Customer success is much more than a function.

There are a plethora of browsers available in the market for both web browsing and mobile browsing such as Internet Explorer, Google Chrome, Mozilla Firefox, Safari, Opera, etc. Now, the challenge here is that no one browser is similar to others or works in a similar fashion. A website may look and function differently on different browsers which can result in a loss in revenue.

We've recently released a new plugin for Netlify that allows you to automatically run Ghost Inspector tests on every deploy, including deploy previews! By default, Netlify deploys your production branch on every change. To enable deploys for other branches, follow the Netlify instructions: With branch deploys enabled, you can now run Ghost Inspector tests on your website before deploying to production!

We looked at service design considerations in the first part of this blog series. In this next part, I’d like to share some best practices for API versioning – a topic that comes up quite often with every customer as it is one of the key concerns when implementing API gateways. There are two ways to version RESTful APIs: URI and header-based, as summarized in this REST API tutorial.

With summer just around the corner, things are really heating up. But you’re in luck because this month BigQuery is supplying a cooler full of ice cold refreshments with this release of user-friendly SQL capabilities. We are pleased to announce three categories of BigQuery user-friendly SQL launches: Powerful Analytics Features, Flexible Schema Handling, and New Geospatial Tools.

In this post, we will discuss some of the primary commands, tools, and techniques that could help to monitor CPU Memory and Disk Usage in Java. The Java tools observe Java bytecode constructs and processes. Java Profilers follow all system commands and processor usage. This lets you look at call arrangement at whatever point you prefer.

Data analysis is a key component for operating a successful business in today's tech-savvy world. When analyzing data sets, however, every business has its own needs. While some companies employ data scientists to work with complex big data, others have fewer and less complicated data sources that even non-technical users can navigate. Your specific needs will influence the type of tool your company chooses for data management.

With billions of industrial IoT (IIOT) devices in place, generating massive volumes of data from “the edge,” the potential for proof of concept success for use cases in the factory can be paralyzing. While the value of this digital revolution, aka Industry 4.0, is clear, realizing the full promise has been slow. Research and real-life experience from Accenture shows that many manufacturers get stuck early on or can’t get beyond proof-of-concept pilots to scale.

The move into any new technology requires planning and coordinated effort to ensure a successful transition. This blog will describe the four paths to move from a legacy platform such as Cloudera CDH or HDP into CDP Public Cloud or CDP Private Cloud. The four paths are In-place Upgrade, Side-car Migration, Rolling Side-car Migration, and Migrate to Public Cloud.

Many people consider data-driven culture exclusively as one that comprises a large data team (analysts, data scientists), perfect data-sets neatly organized in a structured data warehouse, and the perfect tools to both access and utilize data in every decision made. Ultimately, however, much of data-driven culture comes down to a clear focus on the end-goal. Many people forget data is just an implementation, something that helps us in what we do.