Why LinkedIn Moved from Kafka to Northguard: A Deep Dive into the Next Evolution of Data Streaming

When you think about real-time data streaming, one name almost always comes up: Apache Kafka. And when you think about Kafka at massive scale, another name follows right behind it: LinkedIn.

After all, LinkedIn originally developed Kafka before open-sourcing it. For years, Kafka has powered LinkedIn’s messaging backbone, enabling billions of events per day across feeds, messaging, notifications, ads, analytics, and more.

So when news emerged that LinkedIn moved from Kafka to Northguard for certain workloads, it sparked intense interest in the engineering community.

Why would the company that created Kafka shift to a new streaming system?
What limitations did they face?
And what does Northguard bring to the table?

In this 15-minute deep dive, we’ll explore the motivation, architecture evolution, technical challenges, and strategic implications behind LinkedIn’s migration from Kafka to Northguard.

LinkedIn and Kafka: A Historical Connection

Before diving into Northguard, it’s important to understand the context.

Kafka was created at LinkedIn in 2011 to handle:

• Activity stream ingestion
• Log aggregation
• Real-time data pipelines
• Scalable messaging between services

It quickly became central to LinkedIn’s microservices architecture. Kafka’s publish-subscribe model allowed producers and consumers to operate independently while maintaining high throughput and durability.

Eventually, Kafka was donated to the Apache Software Foundation and became one of the most widely adopted distributed streaming platforms in the world.

At LinkedIn scale, Kafka evolved into a mission-critical backbone supporting:

• Trillions of messages per day
• Multi-datacenter replication
• Strict latency and reliability SLAs
• Global-scale traffic bursts

But as systems evolve, so do requirements.

Why Kafka Was No Longer Enough (For Certain Use Cases)

Let’s be clear: LinkedIn did not “abandon” Kafka entirely. Instead, they identified limitations in specific high-scale, low-latency workloads that required architectural innovation.

Here are the core challenges that pushed LinkedIn toward Northguard:

1. Operational Complexity at Extreme Scale

Running Kafka at global scale introduces:

• Massive cluster management overhead
• Broker balancing challenges
• Storage tuning and retention optimization
• Cross-region replication constraints

As infrastructure grows, so does the complexity of operating it.

Even with automation, operating large Kafka clusters demands significant engineering investment.

2. Latency Sensitivity for Real-Time Features

LinkedIn increasingly relies on:

• Real-time feed ranking
• Instant notifications
• Fraud detection systems
• AI-driven personalization

Some of these workloads require ultra-low latency and predictable performance under heavy load.

Kafka is high-throughput, but at extreme scale, certain workloads experienced tail-latency spikes that were unacceptable for real-time personalization systems.

3. Cost and Resource Efficiency

Kafka’s architecture relies heavily on:

• Disk-based persistence
• Broker replication
• Partition management

While durable and robust, this can become expensive in:

• Storage footprint
• Network replication
• Hardware provisioning

LinkedIn engineers needed a more optimized system for specific data flow patterns, especially those that didn’t require long retention windows.

4. Architectural Evolution Toward Specialized Systems

Modern large-scale infrastructure often shifts from general-purpose systems to specialized internal platforms.

Instead of stretching Kafka to fit every scenario, LinkedIn chose to build or adopt a system optimized for:

• Certain event processing patterns
• Internal routing requirements
• Better integration with newer infrastructure layers

That system became Northguard.

What Is Northguard?

Northguard is LinkedIn’s internal next-generation streaming infrastructure designed to address limitations observed at extreme Kafka scale.

While detailed internal documentation isn’t fully public, engineering insights reveal that Northguard focuses on:

• Improved scalability
• Lower operational overhead
• Optimized data routing
• Reduced infrastructure cost
• Better real-time guarantees

Think of it not as a Kafka replacement across the board, but as an evolution tailored to LinkedIn’s internal ecosystem.

