The faster you can extract, transform, and load data from MongoDB, the better it is for your business processes and business intelligence systems. The problem is, most ETL solutions struggle to manage MongoDB’s dynamic schemas, NoSQL support, and JSON data types. That’s not the case with Xplenty – which was optimized for easy, no-fuss MongoDB integrations with ease: no custom code, no delays, no confusion.

Heroku is a powerful platform for application development. Users can build and deploy on the cloud, and you can effortlessly scale up once your app takes off. And behind every app, you'll find an equally powerful database: Heroku Postgres. If you're building Heroku apps, you'll find them to be a rich source of operational and customer data. Add in the right Business Intelligence (BI) tools, and you'll be able to derive insights about the inner workings of your organization.

When it comes to data storage, there is almost as much diversity in the types of databases as there is in the data that they contain. Designing and implementing a strong enterprise data strategy means that you need to be aware of the different databases and how you might best apply them within your organization. In IT, the term "flat file" means something very different from the heavy-duty steel construction file cabinets that you might buy from Safco.

MongoDB is a popular non-relational (a.k.a NoSQL) database. It is document-oriented and distributed in nature. MongoDB is known to be highly scalable and flexible. In this post, we'll demonstrate how you can utilize MongoDB in your ETL pipelines with Xplenty. To start with, let us briefly discuss why and when you'd want to use MongoDB over other relational databases.

Optimizing server performance is important in supporting end-user requirements. Using server optimization, actively monitor: Web server monitoring and optimization helps you to troubleshoot bottlenecks as they emerge and optimize server performance. In this post, we will discuss how to optimize performance and why it is important.

Database replication for database analytics involves many serious difficulties.

In today’s post, we’ll dive into how we, at AppSignal, solved a daunting engineering challenge. Giving you a look into the kitchen, this post will show you how we tested a new database in production without having to worry about errors/downtime. Alright, let’s get cooking!

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 problem; Xplenty does that as well.