Factor House

Kafdrop: Review, pricing, and best alternatives in 2026

Kafdrop review for 2026: strengths, limitations, pricing, and the best alternatives for platform and data engineers running production Kafka clusters.

Kafka dashboard: the features that matter in production

A Kafka dashboard gives you real-time visibility into consumer lag, broker health, and partition state. Here's what to look for and how Kpow delivers it in production.

Kafka management console: what to look for in a tool

A Kafka management console gives your team full control of topics, consumers, schemas, and connectors from one UI. See what to look for and how Kpow delivers it.

Kafka message key best practices

A technical guide to Kafka message key best practices covering partitioning, ordering guarantees, hot keys, log compaction, and serialization for production systems.

Kafka security architecture: best practices for production

Kafka ships insecure by default. Learn how to build a production-ready Kafka security architecture covering TLS encryption, SASL authentication, ACLs, audit logging, and network isolation.

Dead letter queues in Kafka: patterns and pitfalls

How to implement a dead letter queue in Apache Kafka, with Spring Kafka, Connect, and Streams examples, and the production failure modes to avoid.

Best Kafka management tools for 2026

Compare the 10 best Kafka management tools for 2026, including Kpow, AKHQ, Conduktor, and Confluent Control Center. Covers pricing, RBAC, and deployment requirements.

Best Kafka monitoring tools for 2026

Compare 12 Kafka monitoring tools for 2026, from enterprise-grade Kpow to open-source AKHQ and Prometheus. Covers deployment, pricing, and key trade-offs.

Kafka broker monitoring

How to monitor Kafka brokers: key JMX metrics, alerting thresholds, process monitoring scripts, and common issues with step-by-step diagnosis.

Kafka cluster monitoring

What to monitor at the Kafka cluster level: key JMX metrics, multi-broker collection, alerting thresholds, capacity signals, and a health check script.

Kafka consumer monitoring and performance tuning

Learn which Kafka consumer metrics matter most, how to interpret them, and which configuration changes will improve performance and reduce lag.

Kafka monitoring: a complete guide for platform engineers

A practical guide to Kafka monitoring for platform engineers: the metrics that matter, alert thresholds, JVM tuning, consumer lag, and KRaft changes.

A detailed guide to Kafka producer monitoring

A practical guide to Kafka producer metrics, JMX collection, alerting thresholds, and diagnostic scripts for Java-based Kafka producers.

How Adidas uses Apache Kafka in production

A deep-dive into Adidas's Kafka architecture — covering observability at 100 billion messages per day, self-service topic provisioning, and custom GoLang tooling.

How Afterpay uses Apache Kafka in production

A deep-dive into Afterpay's Kafka architecture — covering PCI-compliant payment streaming, two-hop AWS PrivateLink isolation, and the Project Teleport cross-region data pipeline.

Apache Kafka 4.3.0: A guide for platform engineers

Kafka 4.3.0 covers broker cordoning, partition size metrics, share group tuning, and tiered storage fixes. Here's what platform engineers need to act on.

How Apple uses Apache Kafka in production

A deep-dive into Apple's Kafka architecture — covering their managed internal platform, Strimzi on EKS, tiered storage, zero-data-movement balancing, and mTLS migration.

How Barclays uses Apache Kafka in production

A deep-dive into Barclays' Kafka architecture — covering dual-environment deployment on AWS and IBM Z-Linux, operating practices, and the broader streaming stack.

How Cash App uses Apache Kafka in production

A deep-dive into Cash App's Kafka architecture — covering the evently-cloud bridge service, per-topic application-layer encryption, and the internal PubSub platform at Block.

How Datadog uses Apache Kafka in production

A deep-dive into Datadog's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors across hundreds of clusters.

Apache Kafka architecture: a complete guide to internals, components, and deployment

A complete guide to Apache Kafka architecture: internals, components, KRaft, replication, consumers, Connect, Streams, and deployment options.

How Netflix uses Apache Kafka in production

A deep-dive into Netflix's Kafka architecture — covering the Keystone pipeline, Data Mesh platform, scale figures from 700 billion to 2 trillion events per day, and the engineering decisions behind it.

