Scalable
The punch scales out by design, from one to many (many) nodes.
Start small and simple but be ambitious where you need to go.
RESILIENT
Run your mission critical applications.
No data loss, non-stop service, integrated monitoring.
Standard Resources
Leverage standard log parsers, ready-to-use dashboards, machine learning by configuration.
Focus on your use cases, not on understanding technologies.
PROFESSIONAL SERVICES
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 !
Open
Leverage state-of-the art open source technologies like Elasticsearch, Spark, Kafka, Storm, Siddhi and more.
Keep your data yours.
Use cases
Professional services
Platform sizing
Use case implementation
MIGRATION
Packagings
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Latest news
The Punch at Big Data Paris 2019
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[…]
Leveraging the Elastic Common Schema
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[…]
Craig Release 5.1.2 Annoucement
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[…]
Introducing OmniSci
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[…]
Meetup Machine Learning Rennes – Maritime Traffic Anomaly Detection
Replay available: Related posts: – PySpark to PML
News From Logstash
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[…]
