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Matthew Scullion

4 Podcast Episodes

Latest 9 Oct 2021 | Updated Daily

Weekly hand curated podcast episodes for learning

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Reinventing the Cloud with Matthew Scullion, Founder and CEO of Matillion

Rise of the Data Cloud

This episode features an interview with Matthew Scullion, Founder, and CEO of Matillion. Matthew has spent more than 20 years revolutionizing IT and software development, and just raised $100M in Series D funding. In this episode, Matthew dives deep into data transformation. He shares how Matillion is pushing the world of software forward, how their partnership with Snowflake is advancing the industry, his predictions for 2021 data trends, and much more. _________ This podcast is sponsored by Snowflake, the Data Cloud company.  Inside the Data Cloud, organizations unite their siloed data, discover, and securely share data, and execute diverse analytic workloads across multiple clouds. Join fifty thousand of your peers at Snowflake's annual global user conference, Snowflake Summit, this June 8th through 10th. Hear from Snowflake customers, industry thought leaders and more about how they bring Data Together Now with the Data Cloud. Learn more and register at snowflake.com/summit

46mins

1 Jun 2021

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How to Effectively Action the Growth of Data with Matthew Scullion

EM360 Podcast

In this podcast, Matthew lends his expertise to explore the growth of data and how to action it effectively with the findings of a MarketPulse survey by IDG research and Matillion. Tune in to hear Matthew’s advice on gaining actionable insights and his predictions for the future of data warehousing.Matthew Scullion is the Founder and CEO at Matillion. Matthew has a demonstrable history working in commercial IT and software development at a number of British and European system integrators. He then started Matillion in 2011 and has since expanded the company, opening offices in the USA.

43mins

1 Apr 2020

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Data Warehouse ETL with Matthew Scullion

Software Daily

A data warehouse provides low latency access to large volumes of data. A data warehouse is a crucial piece of infrastructure for a large company, because it can be used to answer complex questions involving a large number of data points. But a data warehouse usually cannot hold all of a company’s data at any given time. Users need to move a subset of the data into the data warehouse by reading large files from a data lake on disk and putting that data into the data warehouse.The process of moving data from one place into another is broken down into three sequential steps, often called “ETL” (extract, transform, load) or “ELT” (extract, load, transform). In ETL, the data is extracted from a source such as a data lake, transformed into a schema that is customized for the data warehouse application, and then loaded into the data warehouse. In ELT, the last two steps are reversed, because modern systems can often leave the necessary schema transformation until after the data has been loaded into the data warehouse.Matthew Scullion is the CEO of Matillion, a company that specializes in building tools for data transformations. Matthew joins the show to talk about the problem of data transformation, and how that problem has evolved over the nine years since he started Matillion.If you enjoy the show, you can find all of our past episodes about data infrastructure by going to SoftwareDaily.com and searching for the technologies or companies mentioned. And if there is a subject that you want to hear covered, feel free to leave a comment on the episode, or send us a tweet @software_daily.

14 Feb 2020

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Data Warehouse ETL with Matthew Scullion

Data – Software Engineering Daily

A data warehouse provides low latency access to large volumes of data.  A data warehouse is a crucial piece of infrastructure for a large company, because it can be used to answer complex questions involving a large number of data points. But a data warehouse usually cannot hold all of a company’s data at any given time. Users need to move a subset of the data into the data warehouse by reading large files from a data lake on disk and putting that data into the data warehouse. The process of moving data from one place into another is broken down into three sequential steps, often called “ETL” (extract, transform, load) or “ELT” (extract, load, transform). In ETL, the data is extracted from a source such as a data lake, transformed into a schema that is customized for the data warehouse application, and then loaded into the data warehouse. In ELT, the last two steps are reversed, because modern systems can often leave the necessary schema transformation until after the data has been loaded into the data warehouse. Matthew Scullion is the CEO of Matillion, a company that specializes in building tools for data transformations. Matthew joins the show to talk about the problem of data transformation, and how that problem has evolved over the nine years since he started Matillion. If you enjoy the show, you can find all of our past episodes about data infrastructure by going to SoftwareDaily.com and searching for the technologies or companies mentioned. And if there is a subject that you want to hear covered, feel free to leave a comment on the episode, or send us a tweet @software_daily. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Data Warehouse ETL with Matthew Scullion appeared first on Software Engineering Daily.

51mins

14 Feb 2020

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