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Visualization & Streaming
Data Science

Visualization & streaming: How to use it to your advantage

Importance of data visualization Many people would say that knowledge sharing is one of the noblest things any human being can do. Aside from helping other people grow and be better, by sharing their knowledge people become happier, develop new professional or private connections and bring more purpose to their

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data detective Ivo
Interview

Get to know our data detectives – Ivo

Come and meet Ivo, one of our data detectives and our data scientist. Venture into his curious mind and discover what makes him love his job, what pushes him forward, and what is his outlook on the world. As one of our talented blog post writers, this interview came easy

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data misinterpretation
Data Science

All you need to know about data misinterpretation and misuse

Data misinterpretation comes from deliberate or unintentional false data interpretation. Often different data users will interpret data differently. It depends on the level of data literacy and what exactly users need for the data to show. Various influences determine how we see and use data, and wrong representation can lead

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Data democratization
Business

Deep dive into data democratization – the good and the bad

Data democratization is being labeled as one of the most attractive trends in data science and data management for the upcoming years. It’s been raising hype for quite some time and it’s not slowing down. As companies face more and more data, they need effective ways of handling it, but

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How we determined KPIs for our Retail BI Platform
Business

How to determine KPIs for a Retail BI Platform

When talking about KPIs we must understand their applicability and what they actually mean. In any data science and data engineering project, the determination of KPIs, or rather metrics and insights, is an important step. How would you know what to measure and what can you actually do with all

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Apache Spark
Data Engineering

Apache Spark – How to create a powerful streaming application

After setting up Apache Kafka, the next step in our Retail Business Intelligence Platform project  was to set up Spark, another widely used software solution from the Apache workshop. Apache Spark, the real spark necessary to ignite our project and turn it into a true stream processing arrangement, is probably

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