Building ELTs and Data Pipelines Faster With Rivery And SnowflakeDB - Scraping Youtube Data

There are plenty of clichés about data and its likeness to oil. But it’s far from easy to get value from data.

Companies are creating more data than ever before. At our current pace, 2.5 quintillion bytes of data are created every day. Companies went from pulling data solely from their CRM and ERP systems to pulling data from dozens or hundreds of data sources simultaneously.

Creating data pipelines for all of these data sources is expensive and difficult to maintain. Data engineers are often stretched thin, between constantly updating API connections, to building new pipelines for every data source. Without access to this data, analysts and data scientists cannot provide key insights for organizational stakeholders.

But, in the last decade, software solutions have emerged that can pull data from many different data sources without requiring companies to build infrastructure manually. These managed, low-code platforms increase efficiency and time-to-insights, while freeing engineers to work on more important projects.

SnowflakeDB https://www.snowflake.com/

Rivery https://rivery.io/

If you enjoyed this video, check out some of my other top videos.

What Skills Do Data Engineers Need? https://www.youtube.com/watch?v=LgSHaOvNodA

Data Engineering Project Ideas https://youtu.be/LJkVvNWlO0g

If you want to learn more about machine learning, check out DataCamps Machine Learining Course

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About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consults on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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