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includes scanning and ingesting data from a large number of ports to identify suspicious IP addresses, exposed servers, out-of-date endpoints, malware-infected devices, and other potential cyber threats for more than 12 million companies worldwide.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
Building Real-Time Data Governance at Scale with Apache Kafka ft. Tushar Thole
Handling 2 Million Apache Kafka Messages Per Second at Honeycomb
Why Data Mesh? ft. Ben Stopford
Serverless Stream Processing with Apache Kafka ft. Bill Bejeck
The Evolution of Apache Kafka: From In-House Infrastructure to Managed Cloud Service ft. Jay Kreps
What’s Next for the Streaming Audio Podcast ft. Kris Jenkins
On to the Next Chapter ft. Tim Berglund
Intro to Event Sourcing with Apache Kafka ft. Anna McDonald
Expanding Apache Kafka Multi-Tenancy for Cloud-Native Systems ft. Anna Povzner and Anastasia Vela
Apache Kafka 3.1 - Overview of Latest Features, Updates, and KIPs
Optimizing Cloud-Native Apache Kafka Performance ft. Alok Nikhil and Adithya Chandra
From Batch to Real-Time: Tips for Streaming Data Pipelines with Apache Kafka ft. Danica Fine
Real-Time Change Data Capture and Data Integration with Apache Kafka and Qlik
Modernizing Banking Architectures with Apache Kafka ft. Fotios Filacouris
Running Hundreds of Stream Processing Applications with Apache Kafka at Wise
Lessons Learned From Designing Serverless Apache Kafka ft. Prachetaa Raghavan
Using Apache Kafka as Cloud-Native Data System ft. Gwen Shapira
ksqlDB Fundamentals: How Apache Kafka, SQL, and ksqlDB Work Together ft. Simon Aubury
Explaining Stream Processing and Apache Kafka ft. Eugene Meidinger
Handling Message Errors and Dead Letter Queues in Apache Kafka ft. Jason Bell
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