Remove Data Storage Remove Structured Data Remove Systems
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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.

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A Flexible and Efficient Storage System for Diverse Workloads

Cloudera

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

But what does an AI data engineer do? AI data engineers play a critical role in developing and managing AI-powered data systems. Table of Contents What Does an AI Data Engineer Do? Data Storage Solutions As we all know, data can be stored in a variety of ways. What are they responsible for?

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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

Instead of handling each piece of data as it arrives, you collect it all and process it in scheduled chunks. It’s like having a designated “laundry day” for your data. This approach is super cost-efficient because you’re not running your systems constantly. The data lakehouse has got you covered!

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2026 Will Be The Year of Data + AI Observability

Monte Carlo

Prior to data powering valuable data products like machine learning models and real-time marketing applications, data warehouses were mainly used to create charts in binders that sat off to the side of board meetings. The most common themes: Data readiness- You cant have good AI with bad data. End-to-end.

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A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

Here are six key components that are fundamental to building and maintaining an effective data pipeline. Data sources The first component of a modern data pipeline is the data source, which is the origin of the data your business leverages. Data storage Data storage follows.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

You don’t need to archive or clean data before loading. The system automatically replicates information to prevent data loss in the case of a node failure. To understand how the entire mechanism works, we need to get familiar with Hadoop structure and key parts. A file stored in the system ?an’t cost-effectiveness.