Data Science Dojo

OCTOBER 10, 2023

IMPACT 2023- The Data Observability Summit (Virtual event – November 8) Focus on Data and AI : The summit will illuminate how contemporary technical teams are crafting impactful and performant data and AI products that businesses can rely on.

AI 243

AI AI Artificial Intelligence Artificial Intelligence 243

Data Science Dojo

NOVEMBER 15, 2023

Learn more about the Data Observability Summit AI Expo in Austin The AI Expo is a yearly conference in Austin, Texas, organized by Amazon, which showcases the latest advancements in artificial intelligence (AI). If you are interested in AI, we encourage you to consider attending one of the conferences listed above.

AI 195

AI AI Machine Learning Machine Learning 195

Smart Data Collective

JULY 15, 2021

It helps you locate and discover data that fit your search criteria. With data catalogs, you won’t have to waste time looking for information you think you have. Data catalogs can contain snippets about the information, including its scope and how your company is using it. How Does a Data Catalog Benefit Your Company?

Data Quality 127

Data Quality Database Data Observability Data Pipeline 127

Precisely

APRIL 11, 2023

With our Data Integrity Suite, you can get this initiative kicked off faster and be on the path to bigger and better results for the business. Replicate data into the data cloud of your choice using hundreds of connectors.

Data Observability 52

Data Observability Cloud Data Data Quality Data Governance 52

Precisely

MAY 16, 2024

Chances are it’s not just one use case but many.

AI 59

AI AI AWS Data Quality 59

Hacker News

JULY 4, 2023

var ( // Simplified schema definition generated by the Arrow Record encoder based on // the data observed. Handling recursive schema definition Apache Arrow does not support recursive schema definitions, implying that data structures with variable depth cannot be directly represented.

Data Modeling 123

Data Modeling Data Models Data Observability 123

Alation

JANUARY 20, 2022

Ryan recommends a nine-step framework for managing data quality with data governance. He also points out where data tools can be applied to help with these process steps. Step 1: Determine list of CDEs (Critical Data Elements). This requires that you determine the scope of your data quality program.

Data Quality 94

Data Quality Data Governance Data Profiling Clean Data 94