If Ben Franklin lived today, he would add cyber threats to his shortlist of life’s certainties. For decades, bad guys have inflicted malware, theft, espionage, and other forms of digital pain on citizens of the modern world. They seek money, celebrity, and political secrets, and often get them. In 2020, hackers halted trading on the New Zealand stock exchange with a distributed denial of service (DDoS) attack.

Exciting news — ChaosSearch won the 2021 InfoSec “Cutting Edge in Cybersecurity Analytics” award from Cyber Defense Magazine! We’re honored to be recognized for our innovation in delivering security insights at scale. The InfoSec panel of judges is made up of certified security pros who understand what SecOps teams care about and how log analytics should be applied to keep data secure.

Properly analyzing the massive amounts of data created by network access and the associated security tools has become a very tedious chore. Today’s cybersecurity professionals are seeking ways to better deal with the massive influx of information so that they can make intelligent choices when it comes to the cybersecurity posture of their networks. Selecting the proper tools is an important task which merits investigation.

Security analysts have long been challenged to keep up with growing volumes of increasingly sophisticated cyberattacks, but their struggles have recently grown more acute. Only 46% of security operations leaders are satisfied with their team’s ability to detect threats, and 82% of decision-makers report that their responses to threats are mostly or completely reactive — a shortcoming they’d like to overcome.

The success of data-driven initiatives for enterprise organizations depends largely on the quality of data available for analysis. This axiom can be summarized simply as garbage in, garbage out: low-quality data that is inaccurate, inconsistent, or incomplete often results in low-validity data analytics that can lead to poor business decision-making.

At first glance, there may seem to be little not to love about the ELK Stack. It’s open source. It’s free (if you set it up and manage it yourself, at least). It’s a widely used solution with a thriving ecosystem surrounding it. But if you’ve ever actually built and managed an ELK stack environment, you have probably found that the theory doesn’t match the reality. The ELK stack is full of hidden costs, and it often fails to deliver real value over the long term.

If your business is among the more than one million organizations that use Amazon Web Services (AWS) to host applications and data, there is a good chance that you struggle to monitor AWS. After all, although AWS makes it easy to deploy cloud services, collecting and analyzing data about those services in an efficient, centralized way can be a real challenge.

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically.

DevOps is a philosophy, a culture, and a set of practices adopted by product teams to shorten the software development life cycle, enhance collaboration and visibility, and accelerate time to market for new updates while ensuring high-quality releases. At the core of the DevOps principal is the organization of software development (Dev) and IT Operations (Ops) engineers into cross-functional teams that can effectively build, run, and monitor their own software releases.