What is Big Data?
When we use specialized skillset and tools to manage, process and analyse data with the following characteristics. The 5vs of Big Data:
- Volume – A huge amount of data
- Variety – Different formats of data
- Value – Useful information can be extracted
- Velocity – High speed of accumulation
- Veracity – Inconsistent and uncertain data
Big Data & Data Sciences
- Data Lake
- Machine Learning
- Cloud Computing
- Business Intelligence
- Natural Language Processing (NLP)
BI, AI & ML Development
About this Service
Managing data generated from numerous sources and in multiple types requires specialized skills. Storing the data of high volume and analyzing the heterogeneous data is always challenging with traditional data management systems. Therefore, we offer services for the end-to-end Big Data ecosystem – developing Datalake, Data Warehouse and Data Mart solutions. Creating a multi-tier Ingestion and Consumption layers, followed by Transformation and Curation of data with Cloud-Native distributed clusters of Compute and Database systems.
Since, the data is meaningless unless visualized and then analyzed. The Consumption of data can we be leveraged for better Business Intelligence goals. We leverage top of the line tools, for instance, PowerBI, Tableau, FineReport, Leaflet and much more.
Components of Big Data Ecosystem
- Data Sources
- Data Lake
- Data Warehouse
- Ingestion
- Transformation
- Curation
- Consumption
- Data Analysis
- Visualization
Use Case of Big Data:
- Recommendation Engines
- 360° View of the Customer
- Fraud Prevention
- Price Optimization
- Social Media Analysis and Response
- Preventive Maintenance and Support
- Internet of Things
- Data Warehouse Offload
Advantages of Data Platform
- A Single Shared Data Repository
- All-around Availability of Data
- High Variety and Velocity of Data Sources
- Real-time Decision Analysis
- Automated Analytics and Recommendations
- Includes Orchestration and Job Scheduling
- Multi-Tenant Consumption
- Distributed Computing and Storage
Benefits of Data Science:
- Smarter Decisions
- Better Products
- Deeper Insights
- Greater Knowledge
- Optimal Solutions
- Customer-Centric Products
- Increased Customer Loyalty
- More Automated Processes, more accurate Predictive and Prescriptive Analytics
- Better models of future behaviours and outcomes in Business, Government, Security, Science,
Healthcare, Education, and more.
Cloud-Native Landscape
A Few Most Used Machine Learning Algorithms
Content-based Methods
- Uses attributes of items and users
- Items similar to those liked by other users
Collaborative Filtering
- Items liked by similar users
- Enable exploration of diverse content
Algorithms
- K-Nearest Neighbour
- Matrix Factorization
- Clustering
- k-Means
- k-Medians
- Fuzzy
- Model-based
- Density-based
- Decision Tree
- Vector Quantization
- Gaussian Naive Bayes
Deep Learning
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
Regression Algorithms
- Logistic Regression
- Linear Regression
- Stepwise Regression
