David is a CS major who worked in Apple’s music group in the 90s and went on to become CEO of eMusic in the aughts.
At Venrock, David invested in early-stage crypto, consumer, and enterprise tech companies. He was early to crypto as a node maintainer on the Bitcoin blockchain and an Ethereum miner, setting up a rig in his basement several years ago.
At CoinFund, he focuses on early- and growth-stage crypto and blockchain companies and technologies like Upshot, a platform for crowdsourced NFT appraisals, and Rarible, a digital art NFT platform.
Take2 is a New Zealand program that teaches incarcerated people to code: building marketable skills, opening up employment opportunities, and dramatically reducing recidivism. At the time of writing, Take2 has a 100% success rate in preventing recidivism.
Expensify is an expense management solution that integrates with your travel, ERP, and finance/accounting software. Check out their full list of integrations.
Expensify engineers rely on Stack Overflow for Teams to make knowledge accessible and shareable, rather than wading through swathes of documentation. Read the case study.
Flat organizations like Expensify have minimal or no middle management, meaning there’s no management layer between staff and executives. A similar model for decentralized management is Holacracy.
Ceora shouts out Mermaid, a JavaScript-based diagramming and charting tool that creates diagrams dynamically based on Markdown-inspired text definitions.
Coinbase’s bouncing QR code ad proved so popular it crashed the app. Considered passé pre-pandemic, QR codes have obvious value now: they’re touch-free, easy to scan, and ubiquitous. (Just don’t call it a comeback.)
In preparation for his move from New Zealand to Canada, Matt is overhauling his hardware and transitioning to an M1 MacBook Pro for performance and efficiency.
These days, every company looking at analyzing their data for insights has a data pipeline setup. Many companies have a fast production database, often a NoSQL or key-value store, that goes through a data pipeline.The pipeline process performs some sort of extract-transform-load process on it, then routes it to a larger data store that the analytics tools can access. But what if you could skip some steps and speed up the process with a database purpose-built for analytics?
On this sponsored episode of the podcast, we chat with Rohit (Ro) Amarnath, the CTO at Vertica, to find out how your analytics engine can speed up your workflow. After a humble beginning with a ZX Spectrum 128, he’s now in charge of Vertica Accelerator, a SaaS version of the Vertica database.
Vertica was founded by database researcher Dr. Michael Stonebreaker and Andrew Palmer. Dr. Stonebreaker helped develop several databases, including Postgres, Streambase, and VoltDB. Vertica was born out of research into purpose-built databases. Stonebreaker’s research found that columnar database storage was faster for data warehouses because there were fewer read/writes per request.
Here’s a quick example that shows how columnar databases work. Suppose that you want all the records from a specific US state or territory. There are 52 possible values here (depending on how you count territories). To find all instances of a single state in a row-based DB, the search must check every row for the value of the state column. However, searching by column is faster by an order of magnitude: it just runs down the column to find matching values, then retrieves row data for the matches.
The Vertica database was designed specifically for analytics as opposed to transactional databases. Ro spent some time at a Wall Street firm building reports—P&L, performance, profitability, etc. Transactions were important to day-to-day operations, but the real value of data came from analyses that showed where to cut costs or increase investments in a particular business. Analytics help with overall strategy, which tends to be more far-reaching and effective.
For most of its life, Vertica has been an on-premises database managing a data warehouse. But with the ease of cloud storage, Vertica Accelerator is looking to give you a data lake as a service. If you’re unfamiliar, data lakes take the data warehouse concept—central storage for all your data—and remove limits. You can have “rivers” of data flowing into your stores; if you go from a terabyte to a petabyte overnight, your cloud provider will handle it for you.
Vertica has worked with plenty of industries that push massive amounts of data: healthcare, aviation, online games. They’ve built a lot of functionality into the database itself to speed up all manner of applications. One of their prospective customers had a machine learning model with thousands of lines of code that was reduced to about ten lines because so much was being done in the database itself.
In the future, Vertica plans to offer more powerful management of data warehouses and lakes, including handling the metadata that comes with them. To learn more about Vertica’s analytics databases, check out our conversation or visit their website.
It’s not news that, as Cassidy says, “remote has grown wildly fast”—but Remote has gone from about 25 employees in March 2020 to 900 now (a 3,500% increase).
Ceora explains to Matt (oh, sweet summer’s child) what it means to get ratioed on Twitter.
Inspired by a great read, the team discusses how Gen Z, having grown up without floppy disks, file folders, or directories, thinks about information.
SphereEX builds distributed data systems, making it easier for organizations to load balance massive data stores across multiple servers.
Now that open-source software has taken over Western software, it’s China’s turn. Even big companies like Baidu and Bytedance are opening up their projects.
Trista is the only female Apache member in China, which is both an honor and a demonstration of how much work needs to be done to support women in STEM.
This episode’s Lifeboat badge shoutout goes to swati kiran for her answer to
