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.

open source CODESTART CLOUD TRIAL

git clone https://github.com/FeatureBaseDB/featurebase-examples.git
cd featurebase-examples/docker-example

docker-compose -f docker-compose.yml up -d

# TIP: Disable Docker compose v2 if needed by going to settings..general in Docker Desktop.

git clone https://github.com/FeatureBaseDB/featurebase-examples.git
cd featurebase-examples/docker-example

docker-compose -f docker-compose.yml up -d

# TIP: Disable Docker compose v2 if needed by going to settings..general in Docker Desktop.