Systems | Development | Analytics | API | Testing

Automate the process of building and maintaining data pipelines to free up data engineers for more interesting, mission-critical projects.

Looking to increase your Customer Lifetime Value? Here are 18 tips provided by experts that’ll make your customer stick around for longer!

What’s the best marketing software for your agency? We asked dozens of agencies to share their “must-have” tools––here are the 38 they recommend most.

If you’ve ever used Ruby on Rails, you’ve probably come across the concept of concerns. Whenever you jumpstart a new Rails project, you get a directory app/controllers/concerns and app/models/concerns. But what are concerns? And why do people from the Rails community sometimes talk badly about them?

As a follow up to our previous blog post about retrieving related data from that REST API, I wanted to dive deeper into the power of the “related records” feature of DreamFactory’s database-backed APIs. One of the powerful features of SQL databases is the ability to relate separate data records and perform queries that retrieve that related data all at once. One problem is that this requires a fairly in-depth knowledge of your database, it’s schema, and SQL syntax.

Hunting for the best SEO plugin for your Wordpress website? 52 marketing professionals share their favorite Wordpress SEO plugins.

One of the Scout’s key features is its ability to quickly highlight N+1 queries in your application that you might not have been aware of, and then show you the exact line of code that you need to look at in order to fix it. In this video, we will use a Ruby on Rails application as an example, but the same concepts apply to other popular web frameworks.

It's easy to get lost in today's continuously changing landscape of cloud native technologies. The learning curve from a beginner's perspective is quite steep, and without proper context it becomes increasingly difficult to sift through all the buzzwords. If you have been developing software, chances are you may have heard of Kubernetes by now. Before we jump into what Kubernetes is, it's essential to familiarize ourselves with containerization and how it came about.

Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.

A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.