How New Relic uses Apache Kafka in production

A deep-dive into New Relic's Kafka architecture — covering use cases, scale, engineering decisions and key contributors.

How Notion uses Apache Kafka in production

A deep-dive into Notion's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors across their data lake and AI pipelines.

How PagerDuty uses Apache Kafka in production

A deep-dive into PagerDuty's Kafka architecture, covering event ingestion, notification scheduling, task execution, and the engineering decisions behind each.

How Pinterest uses Apache Kafka in production

A deep-dive into Pinterest's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors. From 15 million to 40 million messages per second across 3,000 brokers.

How Salesforce uses Apache Kafka in production

A deep-dive into Salesforce's Kafka architecture — covering use cases, scale, engineering decisions and key contributors across a fleet of 100+ clusters processing 3+ trillion events per day.

How Shopify uses Apache Kafka in production

A deep-dive into Shopify's Kafka architecture — covering CDC at 100,000 records/sec, Kubernetes deployment, the Sarama Go client library, and BFCM scale engineering.

How Tencent uses Apache Kafka in production

A deep-dive into Tencent's Kafka architecture — covering their federated cluster design, 20 trillion messages per day, KIP contributions, and tiered storage at Tencent Cloud.

How Wix uses Apache Kafka in production

A deep-dive into Wix's Kafka architecture: 66 billion daily messages, 2,200+ microservices, the Greyhound SDK, Confluent Cloud migration, and operating 500,000+ partitions across 4 regions.

Conduktor: Review, pricing, and best alternatives in 2026

Conduktor review for 2026: pricing, strengths, deployment trade-offs, and how it compares to alternatives for enterprise Kafka governance teams.

How Airbnb uses Apache Kafka in production

A deep-dive into Airbnb's Kafka architecture — covering six production systems, 35+ billion daily events, SpinalTap CDC, Flink-based personalisation, and Kafka as a write-ahead log.

How Bytedance uses Apache Kafka in production

How Cloudflare uses Apache Kafka in production

A deep-dive into Cloudflare's Kafka architecture: use cases at trillion-message scale, 14 clusters, internal tooling decisions, and the engineering lessons behind a decade of Kafka operations.

How DoorDash uses Apache Kafka in production

A deep-dive into DoorDash's Kafka architecture — covering the Iguazu event platform, Flink-based ML feature pipelines, self-serve topic governance, and the engineering decisions behind hundreds of billions of daily events.

How Goldman Sachs uses Apache Kafka in production

A deep-dive into Goldman Sachs's Kafka architecture — covering use cases across three divisions, migration to Amazon MSK, resilience design, and key engineering decisions.

How Grab uses Apache Kafka in production

A deep-dive into Grab's Kafka architecture — how the Coban team built a terabyte-per-hour streaming platform serving 300 billion events a week across GrabFood, GrabPay, mobility, and more.

How JPMorgan uses Apache Kafka in production

A deep-dive into JPMorgan Chase's Kafka architecture — covering multi-tenant cluster design, managed Kafka Connect, the Photon Framework, and the engineering decisions behind one of the largest financial services deployments.

How LinkedIn uses Apache Kafka in production

A deep-dive into LinkedIn's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors.

How PayPal uses Apache Kafka in production

A deep-dive into PayPal's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors across a fleet handling 1.3 trillion messages per day.

How Reddit uses Apache Kafka in production

A deep-dive into Reddit's Kafka architecture — covering use cases, scale, engineering decisions and key contributors.

How Robinhood uses Apache Kafka in production

A deep-dive into Robinhood's Kafka architecture: use cases, scale, engineering decisions, and key contributors. Learn how Robinhood processes 2.2 million messages per second across equities trading, crypto, fraud detection, and more.

How Spotify used Apache Kafka in production

A deep-dive into Spotify's Kafka architecture — covering their event delivery system, 700K events/second scale, engineering decisions, and why they ultimately migrated to Google Cloud Pub/Sub.

