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

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Airbnb Tech

NOVEMBER 28, 2023

Enable full visibility into the quality of our offline data warehouse and individual data assets. Composing the Score Before diving into the nuances of measuring data quality, we drove alignment on the vision by defining our DQ Score guiding principles. whereas others were more like proxies for quality (e.g.,

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Monte Carlo

JULY 11, 2023

If the data they use to inform these decisions isn’t accurate—that is, if it doesn’t reflect reality—then the decisions they make can result in lost revenue, eroded stakeholder trust, and wasted data engineering resources. In other words, bad data is bad for business.”

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

DataKitchen

JUNE 19, 2023

” – Gautama Buddha When your data team is in crisis from errors in production, complaining customers, and uncaring data providers, we all wish we could be unshaken as the Buddha. Our recent survey showed that 97% of data engineers report experiencing burnout in their day-to-day jobs. What is the acceptable range?

Data 52

Data Data Validation Utilities High Quality Data 52

Airbnb Tech

NOVEMBER 24, 2020

Consistency : certified data and metrics represent the single source of truth for key business concepts across all teams and stakeholders at the company. Timeliness: certified data has landing time SLAs , backed by a central incident management process. Fig 7: Example pipeline overview section from a Midas design spec.

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52

Monte Carlo

JANUARY 25, 2023

Data professionals are producers and consumers The first key difference between production service contracts and contracts within the data warehouse is that data producers and consumers are often both in data roles (analysts, data scientists, data engineers, etc). What happens when we detect bad data?

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52