# Data Visualization Published articles for Data Visualization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## TanStack Charts Introduced with a Framework Agnostic Grammar of Graphics for TypeScript DevFeed: [TanStack Charts Introduced with a Framework Agnostic Grammar of Graphics for TypeScript]() Original publisher: [Read original article]() Author: Daniel Curtis Published: 2026-09-17T05:53:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Library](), [TypeScript](), [Graphics](), [Frameworks](), [React]() Tags: [angular](), [data-visualization](), [development](), [framework](), [graphics](), [introduced](), [library](), [news](), [react](), [react-native](), [tanstack-charts-alpha-introduced](), [typescript](), [visualization](), [vue-js](), [web-development]() ### AI overview TanStack Charts is a new framework-agnostic TypeScript visualization library that uses a grammar-of-graphics approach instead of fixed chart types. It is in Alpha, has about 160,000 weekly downloads, and supports multiple frameworks and environments, though it is not yet stable for production use. ### Source excerpt TanStack Charts is a new, framework-agnostic visualization library for TypeScript. It allows developers to create visualizations by composing various elements instead of using fixed chart types. Currently in Alpha, it has about 160,000 weekly downloads. The library supports multiple frameworks and is designed for a variety of environments, though it is not yet stable for production use. By Daniel Curtis ## Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions DevFeed: [Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions]() Original publisher: [Read original article]() Author: hello@smashingmagazine.com (Meriem Benhabiles) Published: 2026-08-26T13:00:00Z Content type: article Language: en Sources: [Articles on Smashing Magazine -- For Web Designers And Developers]() Topics: [dashboards](), [User experience (UX)](), [data](), [datasets]() Tags: [dashboards](), [data](), [data-visualization](), [design](), [insights](), [ux]() ### AI overview This article argues that effective dashboards require UX thinking, not just available data. It explains how designing for a specific audience, question, and intended decision can turn charts into functional tools for insight and action. Examples including Anscombe's datasets show how visualisation can reveal patterns and communicate meaning that raw numbers conceal. ### Source excerpt Data visualisation sits at the intersection of two disciplines that rarely talk to each other: data and design. Meriem Benhabiles explores what changes when you bring structured UX thinking to dashboards and data presentations, from the questions you ask before opening any tool to the decisions that determine whether an insight actually lands. ## Open Sauce and GPS time were my summer AI Antiseptics DevFeed: [Open Sauce and GPS time were my summer AI Antiseptics]() Original publisher: [Read original article]() Author: jeff@jeffgeerling.com (Jeff Geerling) Published: 2026-07-22T14:00:00Z Content type: opinion Language: en Sources: [Jeff Geerling]() Topics: [Artificial Intelligence](), [Data visualization](), [MicroPython](), [Claude](), [datasets](), [Code]() Tags: [ai](), [badge](), [claude](), [coding](), [data-visualization](), [electronics](), [experimentation](), [gps](), [micropython](), [open-sauce](), [projects](), [time](), [videos](), [visualization]() ### AI overview The author describes using Claude to build a 2,141-line MicroPython Badgeware app for a Pimoroni Tufty 2350 and Adafruit PA1010D GPS module. The GPS Time Badge uses GPS data to discipline the badge's internal real-time clock, although timing accuracy was limited by the hardware and BadgeOS architecture. The author also logged GPS satellite and fix-quality data and used Claude to create an interactive visualization, highlighting data visualization as a practical use of AI tools. ### Source excerpt In the midst of our AI slop revolution, traveling to the West coast for Open Sauce this past weekend was the perfect antiseptic for rising costs, summer heat, and online divisiveness. It's ironic, then, that I used Claude to vibe code my Tufty GPS Time Badge. Partly due to time constraints, and partly because I wanted to see if I could complete a personal project end-to-end, without editing a line of code, I throw my requirements at Claude and ultimately came up with this 2141-line MicroPython app for Pimoroni's Badgeware ecosystem. ## Reflections on the Evolution of Data Science + AI at Microsoft and a Career Transition DevFeed: [Reflections on the Evolution of Data Science + AI at Microsoft and a Career