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Redpanda vs Kafka vs Confluent: An Honest Comparison

Jul 10, 2026 By Bijoy Choudhury In Confluent
Data streaming has moved from a niche pattern used by a handful of internet-scale companies to the default backbone for event-driven architectures, real-time analytics, and now AI pipelines. What started as log aggregation at LinkedIn has become the plumbing for fraud detection, IoT telemetry, microservices communication, and retrieval-augmented generation. Three names dominate that conversation today.
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Tableflow: Turn Kafka Topics into Iceberg Tables

Jul 10, 2026 By Mohtasham Sayeed Mohiuddin In Confluent
TL;DR: Tableflow is a Confluent Cloud feature that materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables, eliminating custom data pipelines by automatically handling schematization, type conversions, schema evolution, CDC stream materialization, catalog publishing, and table maintenance.
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Thousands of Migrations, Zero Data Loss: OCBC's Confluent Playbook | Life Is But A Stream

Jul 9, 2026 By Confluent In Confluent
Migrating an entire bank's infrastructure without losing a single transaction sounds impossible. Yet, OCBC successfully modernized their event-driven architecture across Singapore, Malaysia, and Hong Kong with zero data loss. In this episode, George Goh (Executive Director at OCBC) joins Joseph Morais and Sami Amed (Staff Solutions Engineer at Confluent) to pull back the curtain on a massive data streaming migration, how the team executed a flawless migration of thousands of producers and consumers in a single weekend.
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Stream Governance: Making Compliance a Property of Data in Motion

Jun 29, 2026 By Saurabh Ghelani In Confluent
As organizations have transitioned from batch processing to real-time streaming architectures, a critical governance gap has emerged. Legacy data governance tools designed for databases, warehouses, and file systems assume that information is stationary and focus on protecting, classifying, and auditing data at rest.
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Building Secure, Resilient, and Compliant Fraud Detection With Confluent Cloud

Jun 29, 2026 By Saurabh Ghelani In Confluent
Banking customers expect financial transactions to be completed quickly. Fraud analysis must execute in milliseconds, so traditional batch processing systems are inherently too slow. To safeguard transactions, institutions must shift to proactive, in-flight prevention. Confluent enables this shift by using Apache Kafka and Apache Flink to continuously correlate transactional and behavioral signals, blocking malicious activity before a transaction settles.
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Demo: Real-Time Context Engine for Fleet Management

Jun 25, 2026 By Confluent In Confluent
Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.
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How to Eliminate Training-Serving Skew With a Unified Real-Time Streaming ML Pipeline (2026 Guide)

Jun 23, 2026 By Manveer Chawla In Confluent
The problem. Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost. The recommendation. Adopt a unified streaming (kappa) architecture.
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Real-Time Hyper-Personalization in 2026: Architecture Guide

Jun 23, 2026 By Manveer Chawla In Confluent
Hyper-personalization in 2026 is the ability to act on a user's current intent within the current session, using signals from across the journey. Batch customer data platforms (CDPs) can't do this. They can't capture intent as it forms, can't hold session state, and can't activate inside the intent window.
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Meeting Data (and Analytics) Engineers Where They Are: Introducing the dbt Adapter for Confluent Cloud

Jun 18, 2026 By Zander Matheson In Confluent
dbt is the most commonly used tool by data engineers to define SQL transformations (as models), write tests, generate documentation, and deploy through CI/CD and now it’s available with Confluent Cloud too! The magic of dbt is that it brings the engineering rigor to modern data work and data engineering, regardless of the underlying compute source - Snowflake, BigQuery, Databricks, Redshift or Confluent. You can find out more about the launch in our Q2 Confluent Cloud Launch post and the keynote.
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