Streaming real-time data at scale and processing it efficiently is critical to cybersecurity organizations like SecurityScorecard. Jared Smith, Senior Director of Threat Intelligence, and Brandon Brown, Senior Staff Software Engineer, Data Platform at SecurityScorecard, discuss their journey from using RabbitMQ to open-source Apache Kafka® for stream processing. As well as why turning to fully-managed Kafka on Confluent Cloud is the right choice for building real-time data pipelines at scale.
SecurityScorecard mines data from dozens of digital sources to discover security risks and flaws with the potential to expose their client’ data. This includes scanning and ingesting data from a large number of ports to identify suspicious IP addresses, exposed servers, out-of-date endpoints, malware-infected devices, and other potential cyber threats for more than 12 million companies worldwide.
To allow real-time stream processing for the organization, the team moved away from using RabbitMQ to open-source Kafka for processing a massive amount of data in a matter of milliseconds, instead of weeks or months. This makes the detection of a website’s security posture risk happen quickly for constantly evolving security threats. The team relied on batch pipelines to push data to and from Amazon S3 as well as expensive REST API based communication carrying data between systems. They also spent significant time and resources on open-source Kafka upgrades on Amazon MSK.
Self-maintaining the Kafka infrastructure increased operational overhead with escalating costs. In order to scale faster, govern data better, and ultimately lower the total cost of ownership (TOC), Brandon, lead of the organization’s Pipeline team, pivoted towards a fully-managed, cloud-native approach for more scalable streaming data pipelines, and for the development of a new Automatic Vendor Detection (AVD) product.
Jared and Brandon continue to leverage the Cloud for use cases including using PostgreSQL and pushing data to downstream systems using CSC connectors, increasing data governance and security for streaming scalability, and more.
EPISODE LINKS
Confluent Platform 7.0: New Features + Updates
Real-Time Stream Processing with Kafka Streams ft. Bill Bejeck
Automating Infrastructure as Code with Apache Kafka and Confluent ft. Rosemary Wang
Getting Started with Spring for Apache Kafka ft. Viktor Gamov
Powering Event-Driven Architectures on Microsoft Azure with Confluent
Automating DevOps for Apache Kafka and Confluent ft. Pere Urbón-Bayes
Intro to Kafka Connect: Core Components and Architecture ft. Robin Moffatt
Designing a Cluster Rollout Management System for Apache Kafka ft. Twesha Modi
Apache Kafka 3.0 - Improving KRaft and an Overview of New Features
How to Build a Strong Developer Community with Global Engagement ft. Robin Moffatt and Ale Murray
What Is Data Mesh, and How Does it Work? ft. Zhamak Dehghani
Multi-Cluster Apache Kafka with Cluster Linking ft. Nikhil Bhatia
Using Apache Kafka and ksqlDB for Data Replication at Bolt
Placing Apache Kafka at the Heart of a Data Revolution at Saxo Bank
Advanced Stream Processing with ksqlDB ft. Michael Drogalis
Minimizing Software Speciation with ksqlDB and Kafka Streams ft. Mitch Seymour
Collecting Data with a Custom SIEM System Built on Apache Kafka and Kafka Connect ft. Vitalii Rudenskyi
Consistent, Complete Distributed Stream Processing ft. Guozhang Wang
Powering Real-Time Analytics with Apache Kafka and Rockset
Automated Event-Driven Architectures and Microservices with Apache Kafka and SmartBear
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