KDnuggets
FEBRUARY 20, 2025
Whatever role is best for youdata scientist, data engineer, or technology managerNorthwestern University's MS in Data Science program will help you to prepare for the jobs of today and the jobs of the future.
WeCloudData
FEBRUARY 20, 2025
Data is the most powerful weapon in today’s world. Everything works around the data. But data alone is not enough to empower businesses to make data-driven decisions. We need data visualization to make sense of data and understand it to make informed decisions. Data visualization means transforming complex data into visual aids like charts, graphs, […] The post The Importance of Data Visualization in Analytics appeared first on WeCloudData.
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Hevo
FEBRUARY 16, 2025
You work with data to gain insights, improve decisions, and develop new ideas. With more and more data coming from all sorts of places, it’s super important to have a good data plan. That’s where big data integration comes in! It’s all about combining data from different sources to get a complete picture.
databricks
FEBRUARY 18, 2025
As we welcome the new year, we're thrilled to announce several new resources for R users on Databricks: a comprehensive developer guide, the.
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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
Monte Carlo
FEBRUARY 20, 2025
For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. Over the last several months, Gartner has shared several key illustrations to demonstrate how they perceive AI-readiness in 2025. And on the whole, I would say theyre pretty spot on.
Precisely
FEBRUARY 20, 2025
Key Takeaways Trusted data is critical for AI success. Data integration ensures your AI initiatives are fueled by complete, relevant, and real-time enterprise data, minimizing errors and unreliable outcomes that could harm your business. Data integration solves key business challenges. It enables faster decision-making, boosts efficiency, and reduces costs by providing self-service access to data for AI models.
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
FEBRUARY 18, 2025
Being able to leverage unstructured data is a critical part of an effective data strategy for 2025 and beyond. To keep up with the competition and AI-accelerated pace of innovation, businesses must be able to mine the treasure trove of value buried in the mountains of unstructured data that comprise approximately 80% of all enterprise data from call center logs, customer reviews, emails and claims reports to news, filings and transcripts.
Hevo
FEBRUARY 16, 2025
It is the 21st century and you are leading a fast-growing fintech startup that is about to hit a breaking point. The data team has doubled in size over six months, but chaos is reigning. Analysts are wasting hours reconciling conflicting reports, engineers are scrambling to fix broken pipelines, and leaders can’t agree on priorities.
KDnuggets
FEBRUARY 17, 2025
Generate high-quality videos in just a few minutes using these fast and accurate video generation models.
Waitingforcode
FEBRUARY 18, 2025
Using cloud managed services is often a love and hate story. On one hand, they abstract a lot of tedious administrative work to let you focus on the essentials. From another, they often have quotas and limits that you, as a data engineer, have to take into account in your daily work. These limits become even more serious when they operate in a latency-sensitive context, as the one of stream processing.
Speaker: Tamara Fingerlin, Developer Advocate
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Analytics Vidhya
FEBRUARY 20, 2025
If you’re working with AI/ML workloads(like me) and trying to figure out which data format to choose, this post is for you. Whether you’re a student, analyst, or engineer, knowing the differences between Apache Iceberg, Delta Lake, and Apache Hudi can save you a ton of headaches when it comes to performance, scalability, and real-time […] The post Apache Iceberg vs Delta Lake vs Hudi: Best Open Table Format for AI/ML Workloads appeared first on Analytics Vidhya.
Snowflake
FEBRUARY 20, 2025
If you want to add rocket fuel to your organization, invest in employee education and training. While it may not be the first strategy that comes to mind, its one of the most effective ways to drive widespread business benefits, from increased efficiency to greater employee satisfaction and it deserves to be a top priority. Training couldnt be more relevant or pressing in our new AI normal, which is advancing at unprecedented speeds.
Data Engineering Weekly
FEBRUARY 18, 2025
Fluss is a compelling new project in the realm of real-time data processing. I spoke with Jark Wu , who leads the Fluss and Flink SQL team at Alibaba Cloud, to understand its origins and potential. Jark is a key figure in the Apache Flink community, known for his work in building Flink SQL from the ground up and creating Flink CDC and Fluss. You can read the Q&A version of the conversation here, and don’t forget to listen to the podcast.
