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Apache Flume: Data Collection, Aggregation & Transporting Tool

Analytics Vidhya

Introduction on Apache Flume Apache Flume is a platform for aggregating, collecting, and transporting massive volumes of log data quickly and effectively. Its design is simple, based on streaming data flows, and written in the Java programming […]. It is very reliable and robust.

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Data Mining vs Data Warehousing: 8 Critical Differences

Analytics Vidhya

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Both are associated with data usage but differ from each other.

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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Good data governance has always involved dealing with errors and inconsistencies in datasets, as well as indexing and classifying that structured data by removing duplicates, correcting typos, standardizing and validating the format and type of data, and augmenting incomplete information or detecting unusual and impossible variations in the data.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

According to data from Robert Half’s 2021 Technology and IT Salary Guide, the average salary for data scientists, based on experience, breaks down as follows: 25th percentile: $109,000 50th percentile: $129,000 75th percentile: $156,500 95th percentile: $185,750 Data scientist responsibilities.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

This required dedicated infrastructure and ideally a full MLOps pipeline (for model training, deployment and monitoring) to manage data collection, training and model updates. Predictive insights: By analyzing historical data, LLMs can make predictions about future system states.

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Deep automation in machine learning

O'Reilly on Data

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

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3 things to get right with data management for gen AI projects

CIO Business Intelligence

Collect, filter, and categorize data The first is a series of processes — collecting, filtering, and categorizing data — that may take several months for KM or RAG models. Structured data is relatively easy, but the unstructured data, while much more difficult to categorize, is the most valuable.