When I first started my role as an analytics engineer, I was tasked with rewriting a bunch of data models that were written in the past by contractors. These models were taking over 24 hours to run and often failed to run at all. They were poorly thought out and contained a bunch of “quick fix” code rather than being designed with the entire flow of the model in mind.
It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.
Well, that was interesting! I just finished the Show Floor Showdown for Business Intelligence at Gartner Data & Analytics Summit with team ThoughtSpot. (Reminder: ThoughtSpot was named a Visionary in Gartner’s 2022 Magic Quadrant™ for Analytics and BI Platforms.) For the setup, we were asked to play the roles of sustainability experts, government officials, and business leaders working to understand the impact of sustainability goals on economic, environmental, and social outcomes.
Over the past week or so, I’ve been working on updating our Developer Workshop content. One of the trickiest parts of running workshops is the differences in local environment configuration: some attendees have a Mac, others windows, some with admin permissions, and some without. So much depends on what your company provides and how they manage their systems. To make things easier, I’ve been relying on CodeSandbox to eliminate a lot of the unknown.
One of the most significant benefits of the modern data stack is the loosely coupled nature of each layer to help you adapt to change and capitalize on new business opportunities. You can choose the best solution which fits your need without long-term vendor commitments, and the risk of introducing complex integrations and IT management. One of the ways to achieve this loose coupling is through webhooks.
Many businesses rely on Amazon Redshift Serverless for their cloud data warehouse and ThoughtSpot to derive insights from the data stored within. For this blog, I’m going to show you how to create a connection between Amazon Redshift Serverless and ThoughtSpot. It’s easy to connect Redshift with ThoughtSpot whether you have it running as a cluster which you have provisioned, or serverless.
Are you considering venturing into the world of analytics engineering? Analytics engineers are the newest addition to data teams and sit somewhere between data engineers and data analysts. They are technical, business savvy, and love to learn. A huge part of an analytics engineer’s role is learning new modern data tools to implement within data stacks.
If you have managed a cloud data platform, you have undoubtedly gotten that call. You know the one, it's usually from finance or the office of the CFO, inquiring about your monthly spend. And it usually comes in one of two forms: While both are clear and present dangers to cloud data platform owners, they don’t have to be.
Companies are tightening the belts with one exception: data. The modern data stack shows no signs of a recession with Frank Slootman the CEO of Snowflake saying at their annual user conference they will not slow hiring plans. CNBC Tech Council executives noted how technology investments are business drivers, not cost centers. And Foundry’s annual cloud computing survey cites cloud analytics as a top growth driver. A digital economy is fueling this growth in the data sector.
