Cloudera
NOVEMBER 15, 2021
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.
RandomTrees
SEPTEMBER 27, 2024
Data Factory, Data Activator, Power BI, Synapse Real-Time Analytics, Synapse Data Engineering, Synapse Data Science, and Synapse Data Warehouse are some of them. With One Lake serving as a primary multi-cloud repository, Fabric is designed with an open, lake-centric architecture.
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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 ).
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 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
JUNE 20, 2023
The generalist position would suit a data scientist looking for a transition into a data engineer. Pipeline-Centric Engineer: These data engineers prefer to serve in distributed systems and more challenging projects of data science with a midsize data analytics team.
Monte Carlo
JANUARY 23, 2024
Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. But what does that mean for the roles of data engineers and data scientists going forward?
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