This document provides an introduction to key concepts in data analytics including data, information, the types of data analytics, benefits and use cases of data analytics, challenges, and common data analytics tools. It also covers related topics like streaming data, data visualization, and big data.

1. What is Data
&
Information
?

7. Introduction to Analytics
And Architecture
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8. Data Analytics:
● handling data collected by systems
● generate the insights
● improve decision making
○ with facts based on data.
What is Data Analytics?🤔
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9. Types of Data Analytics
● Descriptive Analytics
● Diagnostic Analytics
● Predictive Analytics
● Prescriptive Analytics
What Are the Types of Data Analytics?🤔

10. Benefits Of Data Analytics
Credit card Fraud Detection Customer Personalization Security Threats Detection
Real time Alerting User Behaviour
Financial Modeling

11. Use Cases:
● Social Media
● Ecommerce
● Information Security
● Logistics
● Factory operations
● Internet of Things
What Might be the Use
Cases of Data Analytics?🤔

12. Challenges(5 V’s)

13. Data Analytics Tools 🤔🤔 and Use Cases
Amazon Athena
Interactive analysis
Amazon EMR
Big Data Processing
Amazon Elasticsearch
Operational Analytics
Amazon Kinesis
Real-Time Analysis
Amazon RedShift
Data WareHouse
Amazon QuickSight
Dashboards & Visualizations
Amazon Cloud Watch
monitoring & management

15. Introduction to High
Performance Computing
HPC Workshop
AWS

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18. High Speed computing:
● implemented in
○ Super Computers for Scientific Research.
Main Area Of Discipline:
● Developing Parallel Processing Algorithm
● Developing Software
shifted from supercomputers to computing Clusters.
Application Areas of HPC

19. Climate Modeling
Data Analysis
Drug Discovery
Protein Folding
Energy Research
Why Do We need Ever Increasing Performance?

20. When do you actually need an HPC ?🤔
● Complete time consuming operations in less time
● Complete an operation under a tight schedule
● Perform a high number of operations per second

21. What is Streaming Data?
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22. Streaming Data:
● generated continuously by thousands of data sources
Data Stream:
● Continuous
● Ordered
● Changing
● Fast
● huge amount
Traditional DBMS:
Data stored:
● Finite data sets
● Persistent data sets
Streaming data includes a wide variety of data
● log files
● ecommerce purchases,
● in-game player activity,
● information from social networks,
● financial trading floors,
● geospatial services,
● telemetry from connected devices or instrumentation in data centers.

23. Core Banking
● Improved Scalability
● Met HA and SHA needs
Online Gaming
● Increased reliability
● Accurate and real time data
● Ability to process data at scale
● Faster ramp time

24. Government Services
● Near real time events and
better data quality
● Increased efficiency
● Produce and store data
● Better privacy
Financial Services
● Enhanced Customer experience
● Improved fraud detection engine

25. Real-Time Fraud Detection
● Act in real time
● Detect Fraud
● Minimize risk
● Improve customer experience
Real-Time E-Commerce
● OnBoarding New Merchants Faster
● Enabled 360 view of customers
● Enhanced Performance & Monitoring
● Projected saving of Millions of dollars
Do You know About Amazon GO?

27. Benefits of streaming data
● Improve operational efficiencies
● Reduce infrastructure cost
● Provide faster insights and actions

28. How To Design Stream Analytics solution?

29. Challenges in working with streaming data
Streaming data processing requires two layers:
● a storage layer
○ record ordering
○ strong consistency
■ Fast
■ Inexpensive
■ replayable
● Reads
● Writes
● a processing layer.
○ consuming data from the storage layer
○ running computations on that data
○ notifying the storage layer to delete data that is no longer needed.
● Scalability
● data durability
● fault tolerance

30. Infrastructure to build streaming data applications:
● Amazon Kinesis Data Streams,
● Amazon Kinesis Data Firehose,
● Amazon Managed Streaming for Apache Kafka (Amazon MSK),
● Apache Flume,
● Apache Spark Streaming,
● Apache Storm.

32. Working with streaming data
on AWS
Amazon Kinesis is a platform for streaming data on AWS
● load and analyze streaming data
● custom streaming data applications
offers three services:
● Amazon Managed Streaming for Apache Kafka
(Amazon MSK).
● Amazon Kinesis Data Firehose,
● Amazon Kinesis Data Streams
run other streaming data platforms
● Apache Flume,
● Apache Spark Streaming, and
● Apache Storm
○ on Amazon EC2

33. What is Visualization?🤔🤔🤔
any technique for creating images, diagrams, or animations to communicate a message.
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35. Why is data visualization important?
Data visualization is a:
● Quick & easy way
○ to convey concepts in a universal manner
○ can experiment with different scenarios by making slight adjustments.
● Identify areas that need attention or improvement.
● Clarify which factors influence customer behavior.
● Help you understand which products to place where.
● Predict sales volumes.

36. Data visualization tools
business intelligence (BI) reporting tool.
set up visualization tools to:
● generate automatic dashboards that track company performance across key performance indicators
(KPIs)
● visually interpret the results.
Kibana Tableau Grafana QuickSight Power BI

37. What is Big Data?
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38. BIG DATA:
● collection of data
huge in volume
growing exponentially
with time.
Types Of Big Data
● Structured
● Unstructured
● Semi-structured

39. Why Learn Big Data?
● Gartner – Big Data is the new Oil.
● IDC – Its market will be growing 7 times faster than the overall IT market.
● IBM – It is not just a technology – it’s a Business Strategy for capitalizing on information resources.
● IBM – Big Data is the biggest buzz word because technology makes it possible to analyze all the
available data.

40. Characteristics Of Big Data

42. Your First Big Data
Application on AWS
Word Count example
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