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.

Organizations are swimming in data, but are they actually getting the most value out of it? A recent report by Seagate and IDC found that while enterprise data is projected to increase at 42.2 percent annually over the next two years, up to 68 percent of it may go unused.