Traditional databases suffer a problem when ingesting data. They operate on a schema-on-write approach where data indexed must have a predefined schema as you ingest your data into the database. This schema-on-write model means that you need to take time in advance to dive into your data and understand what is there, and then process your data in advance to fit the defined schema.

Thanks to all who attended our second annual Dash conference! We hope that you enjoyed your time with us at New York City’s Chelsea Piers, and that you were able to learn about building and scaling systems and teams in our breakout sessions and workshops. For those of you who were unable to attend, we hope to see you next year. Check out some of the highlights from our two-day conference below.

Hive and Spark are two very popular and successful products for processing large-scale data sets. In other words, they do big data analytics. This article focuses on describing the history and various features of both products. A comparison of their capabilities will illustrate the various complex data processing problems these two products can address.

At Honeybadger, we use Redis a lot. It's our Swiss Army Knife; it's a cache, a single source of truth, it stores background jobs, and more. Basically, Redis is one of those services that should never fail. I was pondering the DevOps apocalypse recently, as one does (could Redis be one of the four horsemen?), which led me to jump into our #ops channel to ask Ben a simple question: what are the risks if someone executed flushall on our redis instances?

Identities are easy to fake, but not actions. Closely monitoring the behavior of a person can reveal a lot about their true intentions. Similarly, keeping a close watch on a machine’s activities can expose potential security problems. Blending security information and event management (SIEM) with user and entity behavior analytics (UEBA) can bring numerous users and devices belonging to an enterprise under surveillance.

It’s not the first time I’ve been asked by a sales rep the following question: “The customer has looked at Stackalytics and is wondering why Rancher doesn’t have as many code commits as the competition. What do I say?” For those of you unfamiliar with Stackalytics, it provides an activity snapshot, a developer selfie if you will, of commits and lines of code changed in different open source projects.

We’re excited to announce the launch of our new product to the ManageEngine family, Device Control Plus — the Data loss prevention (DLP) solution you need to control removable devices at any stage.

The underlying infrastructures behind IT systems have become complex and overloaded. A single incident in today’s IT stack can shut down large chunks of a business and cost it millions – or even billions. Because of that, many businesses consider implementing AIOps in their IT operations an important part of their future.

Life is full of surprises and we look at in a positive way. But the same cannot be said about business. In business, surprises are unwelcome and you have to be alert about changes happening around that will affect it. This helps in adapting to the new business landscape and change the way of doing business.

Incident escalation is one of those seemingly mundane issues, the importance of which is often underestimated. Simply put, the escalation process is used to flag issues so that the relevant personnel can respond to situations. Implementation of a well-designed escalation process, however, is anything but simple.