Transition]() Original publisher: [Read original article]() Author: Casey Doyle Published: 2026-06-30T07:16:00Z Content type: opinion Language: en Sources: [Data Science at Microsoft]() Topics: [Data Science](), [Microsoft](), [Artificial Intelligence](), [Data visualization]() Tags: [ai](), [data-science](), [data-visualization](), [development](), [journey](), [microsoft](), [reflections](), [ship-of-theseus]() ### AI overview The author reflects on the evolution of Microsoft's Data Science + AI publication, their departure from Microsoft, and the career experiences that shaped their approach to communicating complex ideas for business impact. ### Source excerpt Reflections on storytelling, change, and what enduresPhoto by Joseph Barrientos on Unsplash. And so the mythical hero Theseus sailed home to Athens after defeating the Minotaur. To honor him, the Athenians preserved his wooden ship in their harbor down the centuries. As the old planks rotted, builders replaced them with identical new ones. Eventually, they replaced every single original piece of wood. This sparked a famous debate among philosophers: With every part replaced, is it still the same ship? Like the ship of Theseus, what endures also undergoes many changes. That idea applies not only to the Data Science + AI at Microsoft online publication you're reading now, but also to my own career journey. After two stints totaling more than 32 years at Microsoft, and more than six years leading Data Science + AI at Microsoft, I am turning the page on both chapters this month as I prepare to depart. As I mark this transition, I feel many emotions, but one that stands out most is gratitude -- for the people, opportunities, and experiences that have shaped both the work and me. Leading Data Science + AI at Microsoft (which we call DS@M internally) since its inception has been a highlight of my working life. From its initial focus on data science in early 2020 to its evolution to encompass AI starting in 2023, DS@M continues to attract an audience as it approaches 10,000 followers. For me, this is especially remarkable given some of the internal pushback that came in the time before we launched, reflecting skepticism that it was possible, much less advisable, to share our data science expertise outside the company without crossing proprietary lines. That we moved forward -- and ultimately succeeded -- was possible only because of the many authors and collaborators who believed in the idea and were willing to contribute their work. More than 340 articles later, we've amply proved we could do it, and do it well. In many ways, this work reflects the culmination of the paths th ## 3 Questions: Beyond data-driven aesthetics DevFeed: [3 Questions: Beyond data-driven aesthetics]() Original publisher: [Read original article]() Author: School of Architecture and Planning Published: 2026-06-29T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Data visualization](), [Algorithms](), [Mathematics](), [Computer science](), [Machine learning]() Tags: [3-questions](), [aesthetic-judgment](), [ai](), [ai-and-aesthetics](), [alexandros-haridis](), [algorithms](), [alumni-ae](), [applied-arts](), [architecture](), [artificial-intelligence](), [beyond-data-driven-aesthetics](), [computational-aesthetics](), [computer-science-and-technology](), [data](), [data-visualization](), [design](), [design-computation](), [exhibits](), [interactive-installations](), [interview](), [machine-learning](), [mit-architecture](), [mit-exhibits](), [mit-keller-gallery](), [mit-sa-plus-p](), [philosophy](), [research](), [school-of-architecture-and-planning](), [shape-grammars](), [special-events-and-guest-speakers]() ### AI overview An MIT Keller Gallery exhibition by Alexandros Haridis examines the history of aesthetic judgment and creative production in computing, connecting architecture, design computation, algorithms, and machine-learning systems. ### Source excerpt In a new Keller Gallery exhibition, Alexandros Haridis SM '17, PhD '22 traces centuries of ideas about aesthetic judgment and explores how design can make complex computational systems visible. ## How I Validated Design Decisions Before Writing Production Code DevFeed: [How I Validated Design Decisions Before Writing Production Code]() Original publisher: [Read original article]() Author: andyacevedo Published: 2026-06-04T01:29:47Z Content type: article Language: en Sources: [Slack Design]() Topics: [AI-assisted coding](), [Data visualization](), [Algorithms](), [Figma]() Tags: [ai-assisted-coding](), [algorithms](), [data-visualization](), [figma](), [projects](), [uncategorized]() ### AI overview A Slack designer