Monetize Kafka Streaming Data with Active(SD)™
Volt Active Data’s Active(SD) lets you make complex, real-time decisions on your streaming data.
Your streaming data is only increasing in volume and number of sources.
True Real Time
With the right kind of data platform, you can easily capitalize on this influx of Kafka data instead of being overwhelmed by it.
TOP 7 APACHE KAFKA CHALLENGES
…and most importantly, how to solve them!
Apache Kafka is now synonymous with streaming data, but that doesn’t necessarily mean it’s easy. As most open-source tools go, Kafka comes with its fair share of issues and challenges, especially when you start trying to use it at scale. These challenges include things like constant rebalancing, mid-batch errors, and one consumer doing all the work.
The good news? Most of these Kafka issues are resolvable with the right approach and/or Kafka ecosystem tool.
We’ve been in the Kafka trenches, so to speak, for years, and know exactly which issues companies most often face with Kafka and how best to solve them.
Read this paper to learn:
- The 7 main Kafka challenges companies struggle with the most
- How to resolve these problems with or without the use of a Kafka ecosystem tool
- Where certain Kafka tools work and others don’t, and how to choose between them for each Kafka issue
Read now to get on your way to using Kafka at scale, without issues.
To make your streaming data usable for business goals and drive revenue, your data platform must allow you to both correlate data from the multiple streams in real time and add complex decisioning to it. This can only be achieved by storing and then intelligently using, at just the right time, data from previous events. But this isn’t easy to do, given that data streams often come from multiple sources and at different rates and different points in time.
Active(SD) can handle massive volumes of streaming data (at least 10K TPS per second per core) without taking a breath. By ‘handle’ we mean remaining ‘stateful’, which means we can make intelligent decisions on streaming data in real time—decisions that involve tracking state and working with shared, finite resources.
Active(SD) is also wire protocol level-compatible with Kafka, which means we can handle millions of records per second while pretending to be a Kafka cluster.
Handling Streaming Data at Scale
Receiving tens of thousands of events per second is the ‘new normal’. Amazing tools like Kafka have exponentially increased the scale of streaming data but also created complex problems and outpaced companies’ ability to process data in real time. At the same time, many applications now require events to be handled separately, one record at a time, which can overwhelm systems.
The volume and complexity of event-based data entering systems makes it essential to stay up-to-date, as falling behind can quickly become unmanageable.
Simply put: If you can’t keep up with your data, your systems will go down, and so will your applications.