Peak Advanced Search Engine

Evaluate test data in the Big Data Cluster

The Peak Advanced Search Engine offers you the possibility to evaluate comprehensive amounts of test data in a HDFS/YARN-based Big Data Cluster considering complex criteria.

Overview
Features

Along with the Peak Advanced Search Engine, we also provide special connectors (= input formats) for different measurement data formats (e.g. ASAM ODS, MDF3, MDF4, ISOMME, etc.).

Data analysts can integrate the connectors into Apache Spark applications in order to “decode” the measurement data and to evaluate them through complex analyses. Apache Spark’s In-Memory technology ensures particularly quick data processing. Provided that an accordingly large Cluster of physical and virtual servers, petabytes of measurement data can easily be processed. Just like HDFS/YARN, the Spark Cluster's performance increases linearly with its size.

Using Java and Python, our predefined query algorithms can be integrated in existing applications (e.g. openMDM®) using a defined interface. As an alternative, we provide a web-based user interface.

The measurement data in the HDFS/YARN Cluster is either provided by extracting and transforming consisting databases or online through the Peak ODS Server.

The components of Peak Advanced Search Engine allow our users to achieve fast results when performing demanding measurement data analyses in a Big Data Cluster. The implementation is not protracted by the complex integration of different measurement data formats. You can immediately concentrate on gaining the maximum benefit from your pool of measurement data.

We would be pleased to inform you in more detail about how the Peak Advanced Search Engine can be used to evaluate your test data in a Big Data Cluster using complex criteria.

  • Input formats for ASAM ODS, MDF3, MDF4 and ISOMME (others on request) to create RDDs (= Resilient Distributed Dataset) in Spark applications
  • Extraction of measurement data from existing applications and transformation in the Big Data Cluster
  • Predefined query algorithms for the evaluation of measurement data considering complex criteria
  • High-performance evaluation of petabytes of measurement data with In-Memory technology and parallel processing in several nodes in the Big Data Cluster
  • Fast results during the implementation of big data applications when testing
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