The punch scales out by design, from one to many (many) nodes.
Start small and simple but be ambitious where you need to go.
Run your mission critical applications.
No data loss, non-stop service, integrated monitoring.
Leverage standard log parsers, ready-to-use dashboards, machine learning by configuration.
Focus on your use cases, not on understanding technologies.
The punch team provides support, audit and sizing expertise, a migration task force.
Do not trap yourself to manage your open source zoo, it is hard !
Leverage state-of-the art open source technologies like Elasticsearch, Spark, Kafka, Storm, Siddhi and more.
Keep your data yours.
Use case implementation
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Big Data Paris 2019 Workshop Tuesday 12th of March, 14H00 Machine Learning for Critical Systems Damien Fontanes, Dimitri Tombroff Abstract In real-life critical systems and applications, accessing the data to test, tune and run artificial intelligence algorithms involved several under-estimated problems. The first challenge is mainly to provide consistent tools and execution engines usable for Read more about The Punch at Big Data Paris 2019[…]
Introduction The Elastic Common Schema (ECS) is a new normalized format proposed by the Elastic community. Although still in beta status, it is already usable, concrete and more importantly promising. The ECS idea is simple: benefit from a common specification to structure the data indexed in Elasticsearch. Such a data normalization makes it simple and Read more about Leveraging the Elastic Common Schema[…]
The Craig punchplatform version 5.1.2 is released. This post gives an overview of the new features. New Features The Craig punchplatform 5.1.2 has a number of important new features. First off, it leverages many important component updates: Elastic 6.5.4 stack Kibana 6.5.4 Spark 2.4 Siddhi Rules 4.3.17 It also brings in a number of new Read more about Craig Release 5.1.2 Annoucement[…]
Before diving into leveraging OmniSci, let us quickly recap the well-known database story. Relational databases have long been the standard for storing pre-processed data. Although they cannot keep up with today’s applications requirements in terms of raw performance and volumetry, they stay by far the most popular and the most widely used databases. Their low Read more about Introducing OmniSci[…]
Replay available: Related posts: – PySpark to PML
In the punch, we do not use logstash for high-performance logs parsing. Why is that? Mainly because logstash is nor easily scalable nor does it provide an end-to-end acknowledgment pattern. This is a serious lack because we cannot afford to lose logs whatever happens. We thus selected an alternative technology (apache storm) to run our equivalent input/filter/output processors, Read more about News From Logstash[…]