Different BI tools offer different approaches to building dashboards. On one end of the spectrum, you have tools that prescribe having one query per chart and on the other end you have tools that espouse implementing a complex semantic layer. I believe there's a middle path that lies between both extremes, and I call it the dataset-centric approach. In the dataset-centric approach, the tool is connected to individual datasets that are expected to contain all of the metrics and dimensions for a given subject area. In this post, I'll describe the strengths and tradeoffs for each of the approaches and make the case for the dataset-centric approach as the ideal one for fast-moving data teams.

Talk of low-code, no-code platforms have been making the rounds of late, with Goldman Sacks injecting USD90 million into low-code software maker WSO2, while data automation platform Cascade Labs last week raised USD5.3 million. And as observed in a Washington Post report this month, the rapid rise of low-code has allowed non-computer scientists to create digital applications that were previously the domain of computer science graduates, while simultaneously opening the door to deliver fast and meaningful impact to organizations. But what exactly is a low-code, and what implications does it have on building a vibrant data culture or developing data-centric and AI applications? At its heart, low-code is essentially a development environment for creating application software by leveraging scripting and a graphical user interface (GUI). The ability to visually configure applications significantly speeds development over traditional programming languages such as C or Python.

Unlike typical Web Apps, this is Data-Focused Data Science Machine Learning Built during the Data Science Workflow or at the end Interactive EDA UI for the Model Inference Examples: Data Science Dashboards ML Model Result with Parameters ML Interpretability App Model Inference as a Web App (like Sales Forecasting) Hey Python Data Professionals, How many frameworks do you need to build a full-stack data app? Example Sales and Demand Forecasting App Skin Cancer Prediction with Images Analytics Dashboards HTML CSS Javascript Flask / Django Typically, pip install streamlit Now, What is streamlit? Streamlit, a python library that helps to turn data scripts into shareable web apps. No front‑end (html, css, js) experience required. Open Source 16,000 GitHub stars pip install streamlit 4.5 million downloads Well-Funded $35 million Series B Loved by Community 10,000 organizations (including over half of the Fortune 50) A Simple "streamlit" App Widgets Just like creating a variable Layouts st.columns() 3rd Party Components streamlit.io/components

Artificial Intelligence (AI) has proven its ability to re-invent key business processes, dis-intermediate customer relationships, and transform industry value chains. We only need to check out the market capitalization of the world's leading data monetization companies in Figure 1 – and their accelerating growth of intangible intelligence assets – to understand that this AI Revolution is truly a game-changer! Unfortunately, this AI revolution has only occurred for the high priesthood of Innovator and Early Adopter organizations that can afford to invest in expensive AI and Big Data Engineers who can "roll their own" AI-infused business solutions. Technology vendors have a unique opportunity to transform how they serve their customers. They can leverage AI / ML to transition from product-centric vendor relationships, to value-based relationships where they own more and more of their customers' business and operational success… and can participate in (and profit from) those successes.

Flask used to come to mind when data scientists want to spin up a python-based data science app, but there is a better option now. To create an interactive facade for a machine learning or visualization script, Streamlit is way faster, since it removed the need to write any front-end code. Now we'll go through step-by-step how to build a Streamlit app. I will also review some pros and cons of Streamlit. Anyone who wants to put an interactive user interface or visible facade to the python scripts. Streamlit can be used to built machine learning/AI apps or display exploratory/analytical data visualizations or both at the same time.

As technology evolves at a rapid pace, the healthcare industry is transforming quickly along with it. Tech breakthroughs like IoT, advanced imaging, genomics mapping, artificial intelligence and machine learning are some of the key items re-shaping the space. The result is better patient care and health outcomes. To facilitate this shift to the next generation of healthcare services – and to deliver on the promise of improved patient care – organizations are adopting modern data technologies to support new use cases. We are a large company operating healthcare facilities across the US and employing over 20,000 people.