Architectural Differences: Kafka vs Northguard

Here’s a conceptual comparison of how the systems differ:

Kafka Architecture

• Distributed log-based messaging
• Partitioned topics
• Broker-based storage
• Strong durability guarantees
• Consumer pull model

Strength: High throughput and reliability
Tradeoff: Operational complexity and disk-heavy design

Northguard Architecture (Conceptual)

• More lightweight routing model
• Optimized memory and storage usage
• Possibly more decoupled metadata management
• Designed around LinkedIn-specific traffic patterns

Strength: Lower latency and cost efficiency
Tradeoff: Specialized to LinkedIn use cases

Why Companies Build Internal Alternatives to Their Own Open-Source Projects

This may seem ironic at first.

But this is actually common in large tech companies:

• Google moved beyond MapReduce to newer internal systems.
• Meta built internal messaging systems beyond open-source equivalents.
• Amazon often uses proprietary evolutions of open technologies.

At massive scale, even highly successful open-source systems may not perfectly match:

• Internal workload patterns
• Traffic bursts
• Compliance requirements
• Cost constraints

LinkedIn’s move is less about replacing Kafka globally and more about optimizing their internal stack.

Key Benefits LinkedIn Gains from Northguard

Based on engineering insights and architectural analysis, LinkedIn likely gained:

1. Better Resource Efficiency

Optimized handling of short-lived messages reduces unnecessary disk persistence and replication overhead.

2. Reduced Operational Burden

A system designed specifically for LinkedIn workloads reduces the need for complex tuning.

3. Improved Tail Latency

Real-time ranking and personalization systems benefit from predictable performance under load.

4. Infrastructure Consolidation

Better integration with LinkedIn’s evolving microservices and AI-driven architecture.

Does This Mean Kafka Is Obsolete?

Not at all.

Kafka remains:

• A dominant streaming platform
• The backbone of thousands of enterprises
• Actively developed and improved
• Widely supported by cloud providers

LinkedIn itself continues to use Kafka in many areas.

Northguard solves specific challenges at LinkedIn scale, not universal problems for every organization.

For most companies, Kafka remains more than sufficient.

Strategic Implications for Engineers and Architects

If you’re a system architect, this transition highlights important lessons:

1. Scale Changes Everything

What works at 100 million events per day may struggle at trillions.

Architecture must evolve with scale.

2. One Tool Rarely Fits All

Large systems often become polyglot infrastructures, combining:

• Kafka
• Custom routing systems
• Stream processors
• Cloud-native services

Specialization improves efficiency.

3. Operational Cost Matters

At massive scale, even a 5% infrastructure improvement can translate into millions of dollars saved annually.

4. Latency Is Competitive Advantage

In AI-driven platforms like LinkedIn, real-time decision systems are business-critical.

Milliseconds matter.

What This Means for the Future of Data Streaming

The move from Kafka to Northguard signals a broader trend:

• Increasing specialization in distributed systems
• More focus on workload-aware streaming architectures
• Hybrid streaming models
• Integration with AI/ML pipelines

Future streaming platforms will likely:

• Be more adaptive
• Be more resource-efficient
• Integrate deeply with cloud-native ecosystems
• Offer better latency predictability

Should Your Company Replace Kafka?

Short answer: Probably not.

Unless you are operating at LinkedIn-scale global traffic, Kafka remains an excellent choice.

Instead of replacing Kafka, focus on:

• Proper cluster sizing
• Observability and monitoring
• Partition optimization
• Retention policy tuning
• Consumer scaling strategies

Northguard reflects extreme-scale optimization, not mainstream necessity.

Final Thoughts

LinkedIn’s move from Kafka to Northguard is not a rejection of Kafka. It’s a testament to how systems must evolve as scale, complexity, and business demands increase.

Kafka helped define modern streaming infrastructure. Northguard represents the next internal iteration tailored to LinkedIn’s specific needs.

For engineers, this is a powerful reminder:

Architecture is never static.
At scale, even the tools you created may eventually need reinvention.

And that’s not failure — that’s engineering maturity.

If you run a tech blog or work in distributed systems, this case study is a goldmine for understanding how real-world infrastructure evolves beyond open-source foundations.

Because at the highest level of scale, innovation never stops.