Zing Data

Dashboard and question linking is live! So you can have a question and link to note about the methodology behind the analysis, or link to frequently asked questions, a table of contents, and more. And of course you can click into any data point to create a new analysis, see underlying values, or export data. more intelligent business intelligence https://lnkd.in/gizD_vRB

Zing's updated React SDK supports dashboard embedding, chart embedding, and user-aware attributes. NPM install from here: https://lnkd.in/gmWpBxBt and learn more here: https://lnkd.in/gZJBRyys

At the Small Data SF conference yesterday, was sharing Zing Data with folks only to find that some people already had it installed! We’ve long had this vision that you should be able to access data anywhere – on the web, mobile, or embedded within your own applications. Seeing Zing on people’s phones out in the wild is…wild! ( this photo isn’t my phone, and the app was already installed before the conference! ) Start free at www.zingdata.com

Embeddable dashboards are now part of our React / Javascript SDK, allowing you to visually create dashboards on real-time data, and immediately have those appear to your customers in a secure (authenticated urls), user-aware (full row level security), and beautiful way thats easy to include in your own application in minutes. You can embed individual charts and data tables, or you can embed a whole dashboard with a single embed id. Support for: * Bar Chart * Stacked Bar Chart * Stacked Bar (100%) Chart * Horizontal Bar Chart * Line Chart * Stacked Line Chart * Area Chart * Donut Chart * Big Number * Scatter Plot * Combo Chart (Line + Bar) * Data Table Dashboard elements can be created with natural language, visually, or with SQL and you can have the data that feeds into them be a real-time query, or choose a cache update frequency from one minute to one month to minimize database load. Get started here: https://lnkd.in/d-iUrB6v

"how do I set up guardrails for people not familiar with the data?" its important for people to get reliable answers. Thats where Zing's guided analysis feature comes in. It matches a users search to a structured analysis (still with user-specified inputs) so they are getting trusted, known queries but with their own inputs / parameters. We've now made it flexible to optional inputs, single vs. multiselect, aliases (so "mean time per trip" vs. "typical time per trip by station" vs. "common duration by departing station" can all be searchable for the same analysis). Its available now, and is already being used by data teams to deliver trusted, reliable, guided results alongside Zing's more flexible ad-hoc AI capabilities. https://lnkd.in/gzwDkAAj

Guided analyses in Zing Data provides an easy, trusted, structured analysis path with the full power of SQL and even CTEs and subqueries. This lives alongside ad-hoc and flexible analysis in Zing. 📊 What You'll Learn: * How to set up guided analyses in Zing Data * The benefits of using guided analyses for your team * Step-by-step process of creating a guided analysis * Tips for crafting effective SQL queries for guided analyses * How to incorporate dynamic fields and multi-select options 🔍 Why Guided Analyses? Guided analyses allow you to create pre-defined paths for data exploration, making it easier for non-technical team members to access and interpret complex data without needing to write SQL. This feature combines the flexibility of custom SQL with an intuitive user interface, opening up new possibilities for data-driven decision making across your organization. 🛠️ Tutorial Highlights: * Accessing the Data Sources menu * Setting up a new example analysis * Defining dynamic fields for user input * Writing SQL queries with dynamic field integration * Testing and refining your guided analysis * Using the search bar to access guided analyses * Combining guided analyses with natural language queries Want to learn more? Check out our blog post for an in-depth look at guided analysis: https://lnkd.in/gFBnZcas

We're joining forces with Southeastern Railway, London North Eastern Railway and TransPennine Express as part of Future Labs to help accelerate ideas that address common and emerging issues for the sector. In the first scheme of its kind, successful technology startups will receive unique insight and mentorship during a 12-week programme to bring their products and services to life. More than 100 companies applied to be part of Future Labs, each proposing innovative solutions and products which address four categories: 👍 Enhancing customer experience 🚆 Improving performance and operational excellence 🙋 Developing people and talent 💫 A wildcard category The finalists have been selected by rail industry leaders following a pitch day in York earlier this month. Chata.ai Cleancore Intelligence 1Huddle iqast - intelligent forecasting software Cognition24 Zing Data Treeva PotentialU Moonbility Find out more about them here: https://bit.ly/3YWVKqd

With Guided Analysis, you can help guide users through a trusted pre-created analysis where they can specify relevant inputs to generate forecasts, filter values, and more. You get the full power of CTEs, subqueries, and row-level access controls with the ease of use a search box and typeahead. And its 100% verified and reliable, while still being flexible to input values. Available now: https://lnkd.in/gFBnZcas

From Snowflake's own documentation, "Joins aren’t directly supported in the semantic model. Instead, use views that contain pre-joined data." ( https://lnkd.in/gTPbGuqT) Joins are how you answer any question of even moderate complexity, like: * how much money did customers return last month, or * which high value leads visited our website in the past week Pre-creating every view to answer those questions means you still need to create those views ahead of time -- hobbling the usefulness of Cortex. Here's how Zing handles joins on the fly between tables in a trusted, reliable, and seamless way - all from a natural language query. https://lnkd.in/gPfgUnwN

We're #hiring for a Senior Software Engineer at Zing. If you're amazing at NodeJS, familiar with databases, and have strong React chops we'd love to review your application! You'll work with a smart, nimble team and the latest LLMs to solve problems for leading companies, fast-growing startups, and government agencies trying to run more efficiently. Preference for candidates in San Francisco Bay Area, or Boston MA although we're open outstanding candidates in the US, Canada, or Mexico outside these cities. DM me, or better yet, apply here: [no agencies / recruiters / dev shops]