Autore: martechcubejohn

In the digital world, the serverless framework is one of the most innovative technologies that allows software developers (SDEs) to build and deploy applications without the requirement to address the underlying server infrastructure.

Numerous organizations are gradually switching to serverless computing frameworks as they help them achieve faster, simpler software development and eliminate traditional monolithic software models. However, implementing serverless computing SDEs requires frameworks that will help them to focus solely on writing code to implement their application’s logic.

In this article, we’ll explore the top five serverless frameworks that SDEs can use to deploy code faster and scale seamlessly.

Ruby on Jets

Software developers who have expertise in the Ruby language and wish to develop applications in this language can opt for Ruby on Jets. Jets further have unique functionalities that can be used to assemble diverse AWS resources. This tool aids in the creation and deployment tasks of programs employing SQS, DynamoDB, AWS Lambda, SNS, and many more.

AWS Amplify

With the AWS Amplify framework, SDEs can rapidly create robust serverless applications for web apps and enjoy unlimited versatility. With a few taps, you can supervise and launch single-page web applications, static websites, server-side-produced applications, and status web pages. Using this application’s intelligent processes, you can easily set up your serverless backends with information, storage, and authorization.

Architect

Architect is a comprehensive framework that uses AWS, node.js, NPM, and other languages to create applications. It is an open-source serverless platform with more than 30 collaborators on GitHub, keeping it safe and reliable to use. It is also quite user-friendly for novice developers, aiding them to operate faster and adapt to changes easily. This framework has the potential to build, operate, and manage serverless applications and further simplifies the configuration and provisioning.

To Know More, Read Full Article @ https://ai-techpark.com/top-five-serverless-frameworks-in-2024/

Related Articles -

Emergence of Smart Cities in 2024

Top Five Best Data Visualization Tools

Trending Category - Threat Intelligence & Incident Response

Explore how AI innovation and sustainable data center management intersect, focusing on energy-efficient strategies to balance performance and environmental impact.

With all that’s being said about the growth in demand for AI, it’s no surprise that the topics of powering all that AI infrastructure and eking out every ounce of efficiency from these multi-million-dollar deployments are hot on the minds of those running the systems.  Each data center, be it a complete facility or a floor or room in a multi-use facility, has a power budget.  The question is how to get the most out of that power budget?

Balancing AI Innovation with Sustainability

Optimizing Data Management: Rapidly growing datasets that are surpassing the Petabyte scale equal rapidly growing opportunities to find efficiencies in handling the data.  Tried and true data reduction techniques such as deduplication and compression can significantly decrease computational load, storage footprint and energy usage – if they are performed efficiently. Technologies like SSDs with computational storage capabilities enhance data compression and accelerate processing, reducing overall energy consumption. Data preparation, through curation and pruning help in several ways – (1) reducing the data transferred across the networks, (2) reducing total data set sizes, (3) distributing part of the processing tasks and the heat that goes with them, and (4) reducing GPU cycles spent on data organization​.

Leveraging Energy-Efficient Hardware: Utilizing domain-specific compute resources instead of relying on the traditional general-purpose CPUs.  Domain-specific processors are optimized for a specific set of functions (such as storage, memory, or networking functions) and may utilize a combination of right-sized processor cores (as enabled by Arm with their portfolio of processor cores, known for their reduced power consumption and higher efficiency, which can be integrated into system-on-chip components), hardware state machines (such as compression/decompression engines), and specialty IP blocks. Even within GPUs, there are various classes of GPUs, each optimized for specific functions. Those optimized for AI tasks, such as NVIDIA’s A100 Tensor Core GPUs, enhance performance for AI/ML while maintaining energy efficiency.

Adopting Green Data Center Practices: Investing in energy-efficient data center infrastructure, such as advanced cooling systems and renewable energy sources, can mitigate the environmental impact. Data centers consume up to 50 times more energy per floor space than conventional office buildings, making efficiency improvements critical.  Leveraging cloud-based solutions can enhance resource utilization and scalability, reducing the physical footprint and associated energy consumption of data centers.

To Know More, Read Full Article @ https://ai-techpark.com/balancing-brains-and-brawn/

Related Articles -

AI in Drug Discovery and Material Science

Top Five Best AI Coding Assistant Tools

Trending Category - Patient Engagement/Monitoring

Explore how Lakehouse Architecture has evolved, merging the best of data lakes and warehouses into one game-changing solution!

It must be noted that the existence of lakehouse architectures has brought some substantial changes in the data architecture landscape. In this evolution process, organizations are still struggling on how to handle complex and diverse data management, to which the answer is the lakehouse model. Lakehouses can be viewed as a better integration of data lakes and data warehouses to provide improved data management systems. This blog post delves into the further evolution of lakehouse architecture and explains its main concepts, recent developments, and transformation of today’s data management.

Historical context and core principles

Before understanding the progression of architectural styles of the lakehouse, it is crucial to look at the basic components of the concept. Earlier, companies used data warehouses for structured data processing and analysis. Data warehouses offered strong and well-developed SQLQuery, transactional, and near real-time query processing for complicated queries. However, it became a drawback when attempting to work with different and more complex types of data that are incompatible with the one-dimensional, rigid structure of a regular list.

