xREL.v3

.:PROUDLY PRESENTS:.
Cloud Academy AWS Big Data Specialty - Data Collection
Release Date.: 04-09-2019
Type.: Bookware
Disks.: 40x15mb
Link.: https://cloudacademy.com

Release Notes

Course Description:
In course one of the AWS Big Data Specialty Data Collection
learning path we explain the various data collection
methods and techniques for determining the operational
characteristics of a collection system. We explore how to
define a collection system able to handle the frequency of
data change and the type of data being ingested. We
identify how to enforce data properties such as order, data
structure, and metadata, and to ensure the durability and
availability for our collection approach Intended audience:
This course is intended for students looking to increase
their knowledge of data collection methods and techniques
with Big Data solutions.
Prerequisites:
While there are no formal pre-requisites students will
benefit from having a basic understanding of analytics
services available in AWS. Recommended courses - Analytics
Fundamentals https://cloudacademy.com/amazon-web-
services/analytics-fundamentals-for-aws-course/
Learning objectives:
Recognize and explain the operational characteristics of a
collection system.
Recognize and explain how a collection system can be
designed to handle the frequency of data change and type of
data being ingested.
Recognize and identify properties that may need to be
enforced by a collection system.
This course includes:
45 minutes of high-defnition videos
Live hands-on demos

What You'll Learn:
Introduction to Collecting Data: In this lesson we'll
prepare you for what we'll be covering in the course; the
Big Data collection services of AWS Data Pipeline, Amazon
Kinesis and AWS Snowball.
Introduction to Data Pipeline: In this lesson we'll discuss
the basics of Data Pipeline.
AWS Data Pipeline Architecture: In this lesson we'll go
into more detail about the architecture that underpins the
AWS Data Pipeline Big Data Service.
AWS Data Pipeline Core Concepts: In this lesson we'll
discuss how we define data nodes, access, activities,
schedules and resources.
AWS Data Pipeline Reference Architecture: In this lesson
we'll look at a real life scenario of how data pipeline can
be used.
Introduction to AWS Kinesis: In this lesson we'll take a
top level view of Kinesis and it's uses.
Kinesis Streams Architecture: In this lesson we'll look at
the architecture that underpins Kinesis.
Kinesis Streams Core Concepts: In this lesson we'll dig
deeper into the data records.
Kinesis Streams Firehose Architecture: In this lesson we'll
look at firehose architecture and the differences between
it and Amazon Kinesis Streams.
Firehose Core Concepts: Let's take a deeper look at some
detals about the Firehose service.
Kinesis Wrap-Up: In this summary we'll look at the
differences between Kinesis and Firehose.
Introduction to Snowball: Overview of the Snowball Service.
Snowball Architecture: Let's have a look at the
architecture that underpins the AWS Snowball big data service
Snowball Core Concepts: In this lesson we'll look at the
details of how Snowball is engineered to support data transfer.
Snowball Wrap-Up: A brief summary of Snowball and our course.

Greetings fly out to:

Kodemusen, KoseBamsen

STM is back.
For all the ppl we worked with
in the past. We salute you.

NFO by NiMiTech
Updated: 09/09/2002