How The New York Times uses Apache Kafka in production

A deep-dive into The New York Times' Kafka publishing pipeline — covering the Monolog architecture, single-partition design, Kafka Streams usage, and the engineering decisions behind treating Kafka as a permanent content store.

How Uber uses Apache Kafka in production

A deep-dive into Uber's Kafka architecture - covering use cases, scale, engineering decisions, and key contributors. From one region to trillions of messages a day.

How Walmart uses Apache Kafka in production

A deep-dive into Walmart's Kafka architecture — covering real-time inventory, fraud detection, the Customer Data Platform, and the Messaging Proxy Service handling trillions of messages per day.

Accelerating incident response: advanced filters, streaming search, and AI-powered queries

Fix streaming data failures faster. Learn how Kpow uses advanced kJQ filtering, BYO AI, and Streaming Search to slash incident response times.

Kafka Data Management with Kpow: Unlocking Engineering Productivity

Enterprise Kafka adoption promises massive scalability and decoupled agility. However, interacting with complex streaming data at scale often bogs developers down in manual operational friction. By identifying four critical friction points across visibility, velocity, remediation, and compliance, this article introduces a comprehensive data management strategy to eliminate bottlenecks and unlock engineering productivity with Kpow.

Kafka scaling best practices: An in-depth primer

A practical guide to scaling Apache Kafka in production, covering partitioning strategy, consumer group design, broker sizing, KRaft migration, and more.

AKHQ: Review, pricing, and best alternatives in 2026

AKHQ review for 2026: features, known limitations, pricing, and the best alternatives for teams that need more than open-source tooling.

CMAK: Review, pricing, and best alternatives in 2026

CMAK is a free, open-source Kafka admin tool from Yahoo. This review covers features, KRaft limitations, security gaps, and the best alternatives for 2026.

Confluent Control Center: Review, pricing, and best alternatives in 2026

An honest technical review of Confluent Control Center in 2026, covering features, deployment, pricing, and the best alternatives for Kafka teams.

Kadeck: Review, pricing, and best alternatives in 2026

Kadeck review for 2026: features, deployment, pricing, and how it compares to AKHQ, Kafbat, Conduktor, and Kpow for Kafka management teams.

Kafbat UI: Review, pricing, and best alternatives in 2026

A practical review of Kafbat, the open-source kafka-ui fork — covering features, deployment, security, pricing, and best alternatives in 2026.

Lenses.io: Review, pricing, and best alternatives in 2026

Lenses.io review for 2026: honest assessment of SQL Studio, deployment complexity, pricing, and when to consider alternatives like Conduktor or Kpow.

Redpanda Console: Review, pricing, and best alternatives in 2026

Redpanda Console reviewed for 2026: features, pricing, limitations, and the best alternatives for engineering teams running Apache Kafka or Redpanda.

Top Kafka UI Tools in 2026: A Practical Comparison for Engineering Teams

Honest comparison of Kafka UI tools for enterprise teams. We evaluate AKHQ, Kafbat, Redpanda Console, Conduktor, Confluent Control Center, and Kpow.

Best practices for Kafka data observability

12 best practices for Kafka data observability covering consumer lag monitoring, schema enforcement, end-to-end auditing, DLQs, and lineage, with an implementation roadmap.

Kafka message size best practice

How large should Kafka messages be in production? Covers sizing tiers, the four-config chain, compression codecs, and patterns for handling payloads above 1 MB.

Kafka partition key best practices

How Kafka partition keys work, what makes a good key, and practical guidance on cardinality, hot partitions, compaction, cross-language hashing, and safe key migration.

Kafka cluster management: A practical guide for engineers

A practical guide to Kafka cluster management: architecture sizing, day-to-day operations, performance tuning, KRaft migration, and monitoring for production clusters.

Kafka topic partition best practices

Size Kafka topic partitions correctly from day one. Covers the throughput formula, the keyed topic asymmetry, KRaft-era limits, and operational best practices.

The Complete Guide to Kafka Change Data Capture (CDC)

Learn how to implement change data capture with Kafka using Debezium. Includes working PostgreSQL CDC examples, architecture patterns, and monitoring.

