Citrix Moves Data to Amazon Redshift Fast with Matillion ETL

Citrix, a Fortune 1000 multinational software company, needed to migrate massive amounts of data generated by its FileShare platform to Amazon Redshift, where it could perform data analytics to help inform product improvements and drive success. To do this, Citrix needed an Extract, Transform, and Load (ETL) solution. Matillion ETL, easily deployable from Amazon Web Services (AWS) Marketplace, helps Citrix collate and summarize data and augment it with more traditional business data from Microsoft SQL Server for additional context. Join our webinar to learn how organizations of any size can move data to the cloud quickly, accurately, and affordably with Matillion ETL.
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Spotlight

OTHER ON-DEMAND WEBINARS

Financial Analytics Fundamentals Use Data Science And Analytics In BFSI

analyticsindiamag

For a data analyst, financial analytics is one of the most useful skill sets to inculcate given the rapid growth of roles in this booming sector. A specialisation in this field will be a value-add given the rapid rise of fintech in India’s digital ecosystem. Financial analytics helps companies uncover deeper insights into the company’s finances and can help in finding pain points and new streams of revenue. Financial Analysts work in investment firms, banks, insurance sector and digital payments companies.
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Practical tools and techniques for building a brand that is trusted with data

In this ODI Inside Business webinar you’ll discover how you can help your business work towards six critical outcomes that together contribute to developing a brand that is trusted with data.
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SMALL DATA, BIG INSIGHTS

Agillic

There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.
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Moving from centralized data platform to a federated data mesh

In this webinar, we will cover the pros and cons of building a centralized data lake vs federated data mesh. Traditionally data warehouses are built on the premise of centralized data. This requires team, process and tool alignment which adds significant complexity and layers of process. Oftentimes the internal conflicts lead to subpar data management and quickly fragments to siloed data processing and insights. We will provide our unbiased view on building federated data mesh and the benefits of building an operational metrics layer.
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