The latest news, releases, features and commentary
Nussknacker can be used to inject additional business logic for the Firebase events and is easily integrated with Firebase subscriptions. This blog post describes how to integrate Google Firebase with Nussknacker with the help of Kafka Connect.
With a bridge application, the business team won’t need to spend so much time thinking about optimal process design before they pass their requirements to the IT team. They can carry out experiments on live data, quickly verify assumptions and introduce adjustments that will improve prepared indicators.
In this blog post, I'll take you step-by-step through integrating with Microsoft Azure Active Directory (AAD), based on recent work for one of our valued customers, to show you the real-world impact and how easy it can be.
We are delighted to unveil that most of the inimitable software engineers of TouK have joined Snowflake today, enhancing their capabilities. At the same time, a few of them will continue working with us on Nussknacker.
The power of Snowflake is that you can build customer profiles with a 360° view. All the relevant information about your customers in one place. And Nussknacker not only makes it possible to react to current behaviour using all the prior knowledge, but it makes it possible for non-developers, shortening the path from an idea to actually taking actions.
The success of spreadsheets is proof that non-professionals are not afraid of coding - just the constructs they use must be easier to learn than regular programming languages.
Low-code solution for enrichments and time windows in marketing promotion. In this post we go through a specific example to highlight Nussknacker’s capabilities: data enrichments and aggregates in time window.
Nussknacker is a low-code tool that is also highly extensible - it allows the implementation of custom components, and custom runtime engine managers - reducing the deployment time
Thanks to Nussknacker’s schema evolution support, it will evolve all incoming events to the latest schema version. If a new field will be unavailable, the default value will be used.
Low-code and open data standards allow fast TTM and bring business experts closer to faster iterations and innovations. You can build complex decision scenarios using bricks-like components and low code
Both Stream Designer and Nussknacker Designer take similar, visual tool approaches to facilitate working with data streams. However, they diverge when it comes to target user groups. What’s most important is that both make data stream processing more ubiquitous and democratised.
Once data is on topics with defined schemas, they are ready to be used in Nussknacker. What’s more, Nussknacker has a flexible expressions language that allows for defining decision logic. The results of computations are visible in a nice form on metrics charts.
The general approach should be pragmatic: while we should aim at hiding all technical complexities of underlying systems and data, sometimes we have to accept that there will be cases where technical help for the experts (temporary or permanent) will be needed.
Algorithms for credit rating as well as in other domains tend to be complicated, therefore having them implemented in a visual way lets us understand them better than having them in the code.
How can data streaming become part of the enterprise nervous system, if acting on the data in streams is the domain of savvy developers only?
This version focused mainly on unifying our kafka source and sink components and some filter and choice (former switch) component improvements.
See the details
You can use Nussknacker to design and deploy ML models in use cases where complex decisioning, data transformation, and enrichment logic are needed. The decision logic you can build with Nusskkncker can be pretty sophisticated - we moved the bar of how much you can achieve with low code to pretty high.
Extending SQL for the streaming world is very promising, but its main drawback is that in order to use it, you need to have all of your data in a homogenous environment. We took another approach that allows us to integrate any existing technology with stream processing.
Chances are that you are already using a container’s scheduler - that is, Kubernetes. Wouldn’t it be great if we could just use it to deploy our business logic? You want to handle events from Kafka, enrich them with data from external systems, and possibly score some ML models? Let's glance at what is possible in Nu 1.3.
We’ve been working on making Nussknacker more cloud-ready, and it turned out we need to make the runtime container as small as possible.
It looks like GraalVM can be the answer: the resulting image and memory usage are really small, which makes running each Nussknacker scenario
in a separate container viable.
People quite often ask us, what are the differences between Nussknacker and other similar (at first glance) tools. There are usually some overlapping areas in which many of them can be used interchangeably. But when your system grows, tools used for other purposes than they were designed for can cost you a lot of pain and can introduce a lot of technical debt.
A data cache allowing fast access by key is a must-have when dealing with a stream of thousands of records per second
We believe there are many use cases where Nussknacker can bring significant value without operational overhead caused by Flink or other sophisticated stream processing engine
We have released version 1.1 of Nussknacker 🍾
See how lowcode can help with business decisions on streaming data 💪
Business users can create or change decisioninig algorithmss anytime, without any assistance from IT department.
We can help you customise and extend Nussknacker to meet your specific needs and then deploy and maintain it on your infrastructure.