc.
Future of other storage types (NAS, SAN) relative to object storage.
Data protection
Preservation over time in the face of increased volumes.
Online or local backups?
Traditional archiving and active archiving.
Links with storage hierarchy management: Future of magnetic tape.
Multisite replication.
Damage to storage media.
Scope processing methods
Classification of analysis methods based on data volume and processing power.
Hadoop: The Map Reduce processing model.
The Hadoop ecosystem: Hive, Pig. The difficulties of Hadoop.
OpenStack and the Ceph data manager.
Complex Event Processing: An example? Storm.
From BI to Big Data.
Return to decisional and transactional models: NoSQL databases. Types and examples.
Data ingestion and indexing. Two examples: Splunk and Logstash.
Open-Source crawlers.
Search and analysis: Elasticsearch.
Learning: Mahout. In-memory.
Visualization: Real-time or not, in the Cloud (Bime), comparison of QlikView, Tibco Spotfire, and Tableau.
A general architecture of data mining via Big Data.
Usage case through examples and conclusion
Anticipation: Needs of users within companies, equipment maintenance.
Security: People, fraud detection (mail, taxes), the network.
Recommendation. Marketing analysis and impact analyses.
Path analyses. Distribution of video content.
Big Data for the automotive industry? For the oil industry?
Should you begin a Big Data project?
What future is there for data?
Governance of data storage: Roles and recommendations, Data Scientists, skills involved in a Big Data project.
Customer reviews
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Dates and locations
Select your location or opt for the remote class then choose your date.
Remote class
No session at the moment, we invite you to consult the schedule of distance classes.