These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.

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.

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.

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?

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.

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.

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.

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.

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.

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.

Extract, Transform, Load technology sits between your data source and its destination in your data stack. It’s a useful way of delivering data from multiple applications, databases, and other sources to your CRM, data lake, or data warehouse for analysis and use. But how do you know that it’s time to add ETL to your organization’s data stack?

In Part 1 we learned how to set up our Xplenty pipeline to work with Chartio and prepared the data source. In Part 2, we will focus on using the data Xplenty provides in the Chartio platform. If you're new to Chartio, you can read through their QuickStart docs (shouldn't take more than 5-10 minutes) to gain some familiarity.

Five differences between Stitch, Talend, and Xplenty: Organizations store data in many destinations, making that data difficult to analyze. Legacy systems, SaaS locations, in-house databases, apps, you name it — by storing data in all kinds of places, companies can complicate data analytics considerably. Storing data in a warehouse or a lake makes more sense.

Are you tired of slow dashboards? It’s a problem we hear end-users of BI tools complain about time and time again. Whether you’re an end-user or on the data team that the end-users blame, slow dashboards suck! With many BI tools now offering their own connectors and lightweight data transformation/preparation layers, slow dashboards are a common pain point across all organizations.

Xplenty provides features to efficiently extract, transform, and store data from various sources. Chartio provides Visual SQL features that let us explore and analyze data. Furthermore, it includes functionality to arrange charts and metrics in dashboards that can be shared. Both these tools can be used synergically. In this post, we will cover how you to configured Xplenty to use Chartio data. In a subsequent post, we will explain how to visualize the data provided by Xplenty in Chartio.

The data lakes concept has come back into popular focus with Amazon Athena, an innovative, serverless solution. But does it fit into your organization’s data stack? This article covers Amazon Athena capabilities, pros and cons, competitors, and use cases.

How vulnerable is your sensitive data? Your data policies may put this information at risk of being breached. An ad hoc approach for dealing with this data makes it difficult to maintain your organization’s cybersecurity. Data obfuscation holds the key to improving your security and making it easier to use your data, but it must be driven by your policies to be effective.

Are you asking yourself the question “what is no-code”? You’re not alone. The concept sounds almost too good to be true: developing your own software applications without ever having to learn a programming language like Java or Python. Even your most technophobic employee can become a star software developer thanks to the proliferation of no-code development tools.

If you're a developer trying to ETL data into and out of Heroku, the seemingly shortlist of options may disappoint you. Heroku itself promotes Heroku Connect, but this expensive solution might not even integrate with all the systems you use (like AdWords and Facebook), making it difficult to get a holistic view of your data. Fortunately, Heroku Connect isn't the only solution. In fact, there are several third-party ETL tools that can help you get your data in and out of Heroku with ease.