Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

JUNE 22, 2023

Large organizations processing huge volumes of data usually store it in Amazon Simple Storage Service (Amazon S3) and query the data to make data-driven business decisions using distributed analytics engines such as Amazon Athena. There are a few factors to consider when deciding the columns on which to partition.

Metadata 80

Metadata Management Recreation/Entertainment Optimization 80

O'Reilly on Data

MARCH 23, 2023

Many of these are unsurprising: you can ask it to write a letter, you can ask it to make up a story, you can ask it to write descriptive entries for products in a catalog. Many of these are unsurprising: you can ask it to write a letter, you can ask it to make up a story, you can ask it to write descriptive entries for products in a catalog.

IT 264

IT Modeling Testing Risk 264

Alation

DECEMBER 14, 2021

They were once typically deployed in traditional data center environments but have been retrofitted and remarketed as “cloud” products. Furthermore, does my application really need a server of its own in the first place — especially when the organizational plan involves hosting everything on an external service?

Cost-Benefit 52

Cost-Benefit Data Processing Optimization Consulting 52

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

OCTOBER 9, 2019

Paco Nathan’s latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference. Data Science meets Climate Science. Data veracity, data stewardship, and heros of data science.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

Occam's Razor

APRIL 21, 2014

When you feel you have enough, use it to buy time/money to go fix the configuration problems. The mistake we make is that we obsess about every big, small and insignificant analytics implementation challenge and try to fix it because we want 99.95% comfort with data quality. We wonder why data people are not loved. :).

Marketing Analytics 131

Marketing Analytics Marketing Analytics Measurement 131

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. Use ML to unlock new data types—e.g.,

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

AWS Big Data

AUGUST 14, 2023

This post is the first in a series dedicated to the art and science of practical data mesh implementation (for an overview of data mesh, read the original whitepaper The data mesh shift ). Taken together, the posts in this series lay out some possible operating models for data mesh within an organization.

Management 79

Management Risk Measurement Optimization 79

datapine

MAY 17, 2021

1) What Is Data Discovery? 2) Why is Data Discovery So Popular? 3) Data Discovery Tools Attributes. 5) How To Perform Smart Data Discovery. 6) Data Discovery For The Modern Age. We live in a time where data is all around us. Being a data-driven organization starts with understanding your data.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161