This video is part of a series of three that I'd recommend to watch in combination. The other two are: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-HRfR4dJoKDc.html and ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-Ausqifk0ZiM.html
Hi Torstein! Very informative lightboard video. I literally spent two hours this morning and took notes about every fine points. I wish I could someday work under you. I would learn so much. 😊
Good explanation, thank you. However, talk can get started with "Big Data" - which means data lakes are intended to store, manage and serve large Big volume, variability, velocity. Data is ingested in native format. It need to be kept organized, controlled and managed - governance. Data needs to be served in native or processed further for other needs - reporting and visualization, recommendations, process automations and more. Some real-life use cases to start the discussions also will help.
This guy adding that extra S on process like 2 minutes later is so funny to me lololol. Besides that this vid is so good thank you for breaking everything down and explaining it visually!! Appreciate it!!
It is funny and ridiculous that these awesome videos by IBM get ridiculously low views on youtube, whilst so many crappier and much less clear videos with shittz slides get a ton more views… Extremely peculiar
Hi there! You can say that data pipelines run inside data lakes (through various services). A data lake is a cloud-native mechanism that supplies large volumes of quite diverse data to analytics, so that IT and business organizations can generate various business insights. It's basically a centralized place where an organization stores all their different data, allowing for many types of analytics at a larger scale (e.g social media data). While a data pipeline is a system that filters these large amounts of data in order to provide a more concise and insightful set of analytics to the organization. It is used for more efficient and detailed reporting and it serves a specific purpose. Hope this answers your question! 🙂
Depends on the cloud platform that you are using... for example with Microsoft Azure you can use Azure Data Factory for the orchestration and transformation
