Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Validity: Validity refers to whether the data accurately represents the concepts or phenomena it is intended to measure.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Airbnb Tech

NOVEMBER 28, 2023

To fully enable this incentivization approach, we believed it would be paramount to introduce the concept of a data quality score directly tied to data assets. We identified the following objectives for the score: Evolve our understanding of data quality beyond a simple binary definition (certified vs uncertified).

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Monte Carlo

MARCH 12, 2024

With Dataplex, teams get lineage and visibility into their data management no matter where it’s housed, centralizing the security, governance, search and discovery across potentially distributed systems. Dataplex works with your metadata. The SQL expression should evaluate to true (pass) or false (fail) per row.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 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

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. Table of Contents What is ETL Pipeline?

Process 52

Process Data Pipeline Data Warehouse AWS 52