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Data Engineering Capabilities

Interlocking Black Triangles

Data Collection

  • Structured and Unstructured

  • Semi-structured

  • Big Data, RDBMS, NDBMS

  • H-Distributed File System

  • Flast File (including txt,CSV,JSON,etc)

  • Email, Web App APIs

Abstract Pattern 25

Data Processing

  • Data Cleansing

  • Data Profiling

  • Normalization, Text Mining

  • Data Extraction

  • Data Transformation

  • Data Warehousing


Data Optimization

  • Cross-Validation

  • Hyperparameter tuning

  • Gradient Descent, SGD

  • Ensemble & Boosting


  • F-measure, Precision-recall

Machine Learning

  • Regression Algorithms

  • Classification Algorithms

  • Support Vector Machine (SVM)

  • KD, Decision tree, Random Forest

  • K ~ Nearest Neighbors

  • Latent Dirichlet Allocation

Feature Engineering

  • Locality Sensitive Hashing (LSH)

  • Principal Component Analysis (PCA)

  • Singular Value Decomposition

  • Image & Text Transformation (word2vect, TF-IDF)

  • Vectorization, Indexing


  • Model Serving

  • Model Pipeline

  • Model Deployment

  • Managed Deployment

  • Monitoring

  • Post Deployment Evaluation

Data Engineering Use Cases

Anomaly Detection

Apply predictive models to real-time transactional data that monitors supervised and unsupervised processes to identify fraudulent activities and take preventative actions.

Internet of Things (IOT)

Stream data from connected devices and process them for value-added operational analytics: Optimizing supply chains and the management of assets.

Predictive Analytics

Design custom recommendation engine that reduces dimensionality and applies collaborative filtering to recognize patterns from historical, past behaviors and third-party APIs to make predictions about future business opportunities.

Social & Emotional Dynamics

Perform sentiment analysis of reviews and comments about products and services across various social and digital media platforms.

360 degree Vista Point

Build single view of X (customer, employee, supplier, and partner) to understand, segment and manage information in a more effective way to improve engagement and satisfaction.

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