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Wide Analytics
Streaming Platform

WASP is our end-to-end framework that leverages real time streaming analytics in the central platform and Artificial Intelligence at the Edge.

It is a scalable solution that works in real production environments where high volumes and performances are a must.

Benefits

Easy processing
pipelines management

​Data Lineage
on streaming pipelines

Real time massive
data analysis

Machine Learning
in streaming

Real time and predictive
alerts notification

​Open
Source

Wasp
Ecosystem

For Business Users

  • New Business Scenarios – not feasible with a traditional Data Analysis approach
  • Self-service Data Access
For Data Scientists
  • Pluggable Data Science Environment
  • Notebook access to the data
  • Easiness data pipelines management via pre-built “streaming analytics”
  • Expandable ML libraries
For IT Dept
  • Deployable both On-Premise or in Cloud
  • Pluggable on the most common BigData frameworks and Cloud PaaS providers
  • Open Source Core platform
  • API Data exposure: raw data and processed data at different stages

Business Cases

Insurance

  • Claims Analytics
  • IoT platform
  • IoT Device Management

Banking

  • Data Quality Management
  • Real Time Data Monitoring
  • Mainframe Offload

Utility

  • Anomaly Detection & Predictive Maintenance
  • Smart Metering

​Manufacturing

  • Production Line Monitoring
  • Anomaly Detection & Predictive Maintenance

IT & Applications

  • Security & Network Monitoring
  • Users & Behaviour Monitoring
  • Threats Monitoring

Retail

  • Real Time Life Cycle Process Monitoring

Wasp Data Quality

Wasp DQ is part of the WASP Platform

Allows monitoring the quality of the data for a pipeline built within a big data environment.
The tool enables the control of the pipeline (both batch or streaming) from raw data to data model, leveraging natively Spark computations, without any external access to the environment typical of the traditional DQ tools.

  • Spark based: scalable, fault tolerant, made expressely for Big Data environments
  • Flexible: it supports Cloudera or Cloud data formats and data stores, works both for batch or streaming
  • Virtual data sources: supports virtual data sources for data preparation
  • Stats: approximate statistics for high volume data
  • Configuration UI: we provide a full functional UI for Business Users
  • SQL access: it’s possible to interface also external Databases to import or export specific data or rules written by other tools
  • Metrics can be applied on tables or columns: by combining different metrics, it’s possible to obtain a specific data quality check
  • Doesn’t need any other environment other than the cluster itself