Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? As your team builds your data fabric, make sure you have a designated way to collect the various metadata associated with your data inputs.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

JULY 11, 2023

System or technical errors: Errors within the data storage, retrieval, or analysis systems can introduce inaccuracies. This can include software bugs, hardware malfunctions, or data integration issues that lead to incorrect calculations, transformations, or aggregations. is the gas station actually where the map says it is?).

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

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. In a fast growing, rapidly evolving, slightly chaotic data ecosystem, metadata management and tooling become a vital component of a modern data platform.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Monte Carlo

JUNE 20, 2023

Second step: Creating data product SLOs A key part of data product thinking is keeping the consumers at the center and considering what provides the most value for them. You can think of SLOs as measures that remove uncertainty surrounding the data and serve as a primary way to define trustworthiness for its consumers.

Building 52

Building Data Datasets High Quality Data 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

DataKitchen

SEPTEMBER 21, 2023

Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? ” For example, these tools may offer metadata-based notifications.

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

Monte Carlo

AUGUST 30, 2023

Always make sure you’re handling sensitive or personal data properly when managing test failures and integrating systems. That’s why dbt testing and data observability can work together beautifully to ensure your data is trustworthy, reliable, and available when and where your data consumers expect it.

SQL 52

SQL Datasets Database High Quality Data 52

U-Next

SEPTEMBER 7, 2022

Efficiency in data access enables businesses to make well-informed decisions more quickly. . Data management enables enterprises to increase data usage and effectively utilize it through repeatable procedures to keep data and metadata updated. Effective Data Integration.

Data Management 52

Data Management Management Electronics Unstructured Data 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

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

Monte Carlo

MAY 17, 2023

Keeping these revenue generators online and accurate is a common data observability use case. Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a gold rush, high quality data is the pickaxe. And your data engineers will thank you.”

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Keeping these revenue generators online and accurate is a common data observability use case. Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a goldrush, high quality data is the pickaxe. And your data engineers will thank you.”

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52