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ING Bank Śląski

Gigabytes of data arriving daily, terabytes of data processed in a dispersed Big Data ecosystem.

Key takeaways

  • Automatic loading of data arriving from external systems
  • Fully automated process of structuring loaded data
  • Easy-to-use tool for creating comprehensive data processing tasks within the cluster
  • Mechanisms and data management algorithms (aggregation, cleaning, structuring)

Profile

Industry: Banking
Location: Poland

Technologies

Apache Hadoop, Apache Hive, Apache Spark, HortonWorks Data Platform, Java 8, Python, Spotify Luigi

BlueSoft has been supporting us in the capacity of an IT Partner in the realm of Big Data solutions for several years.  Owing to BlueSoft’s highly competent consultants, our solutions have made it possible for us to offer our clients top-quality services.

Mariusz Narewski, Application Development Manager

Challenge

Every day, ING Bank Śląski generates tens of gigabytes of data dispersed across multiple systems.  Much of this data is increasing in value when it comes to the work of a variety of specialists – they are able to apply advanced algorithms in order to extract incredibly valuable information from seemingly unrelated data sets.

Therefore, ING Bank Śląski took it upon itself to create an environment allowing for unrestrained processing of immense data volumes, while providing them with high processing power for advanced transformations and operations.

Solution

BlueSoft prepared a set of tools supporting the process of receiving data from the bank’s systems in an efficient and comprehensive manner and placing it in a Big-Data-class system based on Hadoop.  Unstructured data received from external systems is transformed by imposing a certain structure on it, which allows users to freely access it without concern about the way and form in which it had been sent.

The operation of the entire process within the Hadoop cluster is managed intuitively by a tool allowing for combining previously prepared elementary data processing tasks into one coherent and uniformly parametered processing chain.

Result

The Big Data ecosystem delivered by BlueSoft significantly improved and expedited information aggregation and analysis tasks.  Nearly all data collected, which is later processed, is selected and structured, owing to which it takes on a much greater meaning for end users.

Additional introduction of processing-management-type mechanisms enabled the bank’s consultants to have better control and to analyze possible data irregularities faster.

steps during processing

GB+ of data daily

structured tables

million bank customers

Competence centers which participated in the project

Big Data

From data aquisition to business execution

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Challenge accepted

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