Elastic, the company behind open source projects Elasticsearch, Logstash, and Kibana, has acquired Packetbeat, a real-time network packet analytics solution built natively on Elastic’s technology stack, as well as unveiled a new open source framework called Beats for anyone to build data shippers on top of Elasticsearch.
After spending a decade working in networking packet analytics, Packetbeat founders Monica Sarbu and Tudor Golubenco experienced first-hand the complexity of analyzing and troubleshooting wire data. Realizing that existing solutions were mostly proprietary and not able to extract real-time application-level data from network packets, they sought out to create a new solution based on Elasticsearch and Kibana. With a vision to help fellow IT and network operators tap into complex, distributed systems, Packetbeat was created as the first open source solution for network packet analytics to extract real-time insights from wire data.
As Elasticsearch has become a central place to search, store, and analyze data across many use cases, Beats is Elastic’s new open source platform for anyone to build their open source data shippers for Elasticsearch. Using the Beats framework, developers can create their own ‘Beats’ and easily output them to either Elasticsearch or Logstash, as well as visualize the results in Kibana. At the heart of every Beat is libbeat, a common library for forwarding host-based metrics to Elasticsearch and the building block to creating future Beats, such as: Filebeat, the next-generation Logstash Forwarder, infrastructure data Beats, application data Beats, operating system Beats, and virtualization metrics Beats. In addition, developers can add new network protocols to Packetbeat.
“We built Packetbeat to help users monitor and troubleshoot their distributed applications by extracting insights from their network packet data and analyzing them using Elasticsearch and Kibana,” said Monica Sarbu and Tudor Golubenco, creators of Packetbeat. “We are excited to join forces with Elastic to extend the use case beyond network packet analytics to many other data types.”
“From the first time I met the Packetbeat team, I fell in love with their vision to create forwarders for all types of data based on Elasticsearch and Logstash,” said Shay Banon, Elastic Founder and CTO. "Today I’m thrilled to introduce Beats, a new open source framework to build native lightweight shippers, and Packetbeat, our first Beat, focused on network data. Our mission is to help users understand their data, and Beats helps make a dent in unraveling all those pockets of unreachable data."
The Latest
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...