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

Precisely

The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

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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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Methods for Running SQL on JSON in PostgreSQL, MySQL and Other Relational Databases

Rockset

One of the main hindrances to getting value from our data is that we have to get data into a form that’s ready for analysis. Consider the hoops we have to jump through when working with semi-structured data, like JSON, in relational databases such as PostgreSQL and MySQL. It sounds simple, but it rarely is.

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A Prequel to Data Mesh

Towards Data Science

But in order to justify why this concept came into existence, I thought it’d be great to look back in time and understand the evolution of the data landscape. Evolution of the data landscape 1980s — Inception Relational databases came into existence. Organizations began to use relational databases for ‘everything’.

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Best Morgan Stanley Data Engineer Interview Questions

U-Next

Introduction Data Engineer is responsible for managing the flow of data to be used to make better business decisions. A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. What is AWS Kinesis?

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SnowflakeDB: The Data Warehouse Built For The Cloud

Data Engineering Podcast

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Data management and monitoring options.