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Unlocking Success With the Databox Customer Lifecycle Framework

At Databox, we put our company values at the forefront of everything we do. Prioritizing customer impact is one of the values we focus on the most, so taking the time to really understand our customers is paramount and we employ multiple strategies, frameworks, and initiatives on a daily basis to achieve this. One of those strategies is our Customer Lifecycle Framework (CLF), which reflects our dedication to prioritizing the needs of our customers at every stage of their interaction with us.

Top 3 Data + AI Predictions for Retail and Consumer Goods in 2024

Nearly every facet of society has felt the impact of AI since it burst into the mainstream in late 2022 with the public launch of ChatGPT. In 2024, the retail and consumer goods industry is expected to experience massive upheaval due to the proliferation of generative AI (gen AI) tools as well as changes in customer engagement and the general manner in which products are now sold.

Changing expectations: How DevSecOps and codeless automated software testing can help public sector agencies deliver on their missions

It’s no secret that the expectations for public sector digital services have changed significantly over the past few years; services need to be rolled out fast to ensure usable and secure software that can help agencies deliver on their mission. These expectations can be met by combining DevSecOps with codeless automated software testing.

Mock Vs Stub Vs Fake: Understand The Difference

Testing software is like putting it through a series of challenges to make sure it’s tough enough for real-world use. Whether we’re testing each piece individually (unit testing) or how they all work together (integration testing), we need to be prepared for different situations. Sometimes, testing is tricky.There are times, when we need to isolate parts of our code from their dependencies.

Kong Konnect Data Plane Node Autoscaling with Cluster Autoscaler on Amazon EKS 1.29

After getting our Konnect Data Planes vertically and horizontally scaled, with VPA and HPA, it's time to explore the Kubernete Node Autoscaler options. In this post, we start with the Cluster Autoscaler mechanism. (Part 4 in this series is dedicated to Karpenter.)

6 Game-Changing Use Cases of Gen AI in Site Reliability Engineering

Today, Site Reliability Engineering (SRE) emerges as the key player in the fast-paced modern industries, where the demand for seamless software delivery collides with the need for reliability, maintaining this delicate equilibrium. It’s not merely a role; it’s a strategic position that safeguards system health while strategically mitigating the financial pitfalls associated with downtime.

Adopt a Zero Trust Approach with OAuth 2.0 Mutual TLS Client Authentication

In the modern IT stack, API gateways act as the first line of defense against attacks on backend services by enforcing authentication/authorization policies and validating and transforming requests. When backend services are protected with a token-based approach, client applications must obtain an access token to access the protected resource.

Snowflake vs. Oracle: Which Data Warehouse is Better?

Snowflake and Oracle Autonomous Data Warehouse are two cloud data warehouses that provide you with a Single Source Of Truth (SSOT) for all the data that exists in your organization. You can use either of these warehouses to run data through business intelligence (BI) tools and automate insights for decision-making. But which one should you add to your tech stack?

Software Testing Reports: 7 Essential Steps

In the dynamic landscape of software development, where agility and precision are paramount, testing reports emerge as a cornerstone for quality assurance, tracking progress, and fostering effective communication among stakeholders. Let's embark on a journey to explore the intricacies of testing reports, unraveling their significance, types, content, and the tools that can elevate your reporting game.

Best 13 Free Financial Datasets for Machine Learning [Updated]

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.