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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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Data Engineering Weekly #203

Data Engineering Weekly

With Astro, you can build, run, and observe your data pipelines in one place, ensuring your mission critical data is delivered on time. Generative AI demands the processing of vast amounts of diverse, unstructured data (e.g., Generative AI demands the processing of vast amounts of diverse, unstructured data (e.g.,

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Snowflake Startup Challenge 2024: Announcing the 10 Semi-Finalists

Snowflake

The list of Top 10 semi-finalists is a perfect example: we have use cases for cybersecurity, gen AI, food safety, restaurant chain pricing, quantitative trading analytics, geospatial data, sales pipeline measurement, marketing tech and healthcare. Our sincere thanks go out to everyone who participated in this year’s competition.

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Bring Gen AI & LLMs to Your Data

Snowflake

Get smarter about your data with native LLMs Additionally, Snowflake is building LLMs directly into the platform to help customers boost productivity and unlock new insights from their data. One area that’s been especially challenging for customers is understanding and extracting value from unstructured data, such as documents.

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How to Become a Data Engineer in 2024?

Knowledge Hut

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Data scientists and data Analysts depend on data engineers to build these data pipelines.

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Data Engineering Weekly #161

Data Engineering Weekly

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

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What Is A DataOps Engineer? Skills, Salary, & How to Become One

Monte Carlo

In a nutshell, DataOps engineers are responsible not only for designing and building data pipelines, but iterating on them via automation and collaboration as well. So, does this mean you should choose DataOps engineering vs. data engineering when considering your next career move? What does a DataOps engineer do? It depends!