The backbone your data pipelines have been waiting for.

Kafka isn’t just a buzzword—it’s the backbone your data pipelines have been waiting for.

Who new that messaging system, like Apache Kafka should hold a central place in a Data Engineer toolbelt. 

Apache Kafka is a low-latency distributed data streaming platform for real-time data processing. Kafka can handle large volumes of data and is very helpful for distributed data integration projects.

Top 2 reasons why you might need Kafka in your Data Integration architecture

1. Support multiple destinations by decoupling data producers and data consumers. Data in source will be processed only once, which lowers an overall cost in consumption-based data producer databases and we can add new/change existing destinations without changing the extraction components.

2. Ability to deal with massive amounts of data, supports high throughput and scalability. Decoupling pipeline extract and load stages is an important Data Integration principle and can improve pipeline flexibility, extract and load run asynchronously and independently. Extraction process does not need to wait for a data loading to complete. This approach minimizes risk of loosing data, if the pipeline encounters failure, decoupling makes sure that extracted data is preserved. If data sources produce high volumes of data, the extraction process can scale without immediately overloading the loading system. We can also scale data producing pipeline and data consumption pipeline independently.

Extract pipeline stage

Data producers can send data into Kafka, or Kafka can connect to a source system and pull the data using Kafka Connect. The data remains in the queue until it is fully processed by all consumers.

Load pipeline stage

Data consumers can programmatically read data from Kafka, or Kafka can push data to a target database using Kafka Connect

Transform pipeline stage

Kafka can also be used for data manipulation. For instance, Kafka Streams, a powerful library, enables us to transform or analyze data streams directly within Kafka, generating new data or triggering additional events.

In the Azure cloud ecosystem, we can use Azure Event Hubs, Microsoft's direct equivalent to Kafka. It is a high-throughput event ingestion service that even supports Kafka APIs for compatibility. Azure Event Hubs integrates seamlessly with other Azure services, such as Azure Stream Analytics and Azure Data Lake, providing similar functionality to Kafka. However, it is a proprietary implementation by Microsoft and does not use Kafka internally.

What do Netflix, LinkedIn, and Uber have in common? Their secret ingredient is Kafka, and it could be yours too.