AltexSoft

AUGUST 22, 2022

So, instead of replacing or rebuilding the existing infrastructure, you add a new, ML-powered abstraction layer on top of the underlying data sources, enabling various users to access and manage the information they need without duplication. Data fabric architecture example. Unified data access. Data and metadata.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

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 76

BI Data ETL Tools Machine Learning 76

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 79

Unstructured Data Metadata Data Coding 79

Ascend.io

JUNE 8, 2023

Data sharing goes beyond simply making the data available. It can be thought of as the final “packaging” and disseminating of the data in a format that meets the specifications of different stakeholders, and is accessible, understandable, and usable across the organization. But how do you unlock these capabilities?

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

Maxime Beauchemin

JANUARY 20, 2017

In retrospect, complex SCD modeling techniques are not intuitive and reduce accessibility. 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.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

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. Robust role based access controls?

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

JUNE 23, 2022

But those are specialized use cases, and while the demand for streaming data poses new big data challenges on the horizon, today most organizations enjoy a technological moment in time where they can cost effectively access enough storage and compute to meet their organization’s needs without breaking a sweat.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52