Top 10 Python Libraries for Data Engineering in 2026
Want to level up your data engineering toolkit? Here are some Python libraries that'll make your pipelines faster, cleaner, and easier to maintain.
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This article is about the gap between what candidates prepare for and what companies actually need right now.
Read full articleWant to level up your data engineering toolkit? Here are some Python libraries that'll make your pipelines faster, cleaner, and easier to maintain.
Anthropic has acquired Stainless, a startup that generates SDKs, command-line tools, and MCP servers from API specifications, in a move analysts say targets the “last mile” of developer experience. Founded in 2022 by former Stripe engineer Alex Rattray, Stainless converts API specifications into production-ready SDKs across languages, including Python, TypeScript, Kotlin, Go, and Java. Stainless does not sell primarily to enterprises, but its tools form part of the software development chain that enterprise teams may rely on. They help generate SDKs, documentation, and MCP servers that developers can use to connect AI models, cloud services, and APIs to business applications. In a statement, Stainless said it will wind down all hosted products, including its SDK generator, as the team shifts focus to Claude Platform capabilities and connecting agents to APIs. Existing customers will retain the right to modify and extend SDKs they have already generated. This could have competitive impl
In the world of data science, SQL still remains the powerful tool for defining the data, data manipulation, data aggregation and data analysis. While basic SQL commands are very fundamental, and everyone knows about it. If you want to be the unique in the crowd then you should know advanced features like window functions that […] The post 40 Advanced SQL Window Functions Every Data Scientist Must Know(with examples) appeared first on Analytics Vidhya.
The 25th-largest Bitcoin treasury company acquired $15 million worth of BTC as one of only four treasury firms to announce a corporate Bitcoin investment during May.
Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach production. The post LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships appeared first on Towards Data Science.
In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with […] The post How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context appeared first on MarkTechPost.
The post Here’s What Stocks Donald Trump Traded So Far This Year appeared on BitcoinEthereumNews.com. Topline President Donald Trump drew scrutiny Friday over millions of dollars of securities trades made in recent months involving companies his administration has also made deals with — though Trump’s son Eric said the trades were made by a blind trust. U.S. President Donald Trump answers a question from a reporter during an event on maternal healthcare in the Oval Office of the White House on May 11, 2026 in Washington, DC. Photo by Kevin Dietsch/Getty Images Key Facts The president made purchases ranging from $1 million to $5 million in tech giants such as Oracle, Microsoft, Nvidia, Meta, Amazon, Apple and Alphabet—all of which have inked government contracts or made high-profile commitments — according to financial disclosure forms. Trump’s other large tech investments ranging between $1 million and $5 million included software company ServiceNow, semiconductor manufacturer Broadcom
In this article, we will explore five fundamental concepts that every Python developer should have in their toolkit.