The document discusses challenges in modern data processing like increasing data sources and outdated data models. It introduces the SMACK stack for processing big data in real-time. The SMACK stack uses components like a processing engine, cluster manager, decentralized data store, and message broker to extract, transform, and analyze batched and streaming data using tools like Spark, machine learning, and real-time APIs. An example application is a healthcare demo that monitors patients remotely, detects anomalies in vital signs during activities, and alerts doctors.

2. Challenges in modern data-processing
Data is getting bigger with ever-
INCREASING data sources
Data models from 1 hour ago are
practically obsolete
Analysis gets TOO SLOW to get any ROI
Data freshness matters more than data volume

3. SMACK Stack components
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Processing Engine
Cluster Manager
Concurrency Model
Decentralized Data Store
Message Broker
 ETL/ Machine Learning
 In-memory data fabric
 Cluster management
 Resource management
 Actor-based concurrency framework
 NoSQL Columnar high availability database
 Solid data fabric for Spark compute jobs
 Handle high-volume data push
 Process streams of real-time data

4. SMACK Stack – Functional Flow
Decentralized
Data Store
Read the data
Write the model
Read the Model
Data Source,
Files, DB
Extracts
Batched Data
Real Time
APIs
Streaming
Data
Services
Model
Alerts and Notification
Fast Analytics
Event
Processing
Machine Learning

5. Machine Learning – Real World Applications

6. A glimpse of what we have done
DECISION
SUPPORT SYSTEM
BEHAVIORAL
ANALYTICS
PREDICTIVE
ANALYTICS
REFERENCE DATA
ANALYTICS
Healthcare Media Analytics
Platform (HMAP)
Sentiment Analysis of
Social Media Data
Recommender
System
Fraud Management
System for Banking
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7. OUR DEMO

8.  End-User Scenario
 A doctor remotely monitoring 9 patients
with a cardiac condition
 Patient involved in performing different
types of activities
 Real-time information on several
parameters captured and sent to the
backend platform
 Analytics Component
 As soon as an anomaly is identified, the
patient is indicated with a red indicator.
 The doctor can view a patient’s info.
 Patient’s activities
 Heart rate during those activities
 Anomaly score (Probability)
Demo Use Case
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