Earlier this month, an executive order was issued from the White House aimed at improving the cybersecurity of the United States. This much-anticipated order comes on the heels of widely publicized digital attacks, such as Colonial Pipeline and SolarWinds, which illustrate the current state of cybersecurity standards in the software industry.

Many IP and data management tools provide a project-centric view of the design process, creating what are known as design silos. While this makes some sense, as most designers tend to think of themselves as working on a project at time, and other items within the company are tracked on a project basis (such as cost, resources, and timelines), this approach is not without its drawbacks.

In an unforeseen move earlier this year, Atlassian announced that they were depreciating server products to accelerate movement to cloud products. For teams that utilize Jira Server products, the announcement started the clock on a pivotal decision. Users of Jira Server products have until February 2nd, 2024 to integrate into Jira Cloud products, or migrate into an on-prem server solution. This change has had and will continue to have a wider effect of other parts on the ecosystem of DevOps tools.

Over the past 18 months, organizations awoke to the need for digital innovation and transformation as an existential issue. Business leaders recognized that software is central to digital innovation. As the need to quickly create more apps grew, demands on IT skyrocketed. So did the IT application development backlog. Technical debt is a part of software development.

The General Data Protection Regulation (GDPR) is a landmark piece of legislation that affects how organizations can handle, process, and store the personal data of European Union (EU) citizens and residents. But what does the GDPR require exactly, and how can you be sure that your organization complies with it? We go over everything you need to know in this all-in-one guide to GDPR compliance.

ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts. Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed.

This blog post was written by Pedro Pereira as a guest author for Cloudera. Right now, someone somewhere is writing the next fake news story or editing a deepfake video. An authoritarian regime is manipulating an artificial intelligence (AI) system to spy on technology users. No matter how good the intentions behind the development of a technology, someone is bound to corrupt and manipulate it. Big data and AI amplify the problem. “If you have good intentions, you can make it very good.

The accelerated exploitation of new technologies and data drive successful digital transformation and is key to gaining competitive business advantage. Hence, executives place a premium on innovation, flexibility and acceleration.

Use these criteria to choose the best ETL tool for your data integration needs.