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.