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 82

Unstructured Data Metadata Data Coding 82

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 79

BI Data ETL Tools Machine Learning 79

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

Maxime Beauchemin

JANUARY 20, 2017

It also becomes the role of the data engineering team to be a “center of excellence” through the definitions of standards, best practices and certification processes for data objects. Here are a few examples of services that data engineers and data infrastructure engineer may build and operate.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

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

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

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

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

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 Datasets Algorithm 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 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 Data Engineering Data Engineer 52

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

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