DataKitchen
FEBRUARY 17, 2025
No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically As a data engineer, ensuring data quality is both essential and overwhelming. The sheer volume of tables, the complexity of the data usage, and the volume of work make manual test writing an impossible task to get done.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
KDnuggets
FEBRUARY 21, 2025
Read some honest advice on how to become a machine learning engineer.
Start Data Engineering
FEBRUARY 16, 2025
1. Introduction 2. Python environment setup 3. VSCode Primer 4. Extensions overview 1. Gitlens 2. Python test & debug 3. Ruff 4. SQL Tools 5. Jupyter 6. Data Wrangler 7. autoDocstring 8. Rainbow csv 9. DBT power user 5. Privacy, Performance, and Cognitive Overload 6. Conclusion 7. Recommended reading 1. Introduction Whether you are setting up visual studio code for your colleagues or want to improve your workflow, tons of extensions are available.
databricks
FEBRUARY 20, 2025
Finetuning Embedding Models for Better Retrieval and RAG TL;DR: Finetuning an embedding model on in-domain data can significantly improve vector search and retrieval-augmented generation (RAG).
DataKitchen
FEBRUARY 20, 2025
Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
KDnuggets
FEBRUARY 20, 2025
Turn your local LLMs into a personal, autonomous AI application that can effortlessly retrieve answers from the web or your documents.
Scott Logic
FEBRUARY 20, 2025
AI doesnt have to be brute forced requiring massive data centres. Europe isnt necessarily behind in AI arms race. In fact, the UK and Europes constraints and focus on more than just economic return and speculation might well lead to more sustainable approaches. This article is a follow on to Will Generative AI Implode and Become More Sustainable? from July 2024.
Precisely
FEBRUARY 17, 2025
I’ve been in the data business for nearly 30 years, and I’m still learning. Lately, I’ve been diving deep into the specific needs of telecommunication companies, particularly understanding the serviceability and “salability” of an address. Much of my career has been spent building data to accurately locate addresses for business intelligence (at GDT and Pitney Bowes) or navigation (at Tele Atlas and TomTom).
phData: Data Engineering
FEBRUARY 21, 2025
The Greek philosopher Heraclitus (c. 535 BCE475 BCE) proclaimed, There is nothing permanent except change. Ironically, all these years later, Heraclituss sentiment remains true. Progress is frequent and continuous, especially in the realm of technology. The advent of one technology leads to another, which sparks another breakthrough, and another. In only a matter of years, this domino effect can produce a world irrecognizable from years prior.
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With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
KDnuggets
FEBRUARY 18, 2025
Develop AI applications, test them, and deploy on the cloud using user-friendly MLOps tools and straightforward methods.
Cloudyard
FEBRUARY 17, 2025
Read Time: 2 Minute, 20 Second Loading CSV files into Snowflake is a common data engineering task. However, a frequent challenge arises when CSV files contain more columns than their corresponding Snowflake tables. In such cases, the COPY INTO command with schema evolution ( AUTO_CHANGE =TRUE) fails because it requires matching columns. To address this, Dynamic CSV Column Mapping with Stored Procedures can be used to create a flexible, automated process that maps additional columns in the CSV to
databricks
FEBRUARY 21, 2025
As part of our commitment to help upskill the current and future workforce, we are excited to announce new, free courses to help professionals learn.
ArcGIS
FEBRUARY 19, 2025
The latest update of Regrid Premium Parcel dataset will include Esri demographic and curated environmental and elevation data.
Speaker: Tamara Fingerlin, Developer Advocate
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
KDnuggets
FEBRUARY 17, 2025
Enhance the array operational process with methods you may not have previously known.
WeCloudData
FEBRUARY 17, 2025
Welcome back to our Data Wrangling with Python series! In the first blog of the data wrangling series, we introduced the basics of data wrangling using Python. We work on handling missing values, removing special characters, and dropping unnecessary columns to prepare our dataset for further analysis. Now, the next step is to deeply explore […] The post Textual Data Wrangling with Python: A Step-by-Step Guide appeared first on WeCloudData.
databricks
FEBRUARY 17, 2025
As we continue to navigate the complexities of the modern world, it's becoming increasingly clear that data-driven decision making is the key to.
ArcGIS
FEBRUARY 20, 2025
The integration of AutoCAD, Civil 3D, digital models (Revit), and ArcGIS Pro combines the strengths of each system
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
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