explains how AI-assisted coding can make design exploration practical before production work begins. Interactive prototypes tested gallery layout algorithms and max-width strategies against real screen-size data, revealing edge cases and helping evaluate which decisions hold up across conditions. ### Source excerpt The biggest shift AI has brought to design isn't that designers can now write production code. It's that we can finally generate evidence before committing to anything. There's a lot of conversation about AI affecting production: designers pushing code and shipping something closer to the final product. That matters. But the bigger opportunity is even [...] The post How I Validated Design Decisions Before Writing Production Code appeared first on Slack Design. ## Mini Shai-Hulud Hits AntV: 300+ Malicious npm Packages Published via Compromised Maintainer Account DevFeed: [Mini Shai-Hulud Hits AntV: 300+ Malicious npm Packages Published via Compromised Maintainer Account]() Original publisher: [Read original article]() Author: Liran Tal Published: 2026-05-18T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Data visualization](), [npm packages](), [Malware](), [npm](), [C2](), [Bun](), [JavaScript](), [GitHub]() Tags: [awareness](), [blog](), [c2](), [data-visualization](), [developer](), [devops](), [devrel](), [javascript](), [malware](), [npm-packages](), [payload](), [secrets](), [security](), [snyk-open-source](), [snyk-platform](), [supply-chain](), [supply-chain-security](), [teampcp](), [tokens]() ### AI overview This article analyzes the Mini Shai-Hulud supply-chain attack targeting the AntV data visualization ecosystem through a compromised npm maintainer account. It describes the mass publication of malicious package versions, the embedded Bun payload, credential and secret theft, C2 persistence, and self-propagation using stolen npm tokens. ### Source excerpt A compromised npm maintainer account triggered an automated burst of over 300 malicious package versions across 323 packages in the AntV data visualization ecosystem, part of the ongoing Mini Shai-Hulud supply chain worm campaign. Here's what the malware does, how to detect exposure, and how to respond. ## Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions DevFeed: [Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions]() Original publisher: [Read original article]() Author: João Bernardo Narciso Published: 2026-04-07T17:24:51Z Content type: article Language: en Sources: [Feedzai]() Topics: [Data visualization](), [data]() Tags: [data](), [data-visualization](), [dataviz](), [decision-making](), [design](), [feedzai](), [fraud](), [fraud-detection](), [patterns](), [research]() ### AI overview Feedzai's Data Visualization Research team is developing Cosmos Explorer, an interface that uses universe-inspired visual metaphors to help analysts examine patterns, trends, outliers, and possible fraud across hundreds of millions or billions of transactions. The project explores how to preserve meaningful details at very large scale while supporting data analysts and data scientists. ### Source excerpt Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions The Data Visualization Research team is developing Cosmos Explorer, an interface that leverages universe-related visual metaphors to convey information about the billions of transactions processed by Feedzai. Pedro Cruz, professor at Northeastern University, partnered with Feedzai to bring this idea to life by contributing with his creativity and expertise to solve this challenging visualization problem. https://medium.com/media/1b2ecfadd91d204640462f89fa6ff67f/href When we look out into the universe, we don't just see emptiness. We see an unimaginable scale: billions of galaxies, each containing billions of stars, each a point of light carrying its own story. No single observer can take it all in at once. Yet with the right instruments, patterns emerge: the structure of the cosmos itself becomes visible. In the digital realm, there is another universe just as vast and intricate. Every day, hundreds of millions of events flow through Feedzai's system which assesses them to protect consumers all over the world. Each one is a unique data point (e.g., a purchase, a login, a transfer). Individually, they don't tell us much. Together they form a living universe of behavior that represents the diversity in people's lives. But fraud lurks in everyday transactions, with criminals trying to hide their activities within the sheer volume of transactions. The question is: how can we represent those patterns meaningfully, the normal behaviors and the fraudulent behaviors, the trends