The Data Administration Newsletter

APRIL 20, 2021

Steve Hoberman has been a long-time contributor to The Data Administration Newsletter (TDAN.com), including his The Book Look column since 2016, and his The Data Modeling Addict column years before that.

Data Governance 52

Data Governance Data Modeling Data Models Business Intelligence 52

The Data Administration Newsletter

JULY 5, 2022

Bounded Contexts / Ubiquitous Language My new book, Data Model Storytelling,[i] contains a section describing some of the most significant challenges data modelers and other Data professionals face. Like most of its predecessors, including Agile development and […].

Data Modeling 102

Data Modeling Data Models 102

The Data Administration Newsletter

OCTOBER 5, 2021

Doug has spoken many times at our Data Modeling Zone conferences over the years, and when I read the book, I can hear him talk in his distinct descriptive and conversational style. The Enrichment Game describes how to improve data quality and data useability […].

Data Quality 52

Data Quality Data Modeling Data Models 52

The Data Administration Newsletter

APRIL 4, 2023

Why would Technics Publications publish a book outside its specialty of data management? First, Graham is a world-renowned data modeler and the author of Data Modeling for Quality, and therefore many of his examples are in the field of data management. Second, and more […]

Data Modeling 52

Data Modeling Data Models 52

The Data Administration Newsletter

OCTOBER 2, 2024

Every once in a while, a book comes along that contains such innovative ideas that I find myself whispering “wow” and “interesting” as I read through the pages. Enterprise Intelligence,” by Eugene Asahara, is one such book.

Business Intelligence 52

Business Intelligence Business Intelligence Data Models Data Modeling 52

The Data Administration Newsletter

JANUARY 5, 2021

Sometimes I like to read a book purely for pleasure, like a good Dan Brown or Stephen King novel, and sometimes I like to read a book to learn something new. There are not many books that I read for both pleasure and to learn new things. One exception is Telling Your Data Story: Data […].

Data Governance 52

Data Governance Data Models Data Modeling Big Data 52

Data Science Dojo

JUNE 7, 2023

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science 195

Data Science Data Visualization Data Scientist Machine Learning 195

The Data Administration Newsletter

FEBRUARY 1, 2022

My new book, Data Model Storytelling[i], describes how data models can be used to tell the story of an organization’s relationships with its Stakeholders (Customers, Suppliers, Dealers, Regulators, etc.), The book describes, […].

Data Modeling 64

Data Modeling Data Models Data Governance 64

Iguazio

DECEMBER 14, 2023

With practical code examples and specific tool recommendations, the book empowers readers to implement the concepts effectively. After reading the book, ML practitioners and leaders will know how to deploy their ML models to production and scale their AI initiatives, while overcoming the challenges many other businesses are facing.

ML 52

ML ML Data Science Data Preparation 52

The Data Administration Newsletter

JANUARY 17, 2024

Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations.

AI 122

AI AI Data Quality Data Governance 122

Flipboard

MARCH 17, 2025

FastAPI leverages Pydantic for data modeling, one of the standout features of FastAPI, though it is not exclusive to it, which then allows FastAPI to validate incoming data automatically against the defined schema (e.g., Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL!

Python 153

Python Deep Learning Deep Learning Machine Learning 153

Flipboard

NOVEMBER 20, 2024

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries.

AWS 160

AWS Natural Language Processing Machine Learning Machine Learning 160

Dataconomy

OCTOBER 15, 2024

I’ve found that while calculating automation benefits like time savings is relatively straightforward, users struggle to estimate the value of insights, especially when dealing with previously unavailable data. We were developing a data model to provide deeper insights into logistics contracts.

Data Models 113

Data Models Data Modeling Analytics Analytics 113

The Data Administration Newsletter

APRIL 19, 2022

I know very few people who specialize in one area of IT/business architecture yet have practical knowledge of how all of the areas fit together. Roger Burlton is one of those people. Roger has expertise in business process management, but has deep and vast knowledge of all facets of an organization’s business architecture. It is […].

Data Models 52

Data Models Data Modeling 52

Smart Data Collective

NOVEMBER 18, 2020

Therefore, machine learning is of great importance for almost any field, but above all, it will work well where there is Data Science. Data Mining Techniques and Data Visualization. Data Mining is an important research process. Anyone can become a Data Scientist that use Data Mining. Including yourself.

Data Mining 144

Data Mining Data Mining Data Mining Data Science 144

AWS Machine Learning Blog

MARCH 7, 2025

This new capability integrates the power of graph data modeling with advanced natural language processing (NLP). To address this, AWS announced a public preview of GraphRAG at re:Invent 2024, and is now announcing its general availability. More specifically, the graph created will connect chunks to documents, and entities to chunks.

Analytics 130

Analytics Analytics AWS Natural Language Processing 130

Hacker News

SEPTEMBER 26, 2024

It starts with defines a core data model and the relations and the atoms. It starts with “core concepts” and how to configure and how to run things. Humans don't learn about things this way.

