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Data Quality Testing: Why to Test, What to Test, and 5 Useful Tools

Databand.ai

It enables: Enhanced decision-making: Accurate and reliable data allows businesses to make well-informed decisions, leading to increased revenue and improved operational efficiency. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

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Data Observability Tools: Types, Capabilities, and Notable Solutions

Databand.ai

A reliable observability tool should provide customizable alerting options based on specific conditions or thresholds. Incorporating these features into your data observability strategy will enable you to maintain high-quality data pipelines and make informed decisions about optimizing performance.

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The Rise of the Data Engineer

Maxime Beauchemin

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Let’s highlight the fact that the abstractions exposed by traditional ETL tools are off-target.

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Data Quality Testing: 7 Essential Tests

Monte Carlo

Too much data Too much data might not sound like a problem (it is called big data afterall), but when rows populate out of proportion, it can slow model performance and increase compute costs. Utilizing a string-searching algorithm like RegEx is an excellent way to validate that strings in a column match a particular pattern.

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8 Data Quality Issues and How to Solve Them

Monte Carlo

Too much data Too much data might not sound like a problem (it is called big data afterall), but when rows populate out of proportion, it can slow model performance and increase compute costs. Volume tests It’s important to identify data volume changes as quickly as possible.

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