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Introducing Component Previewer

The component previewer is a feature that allows you to preview your data at each component step without having to validate packages and run full-scale production jobs. It gives you the ability to extract, transform and preview your data on any transformation component, allowing you to debug your pipeline and/or to confirm and validate your data flow logic. Component previews are similar to the data previews available on source components, which you might already be familiar with.

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Stitch vs. Dell Boomi vs. Xplenty: Battle of 3 ETL Platforms

Five differences between Stitch vs. Dell Boomi vs. Xplenty: Real-time data provides a competitive advantage, so every business requires an analytics strategy. But many organizations struggle to integrate data because they store information in lots of locations, including apps, SaaS, and legacy systems. Extract, Transform, and Load (ELT) makes it easier for companies like yours to access data in disparate locations and move it to one centralized system.

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How to Check CloudFront Logs for Big Data Collection

AWS provides many solutions for managing business data. There’s Amazon Relational Database, or Amazon RDS, which is ideal for scaling your databases on the cloud. There’s Amazon Redshift for warehousing your data. For collecting big data, we’ve looked at a number of modern data integration platforms, but Amazon CloudFront is more of a content delivery platform. So, why are we talking about CloudFront in terms of big data right now?

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Scheduling With Cron Expressions in Xplenty

One of the most requested features in a data integration tool is greater flexibility around the scheduling of packages and workflows. With Xplenty, this can be achieved through the use of our Cron Expression scheduling feature. Cron is a software utility that enables Unix-based operation systems, such as Linux, to use a job scheduler. You can create cron jobs, which execute a script or command at a time of your choosing. Cron has broad applications for tasks that need time-based automation.

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How Do Data Pipelines Fit Into Your Data Stack?

The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now — customer records, sales orders, chain reports, emails, you name it. Companies need all this data for data analytics — the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove.

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Xplenty's Industry Leading Support - An Extension To Your Data Team

A cornerstone to any successful SaaS business is great customer support. At Xplenty, one of our four key pillars is ‘Providing Fanatical Support’. For those of you who have been fortunate enough to work with our amazing Support team, you will know that they always go above and beyond to deliver fanatical support.

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Xplenty and Package Version Control

Version Control is a critical component of any software development team, particularly if you're collaborating with a large group of individuals. When done right, Version Control will help you track changes over time, like scheduling specific versions to accommodate the development of new features and bug fixes. You can even rollback to a specific version with ease as you continue testing

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Transform Your TV Into a Powerful SaaS KPI Dashboard in 4 Simple Steps

With 42 percent of Americans still working from home, we're using TVs for more than just Netflix. These viewing screens double up as dynamic digital dashboards, displaying powerful SaaS metrics that power your business. Whether you're working from home or the office, turning your TV into a SaaS KPI dashboard is simple. Five tools are all you need. This guide shows you how to bring business metrics to the small screen with your very own custom SaaS KPI dashboard.

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Stitch vs. MuleSoft vs. Xplenty: Which ETL is the Winner?

Five differences between Stitch vs. MuleSoft vs. Xplenty: Organizations of all types need to pull data from disparate locations for data analysis. But the average company draws data from over 400 sources, making data integration difficult. Imagine if a technology could compile data from locations such as in-house databases, cloud-based apps, and SaaS and move it all into a centralized location. Extract, Transform, Load (ETL) makes this possible.