A new vulnerability that impacts devices and applications that use Java has been identified in Log4j, the open-source Apache logging library. Known as Log4Shell, the flaw is the most significant security vulnerability currently on the internet, with a severity score of 10-out-of-10. Fortunately, Perforce static analysis and SAST tools — Helix QAC and Klocwork — can help.

Clear, concise, and executable requirements help development teams create a proper product. How do we organize and present these requirements? That's where a Software Requirements Specification (SRS) comes in. But what is an SRS, and when should you use one? In this blog, we'll outline a typical software requirements specification, including how to define your product's purpose, describe what you're building, detail the requirements, and, finally, deliver it for approval.

As we’re getting closer to the end of 2021, it’s time to look back at those moments in the year when we pushed the needle forward serving the mobile community, helping teams achieve mobile product success. Let's have a recap!

As a new calendar year approaches, public sector CIOs and IT leaders are preparing for another year of change in their technology stack and its role in accomplishing their mission. The last two years have brought immense change and shifting imperatives to the public sector. Perhaps one of the most impactful is the drastic acceleration of digitization initiatives.

As we head into the holidays, there’s no better time to talk about bringing people together. And there’s no better way to bring employees together within a company aspiring to be data-driven than with a data literacy program. What data analytics processes should your organization put into place to increase data literacy? It all starts with establishing a training program to empower your people to work with data, regardless of their level of expertise.

Amazon S3 (Simple Storage Service) has been around since 2006. Most use this scalable, cloud-based service for archiving and backing up data. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size. Enterprises value their data as something worth preserving. But much of this data lies inert, in “cold” data lakes, unavailable for analysis. Also called “dark data”, it can hold key insights for enterprises.

Redshift’s JOIN clause is perhaps the second most important clause after SELECT clause, and it is used even more ubiquitously, considering how interconnected a typical application database’s tables are. Due to that connectivity between datasets, data developers require many joins to collect and process all the data points involved in most use cases. Unfortunately, as the number of tables you’re joining in grows, so does the sloth of your query.

Vertica claims to offer the "most advanced unified analytical warehouse" in the world, providing actionable data insights you can't find anywhere else. The truth is, like any data warehouse, Vertica is only as good as the data you put into it. Moving data to Vertica can be a headache for organizations without a data engineering team. Data might live in various locations — transactional databases, relational databases, customer relationship management (CRM) systems, you name it.

PostgreSQL is the preferred platform of millions of developers around the world. The open-source tool is one of the most powerful databases on the planet, with the ability to handle sophisticated analytical workloads and high levels of concurrency. That makes PostgreSQL (also called Postgres) a popular DB for scientific research and AI/ML projects. It’s also a popular production database for data-driven companies in every industry. But no database is perfect.

After a year packed with one machine learning and data science event after another, it’s clear that there are a few different definitions of the term ‘MLOps’ floating around. One convention uses MLOps to mean the cycle of training an AI model: preparing the data, evaluating, and training the model. This iterative or interactive model often includes AutoML capabilities, and what happens outside the scope of the trained model is not included in this definition.