Monte Carlo

JANUARY 23, 2024

Given that Max and his team couldn’t take a customer’s entire database, or even just the metadata, and fit it in the context window, “We realized we needed to be really smart about RAG, which is retrieving the right information to generate the right SQL. Max agrees. It’s hard with RAG.

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 52

Towards Data Science

APRIL 6, 2023

Principles, practices, and examples for ensuring high quality data flows Source: DreamStudio (generated by author) Nearly 100% of companies today rely on data to power business opportunities and 76% use data as an integral part of forming a business strategy.

Unstructured Data 76

Unstructured Data Metadata Data Coding 76

Towards Data Science

JUNE 16, 2023

Identifying your business-critical dashboards Looker exposes metadata about content usage in pre-built Explores that you can enrich with your own data to make it more useful. How to keep your critical data model definitions updated Automate as much as possible around tagging your critical data models. Source: synq.io

BI 73

BI Data ETL Tools Machine Learning 73

AltexSoft

AUGUST 22, 2022

Support for different data types and use cases. A data fabric supports structured, unstructured, and semi-structured data whether it comes in real-time or generated in batches. It is also driven by metadata, which it uses to recognize patterns, map data, and perform continuous analysis. How data fabric works.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

AUGUST 10, 2023

What is Data observability? The five pillars My data observability definition has not changed since I first coined it in 2019: Data observability refers to an organization’s comprehensive understanding of the health and performance of the data within their systems. What is data pipeline observability?

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

AUGUST 13, 2024

And how should you leverage it as you evaluate different approaches to improving data quality and reliability? The Gartner definition of data observability While Monte Carlo is unquestionably the data observability category’s creator, we’re certainly not the only game in town – and that’s a good thing.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 52

Ascend.io

JUNE 8, 2023

Although point solutions for data lineage exist, fully integrated data pipeline lineage capabilities make superior use of operational metadata and produce better quality and more timely reports. These varying definitions are based on the format, function, origin, and different uses for the data in practice.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

JANUARY 25, 2023

There is, however, an added dimension to this relationship: data producers are often consumers of upstream data sources. Data warehouse producers wear both hats working with upstream producers so they can consume high-quality data and producing high-quality data to provide to their consumers.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Scribd Technology

JULY 27, 2021

Scribd offers a variety of publisher and user-uploaded content to our users and while the publisher content is rich in metadata, user-uploaded content typically is not. Additional insights from data In the first iteration of the model, we had a dataset for training collected by our experts to fit the definition of each category.

High Quality Data 52

High Quality Data Metadata Algorithm Datasets 52

Monte Carlo

FEBRUARY 8, 2024

Vendors who specialize in the detection of data reliability issues did not perform as well as the others.” G2 Crowd G2 was one of the earliest non-vendor resources to put together a credible list of data observability vendors and a definition for the category. That leaves a lot of cracks in your pipelines to fill.

BI 59

BI Data Pipeline Data Warehouse Data 59

Precisely

JANUARY 16, 2024

To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.

Data Governance 69

Data Governance Government Metadata Data 69

Precisely

JANUARY 11, 2023

To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.

Data Governance 52

Data Governance Government Metadata Data 52

Monte Carlo

JUNE 23, 2022

Technologies like data observability can bring data monitoring to scale so there doesn’t need to be a trade off between quantity and big data quality, but the point remains data volume alone is insufficient to make a fraction of the impact of a well-maintained, high quality data product.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Monte Carlo

MAY 17, 2023

At that point, historic data didn’t reflect the current reality of their audience’s daily lives, and real-time data became essential—not just for determining advertising spend, but for understanding the current state of how users were interacting with the Blinkist app and content across the web.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

At that point, historic data didn’t reflect the current reality of their audience’s daily lives, and real-time data became essential—not just for determining advertising spend, but for understanding the current state of how users were interacting with the Blinkist app and content across the web.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52