A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration. Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.

1. Architecture Data Lake
Wael GHAZOUANI
Paris June 6th 2020
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2. Introduction
•What is Big Data?
What Data Lake ?
•What is Hadoop?
•What is Hadoop Data Lake?
•When to use Hadoop?
•Getting Started with Big Data
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3. Introduction
• To store a large amount of data in a
repository that will be the main, if
not the only, source of information
for a number of applications. In Big
Data lingo, such a repository is called
a Data Lake
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4. What is Big Data?
• •A collection of data sets so
large and complex that it becomes
difficult to process using on-hand
database management tools or
traditional data processing
applications
• •Due to its technical nature, the
same challenges arise in Analytics
at much lower volumes than what
is traditionally considered Big
Data.
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5. Data Lake
• This lake of data will contain very varied types of data: log files,
images, binary files, etc. the data will be read by a large number of
varied applications. For these applications, data will be the only
source of truth. Indeed, as this data will be large, there will be no
question of duplicating it. The data contained in the data lake is
therefore precious. For this reason, the data must never be modified
or deleted. The data lake will contain datasets which will serve as a
reference point
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7. Why Now? Open APIs, Platforms, Ecosystems
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8. What is Hadoop?
•Apache Hadoop is an open source software platform for distributed
storage and distributed processing of very large data sets on computer
clusters built from commodity hardware.
Hadoop services provide for data storage, data processing, data access,
data governance, security, and operations
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9. Why Hadoop?
•Scalability issue when running jobs processing terabytes of data
•Could span dozen days just to read that amount of data on 1 computer
•Need lots of cheap computers
•To Fix speed problem
•But lead to reliability problems
•In large clusters, computers fail every day
•Cluster size is not fixed
•Need common infrastructure
•Must be efficient and reliable
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10. Hadoop Platform
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12. •Unified distributed fault-tolerant and scalable storage
•Cost Effective.
•Hadoop can be 10 to 100 times less expensive to deploy than traditional
data warehouse.
•Just-in-time ad’hoc dataset creation.
•Extract and place data into an Hadoop-based repository without first
transforming the data (as you would do for a traditional EDW).
•All jobs are created in an ad hoc manner, with little or no required
preparation work.
•Labor intensive processes of cleaning up data and developing schema are
deferred until a clear business need is identified.
•Data governance for structured and unstructured “raw” data
Hadoop to the Rescue to offer
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Hadoop Data Lake vs. Enterprise
Data Warehouse

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When to Use Hadoop?

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How to
Implement
Hadoop?
McKinsey Seven
Steps Approach

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How to Implement Hadoop?
Dataiku Method supported by a tool
http://www.dataiku.com/dss/

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https://www.linkedin.com/in/ghazouani-wael-251270115/
Waelghazouani.soltani@gmail.com