IoT devices generate large amounts of data that require processing and analysis. Big data refers to high-volume, high-velocity, and high-variety information that is too large for traditional data processing. IoT and big data are interconnected as IoT devices produce data on a massive scale. Common challenges with IoT and big data include having too much data to analyze effectively, difficulty capturing data, and uncertainty about how to use captured data. Technologies like Hadoop and Spark can be used to store and process large volumes of IoT and other big data.

1. IOT AND BIG DATA
KALIMULLAH LONE
DEPARTMENT OF INFORMATION TECHNOLOGY
NATIONAL INSTITUTE OF TECHNOLOGY
SRINAGAR

2. IoT DATA GENERATION

3. DATA GENERATION
https://www.internetlivestats.com/

4. WHO IS GENERATING DATA?

5. IoT DATA PROCESSING

6. BIG DATA
• Data is changing, and it shows no sign of stopping.
Along with that change, the scope and scale of
data are continuously increasing”.
• Bigdata isabout data, plain and simple. Y
es,you canadd
all sorts of adjectives when talking about “big” data,
but at the end of the day, it’s all data.
• IoTis about data, devices,and connectivity.Data– big and
small – is front and centerin the IoTworld of
connecteddevices.

7. BIG DATA
 "Big Data are high-volume, high-velocity, and high-
variety information assets that require new forms
of processing to enable enhanced decision
making, insight discovery and process
optimization” (Gartner 2012)
 Any data that exceeds our current capability of
processing can be regarded as “big”
 Structured and unstructured Data

8. BIG DATA ECOSYSTEM

9. BIG DATA PROCESSING
 Datacollection
 Datapre-processing
 Datastorage
 Dataanalysis
 Datavisualization
 Datamanagement
 Datasecurity andprotection

10. IoT & DATA CHALLENGES
 44%said that there wastoo much data to analyze effectively.
 36% said that it was difficult to capture data in the first place,
25% saying data was not captured reliably, 19% saying that data
wascaptured too slowly to beuseful.
 Once data is captured, 27% said they weren’t sure what to
use it for and were unsure what questions to ask.
 Much like data capture, 26% said that the analysis process was
too slow to be actionable, 24%said that business processes were
too rigid to allow them to act on their findings – even if they
were captured and crunched in time to be useful.
 While cost is often a limiting factor in many technology
decisions, for IoT stakeholders, ease of use appears to be a more
pressing issue than cost.

11. HADOOP

12. HDFS ARCHITECTURE

13. HADOOP VS SPARK

14. COST

15. PERFORMANCE

16. MACHINE LEARNING

17. DATA PROCESSING

18. SCALABILITY

19. SCHEDULER

20. BIG DATA ANALYSIS

21. DESIGN OF ANALYTICS PROCESS*
*Descriptive, Predictive, and Prescriptive Analytics in Healthcare

22. THANK YOU