Cloudera
NOVEMBER 15, 2021
The International Data Corporation (IDC) estimates that by 2025 the sum of all data in the world will be in the order of 175 Zettabytes (one Zettabyte is 10^21 bytes). Most of that data will be unstructured, and only about 10% will be stored. Here we mostly focus on structured vs unstructured data.
Knowledge Hut
APRIL 23, 2024
Data can originate from numerous sources, such as social media, sensors, transactions, logs, etc. Data mining deals with data that usually comes from organized data stored in databases or spreadsheets. Data mining is capable of handling comparatively smaller datasets.
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DareData
MARCH 5, 2024
At DareData Engineering, we believe in a human-centric approach, where AI agents work together with humans to achieve faster and more efficient results. At its core, RAG harnesses the power of large language models and vector databases to augment pre-trained models (such as GPT 3.5 ).
Data Engineering Weekly
MARCH 3, 2024
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!!!
Knowledge Hut
DECEMBER 26, 2023
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. These data have been accessible to us because of the advanced and latest technologies which are used in the collection of data.
Knowledge Hut
JUNE 20, 2023
Let's find out the differences between a data scientist and a machine learning engineer below to make an informative decision. Data Engineer vs Machine Learning Engineer While there are similarities between a data engineer and a machine learning engineer, both play a key role in the technological world.
Monte Carlo
JANUARY 23, 2024
Predicting the future of data and AI in 2024 is not for the faint of heart. Shiny new technologies promise pie-in-the-sky outcomes. Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. But what does that mean for the roles of data engineers and data scientists going forward?
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