Because the world needs better dashboards
While my own professional interests in Building Better Data Insights Faster rely on using visuals and narratives to show data-driven results, “Starting from first principles” is the question you have to ask. Identifying the quality data sources, the time to delivery and the confidence of accuracy are critical aspects of any dashboard.
Source: WrapText by Equals
This is the second article I’ve read about Equals in a week, and while I’m not ready to go back to a spreadsheet, this company has some great previous posts with excellent content, such as the 2023 summary and How to ship fast. An appropriate statement would be.
What a year. We embraced AI. We reimagined BI. We waved freemium goodbye. And as the cliché goes, we’re only just getting started.
[Last Week in AWS] Issue #352: New Year, New You, Here’s December in Review
Damm right, I think you are giving too much created by saying “a year”. More than once I had to rewrite code because AWS was years behind standard Python releases. AWS Lambda adds support for Python 3.12.
Whatever was going on with the delays in getting new language runtimes out a year or more after the language version itself was released seems to have been resolved. I wonder how long it’ll take that unpleasant chapter to fade from the collective awareness around Lambda?
Source: Last Week in AWS
Latency is the new outage
While technically a video that I listened to with Getting Started with ElastiCache for Redis Performance & Cost Optimization, this needs to be a slogan used more frequently. It is so true. The speaker in the opening minutes also describes some compelling reasons why our proliferation of data can contribute to a negative impact.
Source: Random AWS reading.
About “Things I Read About Recently”
Most days I take some time early in the morning to scan my inbox newsletters, the news, LinkedIn or other sources to read something new covering the professional and personal topics of interest. Turning what I read into some actionable notes in a short committed time window is a summary of what I learned, what I should really learn and use, or what is of random interest.
Some of my regular sources include TLDR, Forbes Daily, ThoughWorks Podcasts, Daily Dose of Data Science and BoringCashCow. Also Scientific American Technology, Fareed’s Global Briefing, Software Design: Tidy First? by Kent Beck, Last Week in AWS to name a few.