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Power your multifamily rent surveys with real-time data on over 25M units nationwide, sourced entirely from property websites and public data sources.

Data pipelines built (and added on to) without a solid foundation will suffer from poor efficiency, slow development speed, long times to triage production issues, and hard testability. What if your data pipelines are elegant and enable you to deliver features quickly? An easy-to-maintain and extendable data pipeline significantly increase developer morale, stakeholder trust, and the business bottom line! Using the correct design pattern will increase feature delivery speed and developer value (allowing devs to do more in less time), decrease toil during pipeline failures, and build trust with stakeholders. This post goes over the most commonly used data flow design patterns, what they do, when to use them, and, more importantly, when not to use them. By the end of this post, you will have an overview of the typical data flow patterns and be able to choose the right one for your use case.

Over the past month, we finished a few pilots where the primary feedback was that our product was “too detailed” for on-site management teams. Here's how we found out why, and fixed the problem in 7 days.

Make real-time data your competitive advantage!

With the Python compiler Nuitka, you create protected binaries from your Python source code.

A catalog of patterns to better understand, communicate, and teach the design of distributed systems

ISE Engineering Fundamentals Engineering Playbook

ISE Engineering Fundamentals Engineering Playbook

ISE Engineering Fundamentals Engineering Playbook

ISE Engineering Fundamentals Engineering Playbook

An object that carries data between processes in order to reduce the number of method calls.

If you want to split address in Excel, this comprehensive guide is your ticket to mastering it as You’ll explore through different functions.

Use piped workflows for efficient data cleaning and visualization.

Explore the intricacies of design patterns in functional programming, gaining insights into their implementation and benefits.

Best practices with R around select topics.

Editor's Note: This post was written by Chris Pappalardo, a Senior Director at Alvarez & Marsal, a leading global professional services firm. The standard processes for building with LLM work well for documents that contain mostly text, but do not work as well for documents that contain tabular data (like spreadsheets). We wrote about our latest thinking on Q&A over csvs on the blog a couple weeks ago, and we loved reading Chris's exploration of working with csvs and LangChain using agents, chai

Use LLMs in Excel formulas

LLMs are great at generating text, but how do you get them to generate structured output?

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Explore how Agentic AI for data management enhances automation, governance, and decision-making by leveraging intelligent workflows, real-time insights

A package written for R focused on baseball analysis. Currently in development. - BillPetti/baseballr

The goal of the fantasypros R package is to provide easy and reproducable access to data provided on the fantasypros website. The intital focus is on NFL and fantasy football data, but other sports are planned to be added

Create custom Streamlit and Shiny R apps effortlessly with AI assistance. Design, code, and deploy data apps in minutes.

Learn how to implement strong authentication and SSO in Shiny apps with Descope. This guide integrates both OIDC and SAML with Posit Connect for seamless login.

In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. The infrastructure builds upon the rook specification and is thus well suited to be combined with httpuv based web servers.

Send error reports to the Google Error Reporting service and view errors and assign error status in the Google Error Reporting user interface.

Seamless SSO for R applications