Remove Data Architecture Remove Data Warehouse Remove High Quality Data Remove SQL
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Data Quality Engineer: Skills, Salary, & Tools Required

Monte Carlo

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. About 61% request you also have a formal computer science degree.

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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

Understanding the “rise of data downtime” With a greater focus on monetizing data coupled with the ever present desire to increase data accuracy, we need to better understand some of the factors that can lead to data downtime. We’ll take a closer look at variables that can impact your data next.

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The Symbiotic Relationship Between AI and Data Engineering

Ascend.io

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.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

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.

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DataOps For Business Analytics Teams

DataKitchen

If the IT or data engineering team can’t respond with an enabling data platform in the required time frame, the business analyst does the necessary data work themselves. This ad hoc data engineering work often means coping with numerous data tables and diverse data sets using Alteryx, SQL, Excel or similar tools. .

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Celebrating the New Pioneers of Data Reliability

Monte Carlo

Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers. While a lot has changed in five years, one thing has always remained the same: the company’s commitment to building an insights-driven culture based on accurate and reliable data.

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How to Treat Your Data As a Product

Monte Carlo

For example, when you think about a data warehouse , it’s really just a codebase—primarily composed of SQL—that’s serving internal customers like other analysts, data scientists, and product managers who are using that data to go and make business decisions.

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