and the outliers, to empower data analysts and data scientists in their decision-making processes? The biggest challenge is scale. No one can look at billions of events one by one. Aggregation helps, but it smooths over the details, which often encode the most interesting signals like the faint outlines of fraud or unusual clusters of activity. But what if we could see it all at once? Not just a summa ## February 2026 newsletter DevFeed: [February 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-02-19T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [Data visualization](), [Open Source](), [Streaming](), [anthropic](), [Claude](), [Conversational AI](), [Database](), [Python]() Tags: [anthropic](), [claude](), [clickhouse](), [conversational-ai](), [data](), [data-visualization](), [newsletter](), [python](), [real-time](), [streaming]() ### AI overview The February 2026 ClickHouse newsletter covers the company's Series D funding, its official Kubernetes operator release, data modeling and query optimization guidance, community work using ClickHouse with Claude and LibreChat, and upcoming training sessions and events. ### Source excerpt Welcome to the February 2026 ClickHouse newsletter, which will round up what's happened in real-time data warehouses over the last month. ## BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks DevFeed: [BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks]() Original publisher: [Read original article]() Author: João Palmeiro Published: 2026-01-19T14:52:55Z Content type: article Language: en Sources: [Feedzai]() Topics: [Accessibility](), [LLM evaluation / benchmarking](), [multimodal-ai](), [Canvas](), [AI Models](), [dataset](), [SVG]() Tags: [accessibility](), [ai-models](), [artificial-intelligence](), [benchmarking](), [canvas](), [clustering](), [data-visualization](), [dataset](), [large-language-models](), [multimodal](), [multimodal-ai](), [outlier-detection](), [svg]() ### AI overview The article introduces Benchmark It Yourself (BIY), an effort to prepare a dataset and benchmark multimodal AI models for scatterplot-related tasks. It examines generating distribution-aware descriptions from scatterplot images to improve the accessibility of canvas charts, including direct description generation and structured-data extraction for predefined templates. Initial results were mixed. ### Source excerpt Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks When we need to visualize and interact with millions, or even just thousands, of individual points while analyzing data, we typically resort to rendering them in the browser using a canvas. The other common approach for the web, SVG, doesn't scale when the number of individual elements increases to such quantities. However, while solving one problem, canvas charts introduce a new challenge: accessibility. Although SVG charts are not accessible by default, they can be by design. Each part of an SVG chart has a corresponding element on the web page, allowing for a programmable, accessible experience for screen reader users. We can simply think of SVG as HTML. On the other hand, a canvas chart is just like a PNG image. If a screen reader user tries to learn more about a canvas chart, unless the developer has prepared a detailed description of it, they will just hear the word "image". There's no way to get an idea of what one of these charts represents, let alone extract any insights. For static charts, the solution can be as simple as preparing a description and integrating it into the rendered chart. However, for platforms leveraging dynamic, large datasets, automatically generating these descriptions is not a simple task, especially for charts like scatterplots where data distributions can assume countless forms. At Feedzai, we started exploring ways to generate data distribution-aware descriptions for scatterplots from their respective images using recent multimodal AI models. When the raw data is not available, or the datasets are composed of several thousand or million instances, relying on chart images and these models becomes tempting. This combination has the potential to generate such descriptions and serve them alongside their respective charts, significantly improving the accessibility of canvas charts. That said, we focused on two main directions: using ## From text to charts: a faster way to visualize with ClickStack DevFeed: [From text to charts: a faster way to visualize with ClickStack]() Original publisher: [Read original article]() Author: ClickStack Team Published: 2025-10-22T11:41:42Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [Data visualization](), [log