Python 180

Python Computer Science Computer Science Data Science 180

ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […].

Data Warehouse 52

Data Warehouse ETL Data Governance Data Quality 52

Flipboard

MAY 30, 2025

Data quality is ownership of the consuming applications or data producers. Governance The two key areas of governance are model and data: Model governance Monitor model for performance, robustness, and fairness. Model versions should be managed centrally in a model registry.

AWS 141

AWS AI AI Database 141

Dataconomy

MAY 2, 2023

Machine learning requires computation on large data sets, which means that a strong foundation in fundamental skills such as computer architecture, algorithms, data structures, and complexity is crucial. It is essential to delve deeply into programming books and explore new concepts to gain a competitive edge in the field.

ML 110

ML ML Machine Learning Machine Learning 110

ODSC - Open Data Science

FEBRUARY 14, 2023

Trained with 570 GB of data from books and all the written text on the internet, ChatGPT is an impressive example of the training that goes into the creation of conversational AI. They are designed to understand and generate human-like language by learning from a large dataset of texts, such as books, articles, and websites.

Database 124

Database AI AI Natural Language Processing 124

Pickl AI

JULY 3, 2024

Each book on the shelf is an entity, a unique record with its own story. The attributes of a book entity could be its title, author, publication date, ISBN number, genre, and even the number of pages. These attributes collectively describe the book and differentiate it from others. How do Attributes relate to Data Security?

Database 52

Database Data Analysis Data Analysis Data Wrangling 52

Snorkel AI

OCTOBER 18, 2023

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results. Book a demo today.

AI 64

AI AI Machine Learning Machine Learning 64

Snorkel AI

OCTOBER 18, 2023

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results. Book a demo today.

AI 59

AI AI Machine Learning Machine Learning 59

Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. He is also the author of the book Simplify Big Data Analytics with Amazon EMR.

AWS 167

AWS ML ML Analytics 167

phData

MARCH 11, 2024

Traditional CDPs : These platforms are out-of-the-box solutions designed to gather and house their own data store – separate from your core data infrastructure. Characterized by their plug-and-play nature, traditional CDPs often come with lengthy and costly setup processes predicated on rigid data model prerequisites.

Data Warehouse 52

Data Warehouse Cloud Data Data Modeling Data Models 52

Mlearning.ai

FEBRUARY 13, 2023

where each book represents a record, each chapter represents a field, and each shelf represents a table. These databases are the most common type used today and store data in a structured format using tables, rows, and columns. In this guide, we’ll cover the eight most popular types of databases. and let’s dive in!

Database 52

Database Data Modeling Data Models Big Data 52

Flipboard

MARCH 23, 2023

It has helped to write a book. The world is full of uncreative boilerplate content that humans have to write: catalog entries, financial reports, back covers for books (I’ve written more than a few), and so on. At this point, it’s not clear how language models and their outputs fit into copyright law. What Is the Future?

AI 175

AI AI SQL Natural Language Processing 175

Snorkel AI

OCTOBER 18, 2023

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results. Book a demo today. See what Snorkel option is right for you.

AI 52

AI AI ML ML 52

phData

OCTOBER 30, 2023

Additionally, we will provide a distinction between measures and calculated columns in data modeling and visualization.

Power BI 78

Power BI Data Modeling Data Models Analytics 78

AWS Machine Learning Blog

NOVEMBER 22, 2023

With these focus areas, you can conduct an architecture review from different aspects to enhance the effectivity, observability, and scalability of the three components of an AI/ML project, data, model, or business goal. She focuses on NLP-specific workloads, and shares her experience as a conference speaker and a book author.

AWS 125

AWS ML ML Machine Learning 125

Snorkel AI

MARCH 16, 2023

To solve these problems, we developed two big breakthroughs that helped us with our manufacturing customers: The ML Life Cycle, and data-centric AI. The ML life cycle is broken down into three stages: data, model, and deploy. How am I modifying that data to improve the model performance? What do you do there?

ML 52

ML ML AI AI 52

Snorkel AI

MARCH 16, 2023

To solve these problems, we developed two big breakthroughs that helped us with our manufacturing customers: The ML Life Cycle, and data-centric AI. The ML life cycle is broken down into three stages: data, model, and deploy. How am I modifying that data to improve the model performance? What do you do there?

ML 52

ML ML AI AI 52

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? Adapted from the book Effective Data Science Infrastructure. Data is at the core of any ML project, so data infrastructure is a foundational concern.

ML 145

ML ML Data Scientist AWS 145

phData

JULY 22, 2024

I consciously chose to pivot away from general software development and specialize in Data Engineering. I’ve moved from building user interfaces and backend systems to designing data models, creating data pipelines, and gaining valuable insights from complex datasets.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

APRIL 29, 2024

Besides easy access, using Trainium with Metaflow brings a few additional benefits: Infrastructure accessibility Metaflow is known for its developer-friendly APIs that allow ML/AI developers to focus on developing models and applications, and not worry about infrastructure.

AWS 132

AWS ML ML Python 132