Kafka vs. Kinesis

Introduction to Kafka vs. Kinesis

When choosing a data ingest framework, the choice often comes down to Kafka vs. Kinesis. While similar in many respects, there are some key differences that can sway an organization’s decision one way or the other.

The first consideration in evaluating Kafka vs. Kinesis is implementation. Apache Kafka is an open source, distributed pub sub messaging solution that requires an organization to install and manage clusters, and to maintain the application for durability, availability and failure recovery. Amazon Kinesis, on the other hand, is a managed platform that doesn’t require IT teams to implement, manage or monitor its technology.

Another angle on Kafka vs. Kinesis is the cost. Because Kafka requires an IT team to plan and handle storage capabilities, compute resources, capacity planning, Kafka topic management and so on, the in-house costs are inevitably higher. As a managed solution, the cost of running Kinesis tends to be lower, though in some cases Kafka may be more cost-effective in the long run.

When it comes to data storage in Kafka vs. Kinesis, Kafka has the edge: Kinesis stores messages for 24 hours, which can be increased to seven days maximum by changing the configuration. Kafka on the other hand can store as much data as the business requires and as budgets permit.

Kafka also takes the prize in Kafka vs. Kinesis for flexibility, as it can be installed as an on-premises solution or in the cloud, while Kinesis is only available as a fully managed cloud service on Amazon Web Services.

Real-time database streaming for Kafka

Kafka vs. Kinesis: complexity of ingestion

While the Kafka vs. Kinesis debate illuminates advantages and disadvantages on either side, both solutions can present a challenge for real-time data ingestion. Ingesting real-time data can have a deleterious impact on source systems, and it may often require custom coding that can place a great strain on IT teams when the number of sources climes into the hundreds or thousands.

Qlik Replicate® addresses these challenges with an automated, real-time and scalable data ingest platform.

Kafka vs. Kinesis: real-time data ingestion with Qlik Replicate

Qlik Replicate enables organizations to accelerate data ingestion, replication and streaming no matter which streaming service is being used: Google Pub Sub vs. Kinesis, Confluent, Kafka, Azure Event Hub or MapR-ES.

Qlik Replicate enables IT teams to:

  • Reduce the impact on source systems by using log-based Change Data Capture (CDC) and by using a unique zero-footprint architecture that eliminates the need for agents to be installed on source systems.

  • Simplify management of data ingestion with a configurable graphical user interface that makes it easy to set up data feeds with no manual coding.

  • Scale quickly as needed to ingest data from hundreds or thousands of databases.

Learn more about data integration with Qlik