management](), [Traces](), [Language models](), [anthropic](), [API keys](), [data](), [Docker Compose](), [Open Source](), [Latency]() Tags: [ai](), [anthropic](), [api](), [clickhouse](), [data-visualization](), [docker](), [latency](), [logs](), [metrics](), [observability](), [open-source](), [root-cause-analysis](), [traces]() ### AI overview ClickStack introduces a text-to-chart feature for observability data. Users describe a desired chart in natural language, and an LLM converts the prompt into a query and visualization for logs, traces, or metrics. The article explains how to enable the feature with an Anthropic API key, try it locally with a Docker image, or use a public demo, while noting that the local image is intended for experimentation rather than production. ### Source excerpt Discover how ClickStack's new text-to-chart feature makes analyzing logs, traces, and metrics effortless - turning plain text into instant observability visualizations that speed up root cause analysis. ## app.build Can Now Build Python Data Apps DevFeed: [app.build Can Now Build Python Data Apps]() Original publisher: [Read original article]() Author: Arseni Kravchenko Published: 2025-07-18T15:08:01Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [dashboards]() Tags: [agent](), [dashboards](), [data-visualization](), [databricks](), [postgresql](), [product](), [python](), [react](), [tailwind]() ### AI overview app.build adds support for generating Python data applications using NiceGUI. The announcement describes dashboards and ML demos, optional Python-library customization, PostgreSQL persistence, and planned optional Databricks Unity Catalog integration. ### Source excerpt When we started working on app.build, we knew in the longer run we wanted to build a generic agent that could build apps with different "coding stacks". However, for our first release and initial announcement, we could only build apps written with a single fixed stack: In the rec... ## Visualizing Foursquare places with ClickHouse DevFeed: [Visualizing Foursquare places with ClickHouse]() Original publisher: [Read original article]() Author: Alexey Milovidov Published: 2025-05-08T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Data visualization](), [datasets](), [real-time](), [Command-line interface](), [Web]() Tags: [apache](), [clickhouse](), [command-line](), [data-visualization](), [datasets](), [hackathon](), [real-time](), [web]() ### AI overview The article describes a data visualization hackathon project that uses ClickHouse to explore and display a large Foursquare places dataset. It covers previewing and querying the data with clickhouse-local, loading it into ClickHouse, optimizing geographic coordinates and indexes, and adapting a web visualization tool for interactive map analysis. ### Source excerpt Visualizing geographical datasets with ClickHouse in real-time ## Introducing Heroku-Streamlit: Seamless Data Visualization DevFeed: [Introducing Heroku-Streamlit: Seamless Data Visualization]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2025-05-01T08:00:09Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Streamlit](), [Template](), [Python](), [web applications](), [Cloud](), [Data visualization](), [Data Science](), [Open Source]() Tags: [ai](), [data-analytics](), [data-visualization](), [deploy](), [deployment](), [developer-tools](), [dockerignore-usage](), [getting-started](), [heroku](), [news](), [open-source](), [prototyping](), [python](), [streamlit](), [web-applications]() ### AI overview Heroku announces Heroku-Streamlit, a ready-to-deploy template for deploying interactive Streamlit data visualization applications on Heroku. The template combines Heroku's cloud platform with Streamlit's Python framework and includes one-click deployment, sample Uber pickup data, customization options, and manual deployment instructions. ### Source excerpt We're excited to announce the release of Heroku-Streamlit, a template that makes deploying interactive data visualization applications on Heroku simpler than ever before. Streamlit is an open-source app framework built for machine learning and data science projects. This Streamlit App brings together Heroku's scalable cloud platform and Streamlit's intuitive Python-based data application framework. Whether you're [...] The post Introducing Heroku-Streamlit: Seamless Data Visualization appeared first on Heroku. ## Enabling Apache ECharts in React for Data Visualization DevFeed: [Enabling Apache ECharts in React for Data Visualization]() Original publisher: [Read original article]() Author: hello@taniarascia.com Published: 2025-03-31T00:00:00Z Content type: tutorial Language: