Remove Business Analytics Remove Data Lake Remove Unstructured Data
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Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

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Understanding Structured and Unstructured Data

Sisense

Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Unstructured data.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Inflexible schema, poor for unstructured or real-time data. Data lake Raw storage for all types of structured and unstructured data. Low cost, flexibility, captures diverse data sources. Easy to lose control, risk of becoming a data swamp. Exploratory analytics, raw and diverse data types.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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How Data Management and Big Data Analytics Speed Up Business Growth

BizAcuity

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 billion in 2020?

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

Cloud data warehouses took the benefits of the cloud and applied them to data warehouses — bringing massive parallel processing to data teams of all sizes. Scaling the warehouse as business analytics needs grow is as simple as clicking a few buttons (and in some cases, it is even automatic).