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    Wendelin Big Data Analytics FINANCE

    • Last Update:2019-10-23
    • Version:001
    • Language:en

    Key Benefits

    • Out-of-Core Processing
    • Parallel Computing
    • Persistency
    • Ingestion, Storage, Analysis
    • Convergence Ready

    Analysis Scenarios

    • Search fraudulent patterns
    • Intrusion detection
    • Predict machine failures

    Correlation Scenarios

    • Collect data from multiple sources
    • Aggregate and structure
    • Intelligent search/correlation

    Big Data Analysis & Machine Learning using Wendelin

    Wendelin is an open-source, 100% Python-based platform for data ingestion, storage and analysis. Originating from an ongoing EU research project to develop a Big Data Analysis and Machine Learning stack "Made in France", Wendelin integrates popular libraries such as Jupyter, SciPy, Pandas, matplotlib or Scikit.Learn to offer a range of tools for analysing data without having to recompile and beyond the limitations of available memory.

    Wendelin Analytics for the financial industry

    The Wendelin stack is 100% open source allowing to setup autonomous cloud-based or local infrastructures as well as applications. The underlying architecture uses NEO distributed storage for persistency and parallel computing, while the out-of-core feature developed for Wendelin enables computations independent of the memory available in a cluster.

    Possible usage scenarios for Wendelin include timeseries analysis for searching fraudulent patterns in transactions, machine-learning based prediction of human (cross selling) or machine behaviors (ATM failures) or simply intrusion detection through analysis of activity logs.

    Wendelin also offers the ability to collect data in real time from multiple sources (ATM, websites, ecommerce, customers) through a single data collector (Treasure Data's FluentD). This aggregated data could then be structured using machine learning tools for further analysis, as well as intelligent or correlation searches.

    Wendelin Stack

    Wendelin Logo Wendelin Core
    Wendelin Logo NEO
    Wendelin Logo ERP5
    Wendelin Logo SlapOS

    Wendelin Core: Analytics

    Wendelin Core allows processing of ingested data using various machine learning and analysis tools for extraction of metadata, normalization and structuring. Scikit.learn is used as core library as it shares the same low level representation of data with NEO.

    NEO: Data Archive

    Wendelin stores raw data in local or private cloud database powered by SlapOS and NEO, whose scalable, NoSQL storage engine can leverage a redundant array of inexpensive servers to achieve virtually infinite data processing capacity.

    ERP5: PaaS

    ERP5 is the architecture used to run processes related to storage, acccess and computation of data. Being full-featured, ERP5 could also be used for creation and hosting of analytics applications (internal or client facing) optionally also including business processes.

    SlapOS: Deployment

    The Wendelin stack is deployed and automatically managed via SlapOS, making it easy to scale the underlying cluster while also providing resiliency.

    Success Cases & Services

    Wendelin components are bank security compliant and have been deployed successfully in the financial sector. References include ERP5, SlapOS and NEO managing the West African monetary system or the first French crowd equity platform. Wendelin itself is currently being deployed in pilots related to the ongoing research project. Services provided by Nexedi cover data-collection, analysis and visualization as well as consulting on single topics or implementation of full-fledged Big Data applications on the Wendelin stack.

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