On the other hand, data lakes are a concept that appeared as a result of these limitations, allowing managing raw and unstructured information in a big data environment. Data lakes allowed for accepting and storing data in various formats from different sources; however, they did not offer the usage of atomicity, consistency, isolation, and durability (ACID) transactions and performance improvements typical for data warehouses.

Consequently, the architecture of the lakehouse strived to combine these two paradigms into an integrated system that would represent the advantages of both. To summarize, lakehouses are the next step in data organization with their combination of data lake scalability and flexibility and data warehouse performance and control.

Key Advancements in Lakehouse Architecture

Unified Storage and Compute Layer:

The lakehouse architecture brings in a simplified storage and compute layer in their architectural design, thus minimizing the level of complexity. This layer enables organizations to archive data while fulfilling many types of data processing duties, from batch to real-time. The decoupling of compute and storage resources is a great improvement in regards to scale efficiency.

The concept of lakehouse architecture is one of the most significant steps toward improving data handling processes. Lakehouses, on the other hand, offer a combined approach to data lakes and data warehouses that improves scalability, performance, and governance. When employing this innovative architecture, organizations prepare themselves to get the most out of the gathered data, to foster analysis and creativity in a world headed towards a higher dependency on data and information.

To Know More, Read Full Article @ https://ai-techpark.com/the-evolution-of-lakehouse-architecture/

Related Articles -

AI-Powered Wearables in Healthcare sector

Top Five Data Governance Tools for 2024

Trending Category - Mobile Fitness/Health Apps/ Fitness wearables

Today, social media feeds are full of ads, sponsored posts, and videos from influencers promoting the latest finds. The amount of content in feeds is increasing, to more than what customers can consume, leaving organic posts to become invisible and inundated by sponsored content. The more content is published, the more organic reach falls naturally. Because of this, social platforms are becoming a less viable channel for driving traffic.

Social media is a vital marketing tool for every company, but some brands have already cut back on their social engagement, emphasizing their outlook for building meaningful connections with their customers. With 90% of the U.S. population on social media, brands can’t “just leave.” Although social media presence is important, it is also equally important to be engaged with a targeted audience, which is where social media falls short. Social media is great for touchpoints and one-way interactions, but to really connect with customers and open up the door for long-term loyalty, brands must turn to channels that allow them to collect higher-quality data and build two-way, meaningful relationships.

Harnessing the Power of Data

With the inundation of paid ads and influencers, it’s almost impossible for consumers to decipher between what is organic versus what is sponsored. In addition, typical social media channels only give brands access to third-party data. While this data is helpful for targeting ads, the most valuable data — first-party data — that could help understand preferences, the customer journey, and what offers might resonate best, remains elusive. As it stands, brands can’t see what is collected from clicks, browsing habits, and what posts catch the users’ eye – all of which could be used to create more personalized and meaningful experiences for the shopper.

As brands look for ways to overcome this data barrier, an alternative could be beginning to use a brand-owned ​customer ​network. This ​brand-owned channel can serve as an extension of a ​company’s​ e-commerce site and gathers first- and zero-party data via polls and surveys, user-generated content like review posts, and interactive communication via chats. Brands gain a powerful tool to help keep them stay relevant and top of mind for customers with access to this data. From there, they have everything they need to craft personalized offers, exclusive content, and special promotions based on engagements and purchase history – creating a more compelling journey for the consumer that ultimately ends with them purchasing products.

To Know More, Read Full Article @ https://www.martechcube.com/rethinking-social-media-strategies/

Burnout is at an all-time high as employees face ever-increasing productivity expectations while being forced to do more with less. Approximately 82% of employees are at risk of burnout this year, and 95% say their emotional state plays a big part in their productivity. The research underscores the importance of happy, healthy employees–and while it might seem like simple altruism at the corporate level, there are real financial consequences of letting well-being fall by the wayside. Most notably, burnout costs employers as much as $300B annually.

Today, while most research centers on the burnout of knowledge workers at large, one subset group is faring worse than others: software developers. These teams are the foundation of nearly every modern company, and research shows they’ve been stretched thin to the point of crisis. Solving developer burnout must become a top priority for organizations, or they risk losing the very engine that powers their progress.

Burnout: By The Numbers

According to Harness’ State of the Developer Experience Report, relentless workloads are the leading cause of burnout in the software industry and are the primary reason most developers quit. Over half (52%) of developers cite burnout as the main factor driving their peers to leave their jobs. One of the primary causes of burnout is developer toil: the prevalence of manual, repetitive tasks that consume significant time and effort without providing significant value to the business. The report found that nearly half of developers say they can’t release code to production without risking failures. If that code does need to be rolled back, an astounding 67% of developers do so manually.

The problem is exacerbated by scope creep, which almost two-thirds (62%) of developers experience. To keep up with their workload, nearly a quarter (23%) of engineers work overtime at least 10 days a month, and 97% of developers admit to context switching, meaning they move between unrelated tasks throughout the day, further reducing their productivity.

