KDnuggets
AUGUST 15, 2024
The AI and ML complexity results in a growing number and diversity of jobs that require AI & ML expertise. We’ll give you a rundown of these jobs regarding the technical skills they need and the tools they employ.
databricks
AUGUST 15, 2024
An Introduction to Time Series Forecasting with Generative AI Time series forecasting has been a cornerstone of enterprise resource planning for decades. Predictions.
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KDnuggets
AUGUST 15, 2024
The Walrus operator, introduced in Python 3.8, enables assignment within expressions, but requires careful use to maintain readability. And this tutorial will teach you how.
ArcGIS
AUGUST 15, 2024
With the growing popularity of 3D GIS, users are shifting from 2D to 3D. What is the proper method to move pre-existing 2D data onto a 3D scene?
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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
KDnuggets
AUGUST 15, 2024
Meet the demand for data science leaders with Northwestern University
Cloudera
AUGUST 15, 2024
Data practitioners are consistently asked to deliver more with less, and although most executives recognize the value of innovating with data, the reality is that most data teams spend the majority of their time responding to support tickets for data access, performance and troubleshooting, and other mundane activities. At the heart of this backlog of requests is this: data is hard to work with, and it’s made even harder when users need to work to get or find what they need.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
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Snowflake
AUGUST 15, 2024
In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out.
Monte Carlo
AUGUST 15, 2024
I’m sure you’ve heard the saying “garbage in, garbage out” when it comes to data. But what if we could actually turn that garbage into something useful? In this article, we’ll look into data cleaning techniques to clean up messy data using some SQL magic. Table of Contents What Data Cleaning Techniques Involve Handling Missing Data Techniques to Handle Missing Data Removing Duplicates Correcting Inconsistencies Techniques to Correct Inconsistencies Standardizing For
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