Remove Clustering Remove Data Lakes Remove Database
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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

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Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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Data Integrity for AI: What’s Old is New Again

Precisely

The demand for higher data velocity, faster access and analysis of data as its created and modified without waiting for slow, time-consuming bulk movement, became critical to business agility. The big data boom was born, and Hadoop was its poster child. A data lake!

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Data mining

Dataconomy

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Businesses across various sectors are leveraging data mining to gain a competitive edge, improve decision-making, and optimize operations.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Flipboard

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. Set up the database access and network access. Delete the MongoDB Atlas cluster.

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Drowning in Data? A Data Lake May Be Your Lifesaver

ODSC - Open Data Science

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. The data lake can then refine, enrich, index, and analyze that data. and various countries in Europe.

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Data-Centric Firms Address Athena Shortcomings with Smart Indexing

Smart Data Collective

The size and the variety of data that enterprises have to deal with have become more complex and larger. Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. In traditional relational database engines, users can plan indexing to improve performance.