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Unlock the Full Potential of Hive

Cloudera

In a previous blog post , we explored the power of Cloudera Observability in providing high-level actionable insights and summaries for Hive service users. In this blog, we will delve deeper into the insight Cloudera Observability brings to queries executed on Hive. Are there any baselines for various metrics about my query?

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Best Assessment Software in 2024 for Tech Recruiting

Hacker Earth Developers Blog

Integration : Seamless integration with other systems, such as Learning Management Systems (LMS) or Human Resource Information Systems (HRIS), can streamline workflows and enhance efficiency. Data Security : In an era of heightened data privacy concerns, robust security measures are a must to protect sensitive information.

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Best Assessment Software in 2024 for Tech Recruiting

Hacker Earth Developers Blog

Integration : Seamless integration with other systems, such as Learning Management Systems (LMS) or Human Resource Information Systems (HRIS), can streamline workflows and enhance efficiency. Data Security : In an era of heightened data privacy concerns, robust security measures are a must to protect sensitive information.

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Automatically Pre-Annotate Customer Reviews with NLP Lab

John Snow Labs

Welcome to Part II of the blog series on extracting entities from text reviews using NLP Lab. In the first part of the blog, we discussed the challenges faced by a market research company as they aimed to extract relevant entities from a vast dataset of hotel reviews. The entities above them are pre-annotated.

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10 Help Desk Metrics to Track for Maximum Efficiency

Kaseya

Without a system or process, users will not know where to direct their queries and IT technicians will not know how to track and resolve tickets. What are help desk metrics? IT technicians use several metrics to track help desk performance and ensure that it remains productive, efficient and operates at its best capacity.

Metrics 59
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Mending the rift between business stakeholders and development teams

Xebia

Upon joining the project, and talking to parties involved, Xebia started gathering hard cold data – by looking at activity metrics in client’s issue tracker (JIRA) and commit histories in their version control system (GIT). It was revealed that significant chunks of work were wasted and thrown away.

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What happens when you mess up data modeling?

Xebia

It’s great that data analytics is now more accessible than ever for anyone to get involved in due to all of these new tools, but this has a lot of risks that are not often discussed. Since each team creates pipelines from scratch, alignment of definitions, metrics and KPIs is limited or even nonexistent and leads to discussions.

Data 130