Unified Streaming Powers Real-Time, AI-Ready Apps
Confluent CEO Jay Kreps shares how unifying batch and streaming workflows lets teams build, test, and deploy AI-ready data pipelines–in real time.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
AI Fast-Tracks Financing
AI and real-time data automate credit checks, speed mortgage approvals.
Stream Data Anywhere, Anytime
Confluent’s Addison Huddy breaks down hybrid cloud and on-prem streaming with a bold metaphor and platform overview.
The Easiest Way to Power Real-Time AI: Confluent Announces Delta Lake Support & Unity Catalog Integration for Tableflow
In the age of AI, the hunger for fresh, reliable data to power machine learning (ML) models and real-time analytics is insatiable. Yet, organizations frequently hit roadblocks when trying to bridge their operational data in motion, typically flowing through Apache Kafka, with their data at rest in data lakehouses. On one side, you have the data streaming platform, the central nervous system managing the real-time flow of business events.
The True Cost of DIY Data Streaming
Ethan Chan, CEO of Allium, reveals the hidden costs of self-managing data streaming and why Confluent Cloud saved him and his team from late-night pager duty.#datastreaming.
Allium's Blueprint for Scaling Blockchain Data with Data Streaming | Life Is But A Stream Podcast
Blockchain may be decentralized, but reliable access to its data is anything but simple. In this episode, Ethan Chan, Co-Founder & CEO of Allium, shares how his team transforms blockchain firehoses into clean, queryable, real-time data feeds. From the pitfalls of hosting your own data streaming infrastructure to the business advantages of Confluent Cloud, Ethan reveals the strategic decisions that helped Allium scale from 3 to nearly 100 blockchains, without burning out their engineering team.
Unlocking Real-Time Analytics With Confluent Tableflow, Apache Iceberg, and Snowflake
Users of Snowflake and other data lakes and data warehouses need real-time data for artificial intelligence (AI) and analytical workloads—but they struggle to get that data into their lakes and warehouses. In response to this ubiquitous challenge, Confluent developed Tableflow.
Introducing KIP-848: The Next Generation of the Consumer Rebalance Protocol
The consumer group is a cornerstone of Apache Kafka, enabling scalable and fault-tolerant data consumption by allowing multiple consumer instances to share the workload of reading from topic partitions. The consumer rebalance protocol is the mechanism that coordinates which partitions are assigned to which consumers within a group.
Why Data Integration is Key for M&A Success
Merging data systems post-acquisition? Joe Pardi, Senior Director of Global Data Engineering at Covetrus, explains how real-time data streaming architecture makes 1+1=3.&A.
How to Query Apache Kafka Topics With Natural Language
Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.