en Sources: [Tania Rascia]() Topics: [Data visualization](), [React](), [Front end](), [JavaScript](), [dashboards]() Tags: [charts](), [dashboards](), [data-visualization](), [front-end](), [javascript](), [react](), [visualization]() ### AI overview A tutorial on creating a small React wrapper for Apache ECharts to build interactive data visualizations, including line, bar, and pie charts. It covers chart initialization, configuration updates, event handling, and resizing. ### Source excerpt Making dashboards with charts and graphs is a pretty common part of the front-end developer experience, as well as deciding which JavaScript... ## "Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text DevFeed: ["Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text]() Original publisher: [Read original article]() Author: Beatriz Feliciano Published: 2024-11-22T18:35:14Z Content type: article Language: en Sources: [Feedzai]() Topics: [Transactions](), [data](), [synthetic-data](), [Tool]() Tags: [data](), [data-analysis](), [data-visualization](), [financial-fraud](), [fraud](), [fraud-detection](), [fraud-investigation](), [image](), [interface](), [research](), [synthetic-data](), [transactions]() ### AI overview The article describes a fraud-analysis tool that combines charts and text to help analysts review suspicious financial transactions. It explains that tabular review can make it difficult to identify patterns and anomalies within a 1-to-5-minute review window, and presents an interface using synthetic data to help analysts prioritize investigation areas. ### Source excerpt Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers. However, the impacts of this issue stretch beyond someone's finances. It can also impact a victim's life in many dimensions. Detecting and quickly acting upon suspicious transactions is essential to tackle this problem. Finding Fraud Through Data Tables To review the data of alerted transactions, analysts look at information in tabular format (similar to what is presented in Figure 1), scrolling through it to assess past activity patterns of the alerted person and comparing those with the alerted event. "How much money was spent on average on past transactions?" or "Is that significantly different from the amount on the current alert?" are some questions they might try to answer during their review. Figure 1: Image of a table that analysts typically use to review the data of alerted transactions. The issue with this approach is that finding groups of patterns and anomalies in tabular data can be overwhelming since it requires an increased cognitive load from analysts to interpret the data effectively. This becomes even more complex since these professionals must review and classify the alerted transaction in a short time -- between 1 and 5 minutes. Revamping the analysis To solve this problem, we present a tool that combines charts and text to guide the analysis of financial transactions. As presented in Figure 2, the tool (populated with synthetic data) is divided into three regions that provide different levels of information detail -- from the most high-level to the most detailed. The goal is that the analyst can scan the charts and prioritize their review towards specific areas of the alert. Figure 2: Proposed interface composed of multiple regions: the Knowledge Area Console (A) to detect suspicious areas of the analysis; the Knowledge Are ## Chat with Neon Postgres using natural language DevFeed: [Chat with Neon Postgres using natural language]() Original publisher: [Read original article]() Author: Sheldon Niu Published: 2024-07-19T13:28:59Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [AI Chat]() Tags: [ai](), [community](), [data](), [data-visualization](), [database](), [databases](), [dev](), [development](), [errors](), [gpt](), [integration](), [memory](), [operations](), [postgres](), [production](), [sql](), [support](), [tool](), [tools](), [update](), [visualization]() ### AI overview AskYourDatabase lets users interact with Neon Postgres databases in natural language. It generates and executes SQL, explains results, and can visualize data; Neon branches let teams use an isolated development copy instead of production. ### Source excerpt Interacting with SQL databases can be challenging for non-technical team members, often requiring custom-built GUI tools. If you're not an expert, writing SQL queries can become a barrier to accessing data--this is where AI can help. What is AskYourDatabase? AskYourDatabase is an... ## Navigate your Turso database with Outerbase DevFeed: [Navigate your Turso database with Outerbase]() Original publisher: [Read