Semiotic Parsing Language (SPL)

SPL is a single-step solution to transform machine logs of any format and device into a machine data analytics app. Combining Glassbeam Studio and SPL, you can expect a 100x time-to- value compared to in-house data transformation projects.

Key Features

  • Define source data parsing models, target schema design, and generate metadata in a single step
  • Supports both streams and batch files processing or a combination of both
  • Yields all the metadata for building the user interface and applications
  • Access quick and easy functions to transform and manipulate the data prior to processing
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SCALAR – Platform for Machine data Analytics

Glassbeam’s SCALAR is a highly scalable, parallel, asynchronous, machine data analytics platform that converts any log format into actionable information.

Key Features

  • Parallel, asynchronous loader, compiler, and parser providing multiple levels of parallelization in the data pipeline
  • Supports streaming as well as batch data
  • Dynamically tunes itself to optimize the system for the slowest links in the chain, smoothing out load peaks and valleys while ensuring maximum resource usage
  • Scalable NoSQL data store for pre-defined analytics, robust SQL store for adhoc analytics, and an index store for performing searches
  • Inbuilt Kafka message bus based pipeline allowing external data sources to easily integrate Kafka-based consumers

Rules Engine – Event Processor for machine data

Glassbeam’s Rules Engine a highly scalable, In-line Event processor, which allows users to create complex rules on their incoming streams or batched data.

Key Features

  • Evaluates rules inline and in real time while parsing the data; not based on querying parsed data
  • Allows users to create unlimited number of rules without performance penalty
  • Setup complex rules on either stream or batch data based on your organization policies, customer usage patterns, errors, product defects, and historical configurations
  • Apply complex logic beyond what is supported by the user interface using backend domain specific language (DSL)
  • Ability to send alerts as email and also to invoke specific APIs

Machine Learning – Train and develop prescriptive models on machine data with ease

Glassbeam provides a set of inbuilt Apps and machine learning tool set through its integration with Apache Spark.

Key Features

  • Inbuilt apps to find anomalies, predict component failures, and prescribe machine failure remedies
  • Integrated machine data analytics platform with our machine learning tool kit to get a seamless framework for creating, training, and deploying machine learning models
  • SPL as a development language and Glassbeam Studio as a data transformation tool to handle the bulk of any machine learning (ML) projects (data cleansing, correlation, and ETL)
  • Complete data transformation platform from ingesting raw data to executing ML models