Scalability, Performance, and Architecture: Unpacking the Differences Between Kinesis and Kafka

With data streaming becoming critical for today’s real-time analytics and applications, choosing the right event streaming platform is key. Two of the most popular options are Amazon Kinesis and Apache Kafka. In this comprehensive guide, we analyze how Kinesis and Kafka compare on scalability, performance benchmarks, and underlying architectural differences.

Understanding Scalability

Scalability represents the ability of a streaming platform to elastically scale up or down to handle changes in data volumes and throughput. Auto-scaling capacity on demand is vital for workloads prone to usage spikes or troughs. Let’s examine AWS Kinesis vs Kafka scaling approaches:

Kinesis Shards and Resharding

Kinesis uses shards for processing data streams in parallel. A shard can take in up to 1000 records per second or 1MB of data per second. Shards provide fixed data processing capacity.

Kinesis auto-scaling automatically adds more shards when data flow exceeds shard limits during usage spikes. This prevents bottlenecks. Kinesis also supports manual resharding – splitting shards that reach capacity.

However, resharding temporarily halts data intake for a few minutes, which can disrupt real-time processing. Also, there are limits on the maximum number of shards per stream, usually a few hundred. So, at very high data volumes, the shard limits can bottleneck throughput.

Managing a high number of shards also adds operational overheads for monitoring and provisioning. So, while shards allow Kinesis streams to scale to process large data, real-world limits on the number of shards make it hard to match Kafka’s ultra-high throughput capacities.

Kafka Partitions and Brokers

Kafka streams data via topics. Topics are divided into partitions. Partitions are distributed across Kafka brokers, servers forming a Kafka cluster.

Kafka scales by adding more brokers and spreading partitions across the new brokers. This model allows seamlessly expanding capacity horizontally to handle increasing data volumes. It enables almost unlimited scalability since new brokers can be added programmatically without data limits per broker.

However, to allow this smooth linear scaling, partitions have to be pre-created for each topic with sufficient spare capacity for future data growth. Proactively over-partitioning topics avoids potential data bottlenecks as data volumes grow. The downside is that extra partitions increase hardware resource needs before utilization ramps up. So, balancing partitions to meet current and projected needs is important.

Overall, Kafka’s broker/partition architecture enables transparent horizontal scaling to adapt to data volumes elastically. However, it requires planning partitions adequately upfront for workload growth to realize this benefit.

Performance and Benchmarks

In terms of real world performance, Kinesis can handle writes of 1 MB per second and reads of 2 MB per second per shard. Kafka on equivalent m5.2xlarge instances can deliver about 3 times the write throughput and 6 times the read throughput of Kinesis for the same cost as per AWS published benchmarks. Independent third-party tests by Imply Data found Kafka delivering over 7 times the throughput efficiency at scale.

Consistency and Ordering

For data processing needs requiring strong consistency, Kafka offers exactly-once semantics and total ordering for records sent to a partition. This ensures updates are reliably captured and accounted for. Kinesis order promises are the best effort per shard. Ordering across shards requires checking sequence numbers.


Kafka commits to disk, enabling faster persistence with replication for protection. Kinesis works on batches for higher latency between a few hundred milliseconds to a second. So, Kafka provides lower end-to-end latency for time-sensitive applications.

Ingestion and Delivery Architectures

Now let’s analyze some key underlying architectural differences between Kinesis and Kafka that impact scalability and performance:

Regional Level Architecture

Kinesis Architecture

  • Kinesis is managed as a regional service with data replicated synchronously across 3 availability zones (AZ) for high availability.
  • Stream shards and data records are balanced across AZs automatically. Node outages or partitions inside AZ don’t impact data delivery.
  • Easy to consume stream data from the same region. Cross-region data delivery requires manual efforts.

Kafka Architecture

  • Kafka clusters are formed across zones, regions, data centers, or cloud availability zones.
  • Topics & Partitions are distributed across brokers, providing built-in redundancy.
  • Geo-replication can allow data replication across data centers or regions.
  • Kafka allows application location-agnostic data production and consumption.

Data Access and Analytics Integration Capabilities 

Kinesis integrates seamlessly with AWS analytics services like Redshift, S3, and Athena. Kafka relies on open source tools or the Confluent platform for integrations. While Kafka has a wider tooling ecosystem, Kinesis reduces product integration overheads by leveraging AWS managed offerings.

Ingress and Egress Optimization

Kafka provides data locality, allowing apps to access streams from the nearest broker. It also offers data balancing across brokers and controlled batching. These optimize network and disk I/O, enhancing performance. Kinesis ingress-egress works on synchronous calls between apps and shards, leading to some overheads.

Multi-tenancy and Account Management

Both platforms provide account isolation and access controls for multi-tenancy needs. Additionally, Kafka facilitates tenant separation by topic, enabling unified account management across environments. While the Kinesis identity federation supports some cross-account access, it still requires separate streams per account.


Kafka provides metrics for brokers, topics, partitions & consumers to help track cluster health & usage. Kinesis metrics focus on shards, records, and capacity, helping monitor data flows. While Kafka needs Graphite/Grafana for visualizations, Kinesis integrates with CloudWatch dashboards. Error tracking in Kafka must examine logs versus CloudWatch error streaming for Kinesis.


Encryption in transit using HTTPS and at rest via server-side is available in both options. Additionally, Kinesis offers client-side encryption for stronger data security. Both employ role-based access and VPC controls for isolation.


Given it is a fully managed service with extensive compliance coverage, Kinesis conforms to regulatory requirements out of the box whether related to data sovereignty, residency or security policies across sectors. Kafka offers schema registry and enterprise security plugins to meet compliance needs.


In summary, Kafka is designed to offer more granular throughput control combined with high reliability and low latency. Kinesis provides easier operational management with deeper AWS integration suited for Serverless use cases but hits limits at hyper scale. For complex event processing pipelines that require advanced sequencing, ordering, and delivery guarantees across systems, Kafka is the best fit. Kinesis offers simpler product experience for use cases not needing sophisticated messaging capabilities. Evaluate both platforms against the application architecture, data guarantees, tooling and analytics integration requirements.

Scalability, Performance, and Architecture: Unpacking the Differences Between Kinesis and Kafka was last updated February 21st, 2024 by Mike Cloud