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Building cost effective data pipelines with Python & DuckDB

Start Data Engineering

Project demo 3. Building efficient data pipelines with DuckDB 4.1. Introduction 2. Use DuckDB to process data, not for multiple users to access data 4.2. Cost calculation: DuckDB + Ephemeral VMs = dirt cheap data processing 4.3. Processing data less than 100GB? Use DuckDB 4.4.

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Is the “AI developer”a threat to jobs – or a marketing stunt?

The Pragmatic Engineer

A first, smaller wave of these stories included Magic.dev raising $100M in funding from Nat Friedman (CEO of GitHub from 2018-2021,) and Daniel Gross (cofounder of search engine Cue which Apple acquired in 2013,) to build a “superhuman software engineer.” But there is none of this. And COBOL was just one of many attempts.

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Building an an Early Stage Startup: Lessons from Akita Software

The Pragmatic Engineer

In this issue, we cover: How Akita was founded On cofounders Raising funding Pivoting and growing the company On hiring The tech stack The biggest challenges of building a startup For this article, I interviewed Jean directly. People loved our fuzzer demos. So we started to build API specs on top of our API security product.

Building 207
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Defining Simplicity for Enterprise Software as “a 10 Year Old Can Demo it”

Cloudera

During the development of Operational Database and Replication Manager, I kept telling folks across the team it has to be “so simple that a 10 year old can demo it”. so simple that a 10 year old can demo it”. Watch this: Enterprise Software that is so easy a 10 year old can demo it. How hard is it for engineering to build?

IT 98
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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.

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5 Reasons to Attend BUILD 2023: The Dev Conference for AI & Apps

Snowflake

BUILD 2023 is where AI gets real. Join our two-day virtual global conference and learn how to build with the app dev innovations you heard about at Snowflake Summit and Snowday. We have more demos and hands-on virtual labs than ever before—and you won’t find a bunch of slideware here.

Building 125
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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

In this last installment, we’ll discuss a demo application that uses PySpark.ML For more context, this demo is based on concepts discussed in this blog post How to deploy ML models to production. With this example as inspiration, I decided to build off of sensor data and serve results from a model in real-time.