Record Evolution Blog
Our look at data science, industry trends, data analytics and IoT.
We can think about the data generated by an IoT device as going through three separate phases: the data creation itself, collection and organization, and the process of making that
This article discusses the features of a decentralized IoT edge computing ecosystem. You will find a breakdown of the components that need to be designed, as well as some of
As a company that offers an IoT development studio and a cloud data science platform that thrive on developments such as cloud computing, we are concerned about the environmental impact
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Successful IIoT implementation projects require two major actors: the engineer and the data scientist. Yet how do you bridge the gap between these two mindsets?
This article lays out the constituents of what we call the fully rounded IoT development cycle - both tangible and intangible.
The Industrial Internet of Things (IIoT) holds the promise of a fully automated future of instant insight. As a recent phenomenon, the emerging paradigm of the industrial IoT platform has
A recent publication by the German Federal Ministry for Economic Affairs and Energy (BMWi) sheds light on the evolution of the global platform landscape while also providing local data.
A cloud data platform is your counterpart in delivering value and responding to the concrete needs of companies dealing with data-driven processes.
In March 2020, Gartner published its report on the critical capabilities of data science and machine learning platforms. The report looks at the offerings of 16 vendors dominating the market.
Ever wondered how it is possible to get so much from the internet for free these days? Start with your free Twitter account and Google-searching.
This article highlights the need for an efficiently budgeted, differentiated approach to IoT. It lists the key features of custom IoT solutions and stresses the need for future-oriented approaches.
We define an IoT platform as a complete set of multi-layered services and integration capabilities that facilitates the management and maintenance of connected IoT devices and logic.
This article outlines the concepts of greenfield and brownfield and provides an overview of strategies for bringing IoT technologies to brownfield environments.
For a fully-rounded IoT development cycle, data scientists and data engineers can benefit from a comprehensive offering that distinguishes itself by its scope.
Why a data science platform? AI deployments have grown by 270% over the past four years. Yet data scientists and machine learning engineers still have a long way to go.
Back in August 2019, Gartner projected that the enterprise and automotive Internet of Things (IoT) sector will have integrated up to 5.8 billion endpoints by 2020.
Well into 2020, huge trends such as IoT, big data, and machine learning continue to dominate the data analytics landscape.
Default risk is the chance that companies or individuals will be unable to make the required payments on their debt obligations.
Survey data shows that 43% of companies are looking at the digital transformation to increase competitiveness.
Data science surfaced as a term in the 1960s to denote a line of work that specifically deals with the task of making sense of data.
Think of all the strengths you have to showcase as a data scientist. You may have a background in statistics as you sometimes develop new statistical theories for big data.
Notes on IoT and the Current Innovation Landscape: A recent study called The Internet of Things in German SMEs casts new light on Germany’s current innovation landscape.
This is an overview of trends in cloud computing and the use of SaaS platforms. We also outline the advantages of cloud platforms and the specific type of user experience
Managing mid-to-large quantities of data is no longer the daily bread of data scientists only. Working with data has become pervasive.
And Why a Database is Not Enough: Companies and organizations are increasingly using existing data to generate additional values.
Data Analytics and Business Intelligence are on the rise. New trends are evolving exponentially. An increasing number of companies are publishing their whitepapers or case studies online.
This article provides a quick cloud data platform comparison and feature assessment of three cloud data platforms: Snowflake, Panoply, and Repods.
Repods is a cloud data platform to create and manage data pods. Data pods are compact data warehouses with flexible storage, vCores, and memory.
In this article on data visualization, we are going to demonstrate how to create custom infographics in the Repods cloud data science platform.
This tutorial provides a quick overview of ways of using PowerBI, the Business Intelligence Dashboard, on your data prepared and maintained in the Repods Data Science Platform.
FIFA World Cup Statistics: A Data Science Perspective . Creating Analyses and Reports with Repods— Part II
Data-driven Historical Stats from the FIFA World Cup between 1930 and 2014 : In the second part of our article on data modeling, we are going to model data with Repods.
Data-driven Historical Stats from the FIFA World Cup between 1930 and 2014 : Following our tutorials series on Repods, we can now introduce this slightly more complex example.
In what follows, we are going to show you how to approach the task of adding interactivity to complex infographics in Repods.
Preparing the Structure of the Infographic: In this and the following article, we are going to demonstrate how to create complex infographics by adding interactivity in the Repods platform.