original article]() Author: Brandon Strittmatter Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Data Management](), [SQLite](), [Data visualization](), [dashboards](), [SQL](), [Artificial Intelligence]() Tags: [ai](), [dashboards](), [data-management](), [data-visualization](), [database](), [sql](), [sqlite](), [turso]() ### AI overview This article explains how to connect a Turso database to Outerbase Studio and use it to view, edit, query, and visualize data. It describes table and schema editing, direct SQL queries, AI querying, dashboards, data visualization, and collaboration features. ### Source excerpt Query, edit, vizualise your Turso Database and more with Outerbase ## A behind-the-scenes look at building interactive analysis capabilities in Benchling DevFeed: [A behind-the-scenes look at building interactive analysis capabilities in Benchling]() Original publisher: [Read original article]() Author: Wonja Fairbrother Published: 2024-06-11T13:01:25Z Content type: article Language: en Sources: [Benchling]() Topics: [data-processing](), [web applications](), [Amazon EKS](), [Amazon S3](), [API](), [Back end](), [Front end](), [JSON]() Tags: [apache-arrow](), [apache-parquet](), [api](), [architecture](), [backend](), [data](), [data-analysis](), [data-transformation](), [data-visualization](), [duckdb](), [frontend](), [json](), [real-time](), [s3](), [storage](), [systems]() ### AI overview This article explains the architecture and design decisions behind Benchling's Interactive Analysis capabilities. The system lets scientists select data from multiple sources, transform and analyze it in real time, and visualize results within Benchling. Its architecture uses the Benchling web application, a stateless service running on EKS, temporary S3 storage, and a JSON-based transformation API. ### Source excerpt Authors: Wonja Fairbrother and Eli Levine Science is iterative. To design the next experiment, scientists need to analyze the results of previous ones. Interactive Analysis in Benchling allows scientists to perform real-time data transformation, visualization, and analysis without having to transfer it into other systems. In this post we will describe the architecture behind interactive analysis capabilities in Benchling and give a peek into the decision journey we took along the way¹. Interactive Analysis allows scientists to: 1. Select data from many sources: Benchling entity and results data Instrument data Notebook tables Data upload via both API and UI 2. Transform, visualize, and analyze data in real time, without leaving Benchling: Data transformations: filtering, aggregations, window functions, etc. Visualizations: line chart, bar chart, scatter plot, etc. Scientific analysis methods: IC50 and various curve fitting functions Overall architecture The architecture backing Interactive Analysis consists of: The Benchling web application An auto-scaling stateless internal service running on EKS that performs the transformations Temporary S3 storage locations for input and output data, shared between the web app and the service The frontend of the application is responsible for taking in input datasets and transformation configurations from users. The backend of the web application collects all the input data from the appropriate sources, serializes and uploads the data to S3, and sends a synchronous transformation request to the service. The service's API consists of one main endpoint that takes in a JSON payload of transformation parameters. The service can accept a single transformation, or a list of many transformations to perform. In this endpoint, the service downloads and deserializes the input data, performs the transformation with an analysis engine, and serializes and uploads the resulting data to S3. Each request spins up its own self-contained in-memor ## Add an interface to your Neon database via Outerbase DevFeed: [Add an interface to your Neon database via Outerbase]() Original publisher: [Read original article]() Author: Brandon Strittmatter Published: 2024-06-07T15:02:35Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [SQL](), [Data visualization](), [AI Models](), [Data Management](), [dashboards]() Tags: [announce](), [community](), [dashboards](), [data-management](), [data-studio](), [data-visualization](), [database](), [exploration](), [filter](), [generate](), [guide](), [how-to](), [integration](), [interface](), [language](), [models](), [postgres](), [sorting](), [sql](), [team](), [visualization]() ### AI overview The article announces the public availability of the Outerbase integration for Neon. It lets teams connect Neon Postgres databases to Outerbase