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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. The term “agile” was originally conceived in 2011 as a software development methodology.

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A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

Following this, in 2002, it began delivering its knowledge to customers in online format, using dashboards and interactive reports that provided easier and faster access to data and analysis. Additionally, it continuously explores reams of data and modern tools to improve its capabilities and adapt to the changing data landscape.

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Exploring US Real Estate Values with Python

Domino Data Lab

This post covers data exploration using machine learning and interactive plotting. Visualization tools help make the shape of the data more obvious, surface patterns that can easily hide in hundreds of rows of data, and can even assist in the modeling process itself. .: Interactive Data Visualization in Python.

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Build efficient, cross-Regional, I/O-intensive workloads with Dask on AWS

AWS Big Data

After deployment, the user will have access to a Jupyter notebook, where they can interact with two datasets from ASDI on AWS: Coupled Model Intercomparison Project 6 (CMIP6) and ECMWF ERA5 Reanalysis. Dataset Variables Disk Size Xarray Dataset Size Region ERA5 2011–2020 (120 netcdf files) 53.5GB 364.1

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Understanding the different types and kinds of Artificial Intelligence

IBM Big Data Hub

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. For example, Apple made Siri a feature of its iOS in 2011. This understanding can affect how the AI interacts with those around them. In theory, this would allow the AI to simulate human-like relationships.

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How Zurich Insurance Group built a log management solution on AWS

AWS Big Data

Develop log and trace analytics solutions with interactive queries and visualize results with high adaptability and speed. Austin Rappeport is a Computer Engineer who graduated from the University of Illinois Urbana/Champaign in 2011 with a focus in Computer Security.

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A blazingly fast database in a data-driven world

IBM Big Data Hub

We founded MemSQL (the original name of SingleStore) in 2011. Around 2011, we worked with a hot gaming company with a real-time analytics use case to understand what their users were doing in the moment to optimize the gaming experience by monitoring how users interacted with the game.