For Data Engineers

How to Achieve 1M+ Record/Second Kafka Ingest without Sacrificing Query Latency

As our world moves towards being “always-on,” the ability to make decisions and predictions on streaming data in real-time has become mission-critical. Apache Kafka has paved the way for organizations to capitalize on the power of streaming data, but it needs supporting technology to enable real-time analytics.

Watch this webinar to learn how FeatureBase and Kafka work together to achieve high throughput and low latency without sacrificing data freshness. Here’s what we’ll cover:

  1. How to ingest >1M records per second without sacrificing query latency
  2. How to rapidly update billions of records with real-time updates and inserts
  3. Learn to do automatic schema updates without manual changes or cutover downtime

**Note: This webinar was recorded before we rebranded from Molecula to FeatureBase!

Get Started for Free

Open Source install commands are included below. Requires git and Docker
open source CODESTART CLOUD TRIAL

docker run -p 10101:10101 featurebasedb/featurebase

git clone https://github.com/FeatureBaseDB/featurebase-examples.git
cd featurebase-examples/docker-simple
docker network create fbnet
docker compose up