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A tartalmat a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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Security for Real-Time Data Stream Processing with Confluent Cloud

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Manage episode 346008718 series 2355972
A tartalmat a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

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includes scanning and ingesting data from a large number of ports to identify suspicious IP addresses, exposed servers, out-of-date endpoints, malware-infected devices, and other potential cyber threats for more than 12 million companies worldwide.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.
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265 epizódok

Artwork
iconMegosztás
 
Manage episode 346008718 series 2355972
A tartalmat a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

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

  continue reading

265 epizódok

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