DataKitchen

NOVEMBER 4, 2021

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

JANUARY 31, 2023

The problem: over-reliance on manual testing and a lack of visibility across domains Checkout.com’s decentralized data structure and reliance on manual tests and monitors meant that data engineering was a single point of failure for data issues. Monte Carlo helped the Checkout.com data team combat those challenges.

Data 52

Data High Quality Data Data Engineering Data Engineer 52

Databand.ai

JANUARY 24, 2023

Supporting all of this requires a modern infrastructure and data architecture with appropriate governance. DataOps helps ensure organizations make decisions based on sound data. Enter DataOps. In doing so, it allows teams to identify problems faster and, therefore, deliver solutions faster. Who’s Involved in a DataOps Team?

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Retail Banking Government Manufacturing 52