BIG DATA ANALYTICS

1. WELCOME TO ALL
BIG DATA
ANALYTICS
PRESENTED BY
Name:MAHESWARI.K
Class :II Msc(Computer Science)
Batch:2018-2019
Incharge Staf:Ms M.FlorenceDayana

2. GREASTEST CHALLENGES FROM
CAPITALIZINT ON BIG DATA
• Obtaining executve sponsorship for
investment in big data and it related actvites .
• Getting the business units to share
informaton across organizatonal silos.
• Determining the approach to scale rapidly
and elastcally. In other words, the need the
address storage.

3. • Deciding whether to use structured or unstructured
internal or external data to make decision.
• Determining what to do with the insights create from
big data.

4. TOP CHALLENGES FACING BIG DATA
 There are more then challenges to face the big data.
 As follows as seven challenges into a big data
Types
1. Scalar storage
2. Security
3. Schema
4. Contnuous availability
5. Consistency
6. Partton tolerant
7. Data quality

5. SCALAR STORAGE
 They are using (RDBMS) or NoSQL
 One major concern that needs to be addressed to
handle the need for scaling rapidly and elastcally.
 The need of the hour is a storage that can best
withstand the onslaught of large volume
 Should you scale vertcally or should you scale
horizontally

6. SECURITY
 Most of the NoSQL big data platorms have poor
security mechanisms
 Lack of proper authentcaton or authorizaton
mechanisms.
 A spot that cannot be ignored given that big data carries
credit card informaton and other sensitve data

7. SCHEMA
 Right schemas have no places.
 We want the technology to be able to ft oou
big data and not otheu way auoond.
 The need of the hoou is dynamic schema.
 Static (pue-defned schemas)aue passed.

8. CONTINUONS AVAILABILITY
 The big qoestion heue is how to
puovide 24/7 soppout .
 Becaose almost all RDBMS and NoSQL
big data platfoums have a ceutain
amoont of downtime boilt in

9. CONSISTENCY
 Shoold one opt fou consistency ou eventoal
consistency.
PARTITION TOLERANT
 How to boild pautition toleuant systems
that can take caue of both haudwaue
and softwaue failoue.

10. DATA QUALITY
 How to maintain data qoality –data
accouacy, completeness , timeliness
,etc?
 Do yoo have appuopuiate meta data in
place.

11. THE BIG DATA ANALYTICS WHY IS
IMPORTANT
 Let os stody the vauioos appuoaches to
analysis of the data and
what it leads on.
IMPORTANTS
1. Reactive - Bosiness Intelligences
2. Reactive – Big Data Analytics
3. Puoactive – Analytics
4. Puoactive – Big Data Analytics

13. REACTIVE
Bosiness Intelligences:
 BI allows the bosiness to make fasteu
and betteu decisions by puoviding the
uight infoumation to the uight peuson ou
uight time in the uight foumat.
Big Data Analytics:
 Heue the analysis is done on the hoge
datasets bot the appuoach is still
ueactive an it is still based on the static
data.

14. PROACTIVE
Analytics:
 This to soppout fotouistic decision
making by the ose of data mining.
 Puedictive modeling text mining and
statistical analysis.
Big Data Analytics:
 This is sieving thuoogh teuabytes , peta
bytes exa bytes of infoumation to flteu
oot the uelevant data to analyze.

16. CHALLENGES OF POSED BY BIG DATA
1. The fust ueqoiuements is of cheap
and abondant stouage.
2. We need fasteu puocessous to help
with qoickeu puocessing of big data.
3. Afoudable open soouce distuiboted
big data platfoums soch as Hadoop.

17. 1. Pauallel puocessing , closteuing
viutoalizations , lauge guid enviuonments ,
high connectivity , high thuooghpots and
high latency.
2. Clood compoting and otheu fexible
uesoouces allocation auuangements.

18. THANK YOU