Flexible Network Security Detection And Response With Grapl - Episode 281

September 22, 2020 Tobias Macey Comments Off on Flexible Network Security Detection And Response With Grapl - Episode 281

Servers and services that have any exposure to the public internet are under a constant barrage of attacks. Network security engineers are tasked with discovering and addressing any potential breaches to their systems, which is a never-ending task as attackers continually evolve their tactics. In order to gain better visibility into complex exploits Colin O'Brien built the Grapl platform, using graph database technology to more easily discover relationships between activities within and across servers. In this episode he shares his motivations for creating a new system to discover potential security breaches, how its design simplifies the work of identifying complex attacks without relying on brittle rules, and how you can start using it to monitor your own systems today.

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Simplified Data Extraction And Analysis For Current Events With Newspaper - Episode 280

September 15, 2020 Tobias Macey Comments Off on Simplified Data Extraction And Analysis For Current Events With Newspaper - Episode 280

News media is an important source of information for understanding the context of the world. To make it easier to access and process the contents of news sites Lucas Ou-Yang built the Newspaper library that aids in automatic retrieval of articles and prepare it for analysis. In this episode he shares how the project got started, how it is implemented, and how you can get started with it today. He also discusses how recent improvements in the utility and ease of use of deep learning libraries open new possibilities for future iterations of the project.

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Digging Into Dagster: An Opinionated Open Source Framework For Data Orchestration - Episode 279

September 7, 2020 Tobias Macey Comments Off on Digging Into Dagster: An Opinionated Open Source Framework For Data Orchestration - Episode 279

Data applications are complex and continually evolving, often requiring collaboration across multiple teams. In order to keep everyone on the same page a high level abstraction is needed to facilitate a cross-cutting view of the data orchestration across integration, transformation, analytics, and machine learning. Dagster is an innovative new framework that leans on the power and flexibility of Python to provide an extensible interface to the complete lifecycle of data projects. In this episode Nick Schrock explains how he designed the Dagster project to allow for integration with the entire data ecosystem while providing an opinionated structure for connecting the different stages of computation. He also discusses how he is working to grow an open ecosystem around the Dagster...

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When, Why, and How To Use Web Scraping In A Nutshell - Episode 278

September 1, 2020 Tobias Macey Comments Off on When, Why, and How To Use Web Scraping In A Nutshell - Episode 278

The internet is a rich source of information, but a majority of it isn't accessible programmatically through APIs or databases. To address that shortcoming there are a variety of web scraping frameworks that aid in extracting structured data from web pages. In this episode Attila Tóth shares the challenges of web data extraction, the ways that you can use it, and how Scrapy and ScrapingHub can help you with your projects.

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Working In The Code Mines: Mining Software Repositories With PyDriller - Episode 277

August 25, 2020 Tobias Macey Comments Off on Working In The Code Mines: Mining Software Repositories With PyDriller - Episode 277

A large portion of the software industry has standardized on Git as the version control sytem of choice. But have you thought about all of the information that you are generating with your branches, commits, and code changes? Davide Spadini created the PyDriller framework to simplify the work of mining software repositories to perform research on the technical and social aspects of software engineering. In this episode he shares some of the insights that you can gain by exploring the history of your code, the complexities of building a framework to interact with Git, and some of the interesting ways that PyDriller can be used to inform your own development practices.

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