Systems | Development | Analytics | API | Testing

August 2024

Apna Unlocks AI Job Matching for 50 Million Users With Confluent & Onehouse

Since its beginnings just five years ago, Apna has become the leading jobs site for tens of millions of workers in India, the largest labor market in the world. Today, Apna has more than 50 million registered users, resulting in more than 5 million interviews and 100,000 jobs activated per month.

Unlock Real-Time Value from DynamoDB Data with Confluent's CDC Source Connector

Over the years, Amazon DynamoDB has grown into a feature-rich NoSQL database that has deep integrations with various services such as Amazon S3 and AWS Lambda. As businesses increasingly depend on data for decision-making, it is common to use data residing in DynamoDB to contextualize or even drive events at a granular level (as opposed to bulk or batch).

How to source data from AWS DynamoDB to Confluent using the DynamoDB CDC Source Connector

This is a one-minute video showing an animated architectural diagram of the integration between Amazon DynamoDB and Confluent Cloud using the all new, fully managed DynamoDB CDC Source connector. This real-time data pipeline doesn’t require you to write or maintain code.

Watermark Alignment Explained in 2 Minutes | Apache Flink in Action

Watermark alignment is a relatively new feature in Apache Flink. It lets you cope with the problem of needing to temporally join streams with mismatched event frequencies, e.g., one stream’s updates are much more frequent than those of the stream(s) with which you need to join it. In this video we’ll break the feature down, and relate how it can help you better manage your Apache Flink integration.

Spring Into Confluent Cloud With Kotlin-Part 1: Producers and Consumers

Hey, you! Yeah, you! The puzzled-looking Spring Boot developer, scouring the web for a guide on integrating your microservices with Apache Kafka on Confluent Cloud with Stream Governance. Admit it, you’ve been Googling nonstop for the past hour and all you’ve found are examples using StringSerializer/StringDeserializer with not even the slightest mention of "schema registry-aware" serialization methods. And I bet the examples you found are implemented in Java.

5 Years of Confluent Cloud Connectors: Exploring Your Top Connector Picks

This summer marks five years since we announced our first fully managed connector on Confluent Cloud in 2019, the Amazon S3 Sink Connector. Since then, our connector offerings have not only expanded significantly but also enabled teams to send hundreds of petabytes of data throughput. Today, we support over 80 pre-built, fully managed connectors, custom connectors, and secure private networking.

Build Scalable AI-Enabled Applications with Confluent and AWS

In this video, Confluent and AWS address enterprises' challenges in deploying generative AI and how Confluent Cloud and Amazon Bedrock empower organizations to build scalable, AI-enabled applications. We'll explore how Confluent's comprehensive data streaming platform enables you to stream, connect, and govern data at scale, creating real-time, contextualized, and trustworthy applications that differentiate generative AI.

Introducing the New Confluent Cloud Homepage UI: Enhancing User Experience

At Confluent, we are committed to enhancing our product to meet the evolving needs of our customers. In the last year we’ve seen substantial increases in both UI users and UI usage. With such high growth, we’ve been looking for ways to improve experiences where we can. Based on internal and external validation, the Confluent Cloud Homepage was specifically identified as an area that could be improved.

Streaming BigQuery Data Into Confluent in Real Time: A Continuous Query Approach

Confluent Cloud, a leading cloud-native platform for building data streaming applications, and BigQuery, Google's serverless data warehouse, are revolutionizing how businesses handle data. Together, they offer a powerful solution for real-time data ingestion, processing, and analysis—now enhanced by BigQuery’s new continuous query. Many organizations grapple with challenges in moving data from their data warehouses to real-time processing platforms.

Exploring Apache Flink 1.20: Features, Improvements, and More

The Apache Flink community released Apache Flink 1.20 this week. In this blog post, we'll highlight some of the most interesting additions and improvements. You’ll find a comprehensive rundown of all of the updates in the official release announcement. Recent Flink releases have emphasized improving Flink’s usability, not only for developers, but also for those operating Flink clusters, and this theme continues in this latest release.

How BT Group Built a Smart Event Mesh with Confluent

BT Group's Smart Event Mesh: Centralized Event Streaming With Decentralized Customer Experience, Automation, and a Foundation Built on Confluent. BT Group is a British multinational telecommunications holding company headquartered in London, England. It has operations in around 180 countries and is one of the largest telecommunications companies in the world, providing a range of products and services including fixed-line, broadband, mobile, and TV.

How to Set Idle Timeouts | Apache Flink in Action

This video covers setting an idle timeout on a watermark generator when joining data in Apache Flink. This can be used when you have two streams, one that has frequent updates, and one that has infrequent updates, and you need to join data without waiting for a fresh watermark from the infrequent one.