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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. What is unstructured data?

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Data Scientist roles and responsibilities

U-Next

After understanding what Data Science is and its fundamental principles, what else do we need to discuss? What is a Data Scientist? A Data Scientist is skilled in concluding data using various systems, procedures, and algorithms. The people who have inquiries about data are known as Data Scientists.

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Top 16 Data Science Specializations of 2024 + Tips to Choose

Knowledge Hut

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. They construct pipelines to collect and transform data from many sources.

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Knowledge Graphs: The Essential Guide

AltexSoft

Bringing data together from heterogeneous enterprise sources and creating a unified view of that data is a popular application of knowledge graphs. This is especially useful for big enterprises when the sources are presented in different formats (CSV, XML, JSON, relational databases) and use different data schemas.

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20 Solved End-to-End Big Data Projects with Source Code

ProjectPro

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Advanced data scientists can use supervised algorithms to predict future trends.