The Nu Blog
The latest news, releases, features and commentary
Nu chapter for TouK
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.
Combine streaming and historical data for real-time decisioning
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.
What is wrong with low-code tools in streaming?
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.
Translating business case into streaming concepts
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.
Take Nussknacker to the next level with configuration only
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
How to handle quickly evolving Elasticsearch documents in Nussknacker
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.
Speed up your OpenBanking decision scenarios with Nussknacker
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
Stream Designer and Nussknacker Designer comparison
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.
Real-time sentiment analysis using Nussknacker and SpEL
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.
Low-code for business rules - are we there yet?
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.
Building a credit rating verification service with Nussknacker
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.
Is SQL enough to democratise stream processing?
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
ML models inference. Non trivial processing algorithms built and deployed in an easy way
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.
Stretching old solutions to fit new problems doesn’t work. A genealogy of Nussknacker.
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.
NU has many flavours - which one is for you?
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.
Running low-code scenarios with GraalVM native image
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.
Nussknacker compared with similar open-source tools
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.
Apache Ignite in Nussknacker environment. Lessons learned
A data cache allowing fast access by key is a must-have when dealing with a stream of thousands of records per second
Event streams always need Flink, or ...not really?
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.