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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

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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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Best Practices for Data Lake Security

ODSC - Open Data Science

While databases were the traditional way to store large amounts of data, a new storage method has developed that can store even more significant and varied amounts of data. These are called data lakes. What Are Data Lakes? In many cases, this could mean using multiple security programs and platforms.

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Choosing a Data Lake Format: What to Actually Look For

ODSC - Open Data Science

Recently we’ve seen lots of posts about a variety of different file formats for data lakes. There’s Delta Lake, Hudi, Iceberg, and QBeast, to name a few. It can be tough to keep track of all these data lake formats — let alone figure out why (or if!) And I’m curious to see if you’ll agree.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

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Here is how IBM’s Data Scientists look at Data-Driven Future

Dataconomy

An aspiration to create a data-driven future has resulted in massive data lakes, where even the most experienced data scientists can drown in. Today, it’s all about what you do with that data that determines your success. Without data, you simply can’t. And IBM has the recipe for this.

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

Precisely

Artificial Intelligence (AI) is all the rage, and rightly so. This is of course an over-simplification of the data warehousing journey, but as data warehousing has moved to the cloud and business intelligence has evolved into powerful analytics and visualization platforms the foundational best practices shared here still apply today.