Enhanced Under-Replicated Partition Detection in Kpow

Kpow now offers enhanced under-replicated partition (URP) detection for more accurate Kafka health monitoring. Our improved calculation correctly identifies URPs even when brokers are offline, providing a true, real-time view of your cluster's fault tolerance. This helps you proactively mitigate risks and ensure data durability.

Introduction to Factor House Local

Jumpstart your journey into modern data engineering with Factor House Local. Explore pre-configured Docker environments for Kafka, Flink, Spark, and Iceberg, enhanced with enterprise-grade tools like Kpow and Flex. Our hands-on labs guide you step-by-step, from building your first Kafka client to creating a complete data lakehouse and real-time analytics system. It's the fastest way to learn, prototype, and build sophisticated data platforms.

KIP-1150 Diskless Topics: Rethinking Storage and Cloud Costs in Kafka

Discover how Kafka's KIP-1150 Diskless Topics aim to bring cloud-native scalability and cost-efficiency by natively utilizing object storage, and what it means for your streaming architecture.

Improvements to Data Inspect in Kpow 94.3

Kpow's 94.3 release is here, transforming how you work with Kafka. Instantly query topics using plain English with our new AI-powered filtering, automatically decode any message format without manual setup, and leverage powerful new enhancements to our kJQ language. This update makes inspecting Kafka data more intuitive and powerful than ever before.

Introducing Factor House Docs

We're excited to launch the new Factor House Docs, a unified hub for all our product documentation. Discover key improvements like a completely new task-based structure, interactive kJQ examples, and powerful search, all designed to help you find the information you need, faster than ever. Explore the new home for all things Kpow, Flex, Factor Platform and more.

Data Inspect Enhancements in Kpow 94.5

Kpow 94.5 enhances data inspection with comma-separated kJQ Projection expressions, in-browser search, and flexible deserialization options. This release also adds high-performance streaming for large datasets and expands kJQ with new transforms and functions—testable on our new interactive examples page. These updates provide deeper insights and more granular control over your Kafka data streams.

Releasing Software at Factor House: Our Java Compatibility and Evolution Strategy

At Factor House, delivering reliable software is at the heart of everything we do. A key aspect of this commitment lies in our approach to managing Java compatibility. This blog post outlines our current release process and future plans for evolving Java support, including our approach to deprecating older versions in a way that respects the needs of diverse customer bases.

A final goodbye to OperatrIO

2025 is a pivotal moment at Factor House (formally Operatr.IO). We've announced our fundraise and have much more to announce about our roadmap this year. This is why we think that now is the perfect time to do a bit of spring cleaning and retire the io.operatr artifacts for good.

Ensuring Your Data Streaming Stack Is Ready for the EU Data Act

The EU Data Act takes effect in September 2025, introducing major implications for teams running Kafka. This article explores what the Act means for data streaming engineers, and how Kpow can help ensure compliance — from user data access to audit logging and secure interoperability.

Beyond Kafka: Sharp Signals from Current London 2025

The real-time ecosystem has outgrown Kafka alone. At Current London 2025, the transition from Kafka Summit was more than a name change — it marked a shift toward streaming-first AI, system-level control, and production-ready Flink. Here's what Factor House saw and learned on the ground.

Introducing Factor House 2.0 🚀

Today we introduce Flex for Apache Flink, and announce the Factor Platform, the future of distributed systems engineering.

Factor House Product VPAT

We first published a VPAT in the release notes of Kpow for Apache Kafka v92.4, with a VPAT available to download in every release of Kpow since. Today, we are pleased to announce that we are extending that commitment to all future Factor House product releases - including Flex for Apache Flink and the Factor Platform.

Introducing Kpow's new API

With our new API, you can now leverage Kpow's capabilities directly from your own tools and platforms, opening up a whole new range of possibilities for integrating Kpow into your existing workflows. Whether you're managing topics, consumer groups, or monitoring Kafka clusters, our API provides a seamless experience that mirrors the functionality of our user interface.

Updates to container specifics (DockerHub and Helm Charts)

Discover how our 94.1 release has streamlined DockerHub, Helm Charts, and AWS Marketplace deployments!