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This post discusses the most common requirements of a data lake. Each section details the requirements followed by the technical implementation. If you are only interested in a particular requirement, directly jump to that section. An introductory read is [1] Benefits and concrete use cases of data lakes are discussed in.

You’ve made the decision to migrate to the cloud, and you’re eager to get started. But before you jump into creating a proof of concept (POC), it’s important to take a step back and think about your people. Why? In our experience, it’s common for the team—not the technology—

The General Services Administration (GSA) needed a way to keep the personal information of their more than 30 million customers safe—while making the log-in process as easy and painless as possible. In the past, government users had to create multiple logins for various agencies.

For colleges and universities around the country, the government’s embrace of the new Cybersecurity Maturity Model Certification (CMMC) means creating a plan to get compliant—or risk losing out on millions of dollars in grant and research funding. Follow along to learn the basics.

Whether we realize it or not, artificial intelligence and machine learning (AI/ML) have become part of our daily lives—from customer service chatbots to AI-generated recommendations from your favorite streaming services. But despite the ubiquity of AI/ML, executives often don’t consider how they could use this technology…

The simplest definition of a data lake is ‘a centralized repository to store unstructured and structured data at any scale’. Data stored as-is can be used for data mining or machine learning, while structured data can be used in different analytics applications like dashboards, real-time analytics to big data processing.

With the capability to store high-volume, high-velocity raw data in a centralized location, data lakes are currently the most desirable technology for businesses seeking to reduce silos and maximize data value. The pay-as-you-go price model, the low cost of storage, and groundbreaking advances in big data technology make data lakes very affordable for any project.

No matter your industry, chances are your team is feeling the strain of the cybersecurity talent gap. For nearly a decade, the demand for cybersecurity experts has surpassed the number of skilled workers. “It’s a full-on war for cybertalent,” Matt Comyns, a seasoned cybersecurity expert, told the Los Angeles Times in 2019.