Understanding Kafka Connectors
Kafka Connectors, at their core, are the invisible pipes that seamlessly connect data stores and systems across your business in real-time. Picture it as a constant stream of information — orders, customer feedback, inventory updates, user behavior, — flowing from one part of your business to another, potentially being processed while in motion. This real-time capability is where Kafka and Kafka Connectors shine, they are bridges that span the gaps between different parts of your business, ensuring a smooth, low-latency flow of information.
Traditional Integration Methods
On the flip side, traditional methods involve batch processing. This means data is collected and processed in chunks, rather than streamed from place to place or processed in motion. Think of it as collecting snapshots of information at intervals, rather than consuming from a continuous stream. Depending on the use case, architecturally speaking, batch processing could be the right move or the source of significant limitations.
Advantages of Kafka Connectors
One major advantage of Kafka Connectors is their real-time data streaming. They operate in the now, ensuring that information is transmitted instantly as events occur. This capability is especially crucial in scenarios where up-to-the-minute insights are vital, such as monitoring system health or tracking user activity. You get a bunch of benefits out of the box with Kafka, scalability, durability, observability, operability - but Kafka Connectors give you the ability to plug into a slew of different data producers and consumers, stores, sinks, and systems of record.
This means you can seamlessly connect components of your business using one extremely flexible and powerful platform, and it also means you don’t have to write and maintain a client for the Marketo API (let’s be real, it’s a terrible API) to pump that data into your warehouse.
Drawbacks of Kafka Connectors
While all of the above is true (and awesome) you should aware of potential challenges you might face if you are looking to Kafka as a one-stop-shop for data integration.
- The first thing you should ask yourself is if you actually need real-time data and/or stream processing. A well-tuned batch processing implementation might meet your needs, especially if you are a small business. Many larger businesses run a combination of streaming and batch to support their varying data integration, analytics, and ML requirements.
- You should also consider that the continuous flow of data might not be suitable for every type of integration or data/business process. There are ways to work around this in Kafka by playing with topic retention and looking to ksqlDB, but it is certainly worth mentioning.
- Another key consideration is Kafka’s message size limitations. You shouldn’t be processing anything larger than 20MB. It’s not to abnormal to find legacy databases at large businesses that you need to extract data from, where bad architectural decisions have led to rows that are much larger than the upper bound for Kafka messages, never mind the row size limits in your data warehouse (Snowflake is 16MB or 8MB depending on data type), or the page size in the source DB 😱. If you need to deal with large objects that have been stored in structured databases, Kafka probably isn’t the answer.
Adapting to these challenges requires careful consideration. Kafka is great, but it’s not always the answer.
Advantages of Traditional Integration Methods
It can be cheaper to approach your data needs using batch. Batch processing, while not as fast, ensures a systematic and controlled approach to data integration. You can use DAGs, and there is no shortage of data engineers who know how to transform data in Spark, AWS Glue, Snowflake, and Databricks. This method might be preferable in scenarios where a steady, scheduled flow of data is more critical than real-time updates.
To be clear, even if you are streaming data into your data warehouse or lake, you can still process it in batch. The two are not mutually exclusive and often used in conjunction to meet business needs in well-architected data platforms.
Drawbacks of Traditional Integration Methods
That said, when it comes to data integration, traditional methods come with their own set of drawbacks. The processing speed is slower, and latency in data updates, for ML use cases especially, can be problematic. The rigid structure of batch processing may struggle to cope with evolving schemas and rapidly changing data requirements. Also note that in a fully-batch shop, stream processing won’t be an option.
Now, let's put Kafka Connectors and traditional methods side by side. It's not matter of speed versus stability, or real-time updates versus controlled processing. In an ELT world, the decision on whether to use Kafka hinges on factors like the nature of your business and its data sources and sinks, the urgency data freshness, the evolution of schemas, the need (or lack thereof) to process data in flight, and an array of considerations specific to each company.
For busy managers, the key is understanding your specific business needs. If real-time insights are crucial and your business is dynamic, Kafka might be the way to go. On the other hand, if your business is heavily invested in ETL and data requirements are more predictable, traditional methods could be a better choice. Or, as is often the case, Kafka can service a wide array of your use cases but not all of them and you end up with a multi-facetted platform that brings the right tool to bear when a given challenge arises.
In the end, it's about finding the right fit for your business. Whether you opt for the real-time magic of Kafka Connectors or the steady reliability of batch processing, or both, making an informed choice ensures your data platform aligns with your business goals.