Data Studio to collaboratively view, edit, query, manage, and visualize data. Outerbase AI can generate SQL queries, schemas, data, and visualizations from natural-language requests. ### Source excerpt We are beyond excited to announce that the Outerbase integration for Neon is now publicly available. This integration enables you to instantly connect your Neon Postgres database to Outerbase's Data Studio and invite your team to view, edit, query, and visualize your data with yo... ## Interactive Data Visualizations with React DevFeed: [Interactive Data Visualizations with React]() Original publisher: [Read original article]() Author: admin Published: 2024-06-02T08:26:04Z Content type: tutorial Language: en Sources: [Posts on Chris Banes]() Topics: [React](), [Data visualization](), [JavaScript](), [User Interfaces]() Tags: [charts](), [charts-and-graphs](), [d3-js](), [data-visualization](), [javascript](), [react](), [uncategorized]() ### AI overview This tutorial explains how React can be used to create interactive data visualizations. It discusses component-based architecture, dynamic updates through the Virtual DOM, performance, flexibility, and the React visualization ecosystem, including Chart.js and D3.js. ### Source excerpt React, the popular JavaScript library for building user interfaces, is a fantastic tool for creating interactive data visualizations. With its component-based architecture and a wealth of libraries at your disposal, React makes it easier than ever to bring your data to life and engage your audience. But why should you choose React for your data [...] ## Real-time Data Visualization: How to build faster dashboards DevFeed: [Real-time Data Visualization: How to build faster dashboards]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2023-08-08T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [Data visualization](), [real-time](), [dashboards]() Tags: [build](), [dashboards](), [data-visualization](), [how-to](), [real-time](), [scalable-analytics-architecture]() ### AI overview This tutorial introduces real-time data visualization, focusing on turning raw data streams into immediate insights and reducing latency in dashboards. ### Source excerpt Real-time data visualization turns raw streams into instant insights. Most tools add too much latency. Here's how to keep it fast. ## Trino Fest nears with an all-star lineup DevFeed: [Trino Fest nears with an all-star lineup]() Original publisher: [Read original article]() Author: Cole Bowden Published: 2023-06-01T00:00:00Z Content type: news Language: en Sources: [Trino Blog]() Topics: [SQL](), [Apache Iceberg](), [Redis](), [Python](), [Caching](), [Latency](), [Data visualization]() Tags: [connectors](), [data](), [data-visualization](), [ecosystem](), [event](), [free](), [latency](), [python](), [redis](), [speakers](), [sql](), [virtual-event]() ### AI overview The article previews the speaker lineup for Trino Fest, a free, two-day virtual event. Featured topics include new data-source connectors, Python tools that integrate with SQL and Trino, Iceberg data lakes, query monitoring, caching, Hudi indexing, and query-latency optimization. ### Source excerpt Trino Fest is just around the corner! We're only two weeks away, and we're excited to share that we've got an incredible speaker lineup with a wide variety of talks about all things Trino. If you're out of the loop, we announced Trino Fest back in April as a two-day, free, virtual event. If you want to attend, see talks live, engage with our speakers in Q&As at the end of each session, you'll need to register, so don't delay, and... Register to attend! With that said, we're also excited to bring you a preview of our exciting speaker lineup. Read on if you'd like to learn more. ## What Power BI is still missing DevFeed: [What Power BI is still missing]() Original publisher: [Read original article]() Published: 2023-03-27T00:00:00Z Content type: opinion Language: en Sources: [Mutt0-ds Notes]() Topics: [Data visualization](), [Git](), [Azure](), [JSON](), [Microsoft](), [GitHub]() Tags: [azure](), [data-visualization](), [git](), [github](), [json](), [microsoft]() ### AI overview An assessment of Power BI at the enterprise level identifies features that remain difficult or unavailable, including basic version control for binary .pbix files. The article describes a workaround using an Azure Pipeline, Tabular Editor 2, JSON metadata, and Git, while noting that charts and visuals are not covered. ### Source excerpt During these months, I had the chance to take a look at Power BI at the enterprise level, juggling advanced requests from hundreds of users... [Next page]()