Related papers: From DevOps to DevDataOps: Data Management in DevO…
The parallel and distributed processing are becoming de facto industry standard, and a large part of the current research is targeted on how to make computing scalable and distributed, dynamically, without allocating the resources on…
Open data is an emerging paradigm to share large and diverse datasets -- primarily from governmental agencies, but also from other organizations -- with the goal to enable the exploitation of the data for societal, academic, and commercial…
This chapter presents software architectures of the big data processing platforms. It will provide an in-depth knowledge on resource management techniques involved while deploying big data processing systems on cloud environment. It starts…
Background: Security tools play a vital role in enabling developers to build secure software. However, it can be quite challenging to introduce and fully leverage security tools without affecting the speed or frequency of deployments in the…
Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve…
This paper explores the essential areas of cybersecurity management for big data systems. Big data platform stems its complexity from being a collection of interrelated non-standardized systems that interact with each other to process large…
As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For…
DevOps pipeline is a set of automated tasks or processes or jobs that has tasks assigned to execute automatically that allow the Development team and Operations team to collaborate for building and deployment of the software or services.…
Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate…
Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different…
Recent technology breakthroughs have enabled data collection of unprecedented scale, rate, variety and complexity that has led to an explosion in data management requirements. Existing theories and techniques are not adequate to fulfil…
Quality in software is often understood as "execution according to design purpose" whereas security means that "software will not put data or computing systems at risk of unauthorized access." There seems to be a connection between these…
In today's fast-paced digital world, data has become a critical asset for enterprises across various industries. However, the exponential growth of data presents significant challenges in managing and utilizing the vast amounts of…
Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of…
Improving the performance and reducing the cost of cloud data systems is increasingly challenging. Data processing units (DPUs) are a promising solution, but utilizing them for data processing needs characterizing the new hardware and…
DevOps responds the growing need of companies to streamline the software development process and, thus, has experienced widespread adoption in the past years. However, the successful adoption of DevOps requires companies to address…
The amount of data in the world is expanding rapidly. Every day, huge amounts of data are created by scientific experiments, companies, and end users' activities. These large data sets have been labeled as "Big Data", and their storage,…
DevOps integrates collaboration, automation, and continuous improvement, enhancing agility, reducing time to market, and ensuring consistent software releases. A key component of this process is GitLab's Merge Request (MR) mechanism, which…
This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often…
Software as a Service (SaaS) pricing models, encompassing features, usage limits, plans, and add-ons, have grown exponentially in complexity, evolving from offering tens to thousands of configuration options. This rapid expansion poses…