Top Customer Kafka & Streaming data requests and how Big Industries can help
Data streams can be processed on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics. Information derived from such analysis gives companies visibility into many aspects of their business, (near) real time, making the organizational decision making processes multi-fold faster. Our engineers have deep expertise with designing, building and operating stream processing solutions.
Architecture design & advanced cluster configuration
Our Architects analyse and qualify the Use Cases, identify the data sources, define the data ingestion strategy & acquisition, plan & design data storage and data processing pipelines, establish an information security strategy and choose different forms of data consumption outputs.
Cluster setup & automated deployment
Our DevOps engineers will help you with automating the deployment of your Kafka cluster on any platform. We use infrastructure-as-code tooling to deploy and manage Kafka clusters in the public cloud, as either a cloud-native service on Kubernetes or on bare metal and virtual machines.
Data Pipelines for (near) real-time querying
In the context of Apache Kafka, a streaming data pipeline means ingesting the data from sources into Kafka as it is created and then streaming that data from Kafka to one or more targets. By implementing a streaming data pipeline we are able to react to data as it changes, while it is current and relevant. And this in contrast to the batch world where we wait a period of time before collecting a set of data and then sending it to the target. Furthermore, in a Big Data context, a streaming data pipeline spreads the processing load and avoids resource shortages from a huge influx of data.
Data Driven application development
Our Data Engineers develop Data Driven applications that operate in real-time on a diverse set of data, pulled from multiple different sources. Examples are the use of Machine Learning to make real-time recommendations to customers or detect fraudulent transactions. Or use Graph analytics to identify influencers in a community and target these with specific promotions or perhaps use spatial data to keep track of deliveries.
Note: special thanks to Joris Billen for his contribution to this blog post
Matthias is founder of Big Industries and a Big Data Evangelist. He has a strong track record in the IT-Services and Software Industry, working across many verticals. He is highly skilled at developing account relationships by bringing innovative solutions that exceeds customer expectations. In his role as Entrepreneur he is building partnerships with Big Data Vendors and introduces their technology where they bring most value.