Welcome to Xplenty's Blog

All things data

How to Prepare IBM SoftLayer Data for Analysis

How to Prepare IBM SoftLayer Data for Analysis

IBM joined the cloud computing market when it bought SoftLayer in 2013. One of the services that it provides is cloud-based storage, just like Amazon S3 and Rackspace. You can use it to store gigabytes or even terabytes of data, but at some point, you will need to analyze that data.

Processing Data from Rackspace Cloud Files

Processing Data from Rackspace Cloud Files

Amazon is not alone in the cloud: Rackspace, a cloud computing company that was established back in 1998 in Texas, is one of its major competitors. It also founded OpenStack, the open-source operating system for the cloud. So you may have gigabytes or even terabytes of data stored away on Rackspace, but what are you going to do with them?

Transform Data from Amazon RDS with Xplenty

Transform Data from Amazon RDS with Xplenty

How do you integrate data from Amazon RDS (Relational Database Service) with data from other sources such as S3, Redshift, or even MongoDB? The answer is Xplenty. Our data integration on the cloud lets you join data from various sources and then process it to gain new insights. What about storing the results back to RDS? No problemo, Xplenty does that as well.

Prepare Data for Analysis in Heroku

Prepare Data for Analysis in Heroku

Some developers need to process data. Maybe you work in a small startup where people take on several roles, or maybe in an enterprise company where you are asked to deal with Big Data. Either way, if you use Heroku, the most popular platform-as-a-service, there is a new add-on that can help you or the BI guy next door: Xplenty’s data integration on the cloud.

Data Integration on the Cloud with Heroku PostgreSQL and Xplenty

Data Integration on the Cloud with Heroku PostgreSQL and Xplenty

What can you do with data collected on Heroku PostgreSQL? How will you analyze it and integrate it? With Xplenty, of course! Xplenty lets you connect to a PostgreSQL database on Heroku, design a dataflow via an intuitive user interface, aggregate the data, and even save it back to PostgreSQL on Heroku or other databases and cloud storage services.