Additionally, hiring isn’t helping quickly enough, as organizations struggle to onboard new hires quickly enough to alleviate the pressure on current employees. The report found that 71% of respondents said onboarding takes at least two months, leaving existing engineering teams to shoulder the extra workload in the interim.

Solving the Developer Burnout Crisis

While the challenges are steep, there are numerous ways to circumvent these problems and ultimately improve developer mental health:

Automate Toil

Automating toil refers to the process of leveraging technology to eliminate repetitive, mundane, and time-consuming tasks, freeing up human resources for more strategic and creative endeavors. By implementing advanced software that automates monotonous tasks, such as code rollbacks, organizations can enhance efficiency, reduce the risk of errors, and improve overall productivity. This approach not only streamlines workflows but also enables employees to focus on higher-value activities that require critical thinking and innovation. Consequently, automating toil is a key strategy for leaders looking to optimize DevOps while improving the employee experience.

To Know More, Read Full Article @ https://ai-techpark.com/cost-of-developer-burnout/

Related Articles -

Data Privacy With CPOs

CIOs to Enhance the Customer Experience

Trending Category - AI Identity and access management

Serverless architecture, or FaaS (Function as a Service), is one of the most groundbreaking technologies in software development as it promises to reshape how applications are built, deployed, and scaled. This shift from traditional server-based models towards a more streamlined strategy where developers can focus exclusively on writing code without getting overwhelmed by managing infrastructure.

However, to further understand the heart of the matter, let’s dive into the world of serverless computing and examine the significant impact, real-world examples, and future it has on software architecture.

Impact of FaaS in Software Development Practices

The adoption of serverless architecture is more than just a technological shift but also has the potential to change the organizational culture within the software developer (SDE) team. The new-age serverless architecture encourages a microservices approach where applications are broken down into smaller segments that can further independent functionalities as it is deployed to develop and scale independently, promoting flexibility, agility, and easier supervision compared to rigid architectures.

For CIOs, serverless architecture is one of the best options, as it is quite scalable and cost-efficient when compared to traditional server-based models. The traditional models were based on peak loads, often leading to underutilizing or wasting more resources during off-peak times. On the other hand, we have serverless computing that scales seamlessly by estimating the number of incoming requests, further ensuring optimal resource allocation and cost-effectiveness.

Another significant impact that serverless architecture can control is a reduction in operational complexity. By removing server management, focusing purely on serverless architecture allows developers to concentrate more on writing code and less on addressing infrastructure maintenance tasks. This enables SDE teams to innovate quicker and repeatedly iterate, driving business dexterity and responsiveness.

To sum up, serverless architecture represents a trend in the future of coding, offering scalability, dexterity, and cost-efficiency for modern software development. As enterprises are embracing serverless computing, SDE teams will play a vital role in leveraging capabilities to drive innovation and drive digital transformation.

To Know More, Read Full Article @ https://ai-techpark.com/the-rise-of-serverless-architecture-in-the-21st-century/

Related Articles -

Power of AI With Product Management Certifications

Spatial Computing Future of Tech

Trending Category - IOT Wearables & Devices

Azra AI, a healthtech leader harnessing artificial intelligence (AI) and workflow automation to accelerate the identification and treatment of cancer, today announced a strategic partnership with Registry Partners, a premier provider of healthcare data collection and registry management services. This collaboration aims to transform oncology data management and optimize Cancer Registry operations by integrating cutting-edge technology with expert human services.

The U.S. healthcare system is facing a critical shortage of Oncology Data Specialists, essential professionals responsible for abstracting, managing and interpreting complex cancer data. This shortage is creating significant challenges for oncology departments, leading to backlogs in data processing, delays in patient care, and potential risks to accreditation for many cancer programs. The collaboration between Azra AI and Registry Partners addresses this urgent issue by leveraging advanced AI technology and experienced contracting services to fill the gap, ensuring timely and accurate data management and ultimately enhancing the overall quality of cancer care.

Streamlining Oncology Data Workflows

This partnership combines Azra AI’s advanced data science models and oncology workflow automations with Registry Partners’ comprehensive registry management and consulting services. Azra AI’s technology can help to eliminate manual work for Oncology Data Specialists by capturing cancer data, aggregating that data in real-time, collecting the data in the right format, and pre-populating the required fields in the oncology data management software. Afterward, Registry Partners’ human experts can review the data inputs and ensure that the automated data is entered correctly for submission to state and federal registries.

Read Full News @ https://ai-techpark.com/azra-ai-announces-strategic-partnership-with-registry-partners/

Related Article - celebrating women's contribution to the IT industry

Health Catalyst, Inc. (“Health Catalyst,”  Nasdaq: HCAT), a leading provider of data and analytics technology and services to healthcare organizations, today announced it has successfully completed its acquisition of Lumeon Limited (“Lumeon”), a digital health company with operations in the U.S. and United Kingdom dedicated to helping provider organizations mend broken care coordination processes through automated care orchestration.

Leading providers in the U.S. and the United Kingdom use Lumeon’s Care Orchestration technology to lower costs, optimize clinician and staff time, enhance revenue, and improve quality and patient safety. Together with Health Catalyst, this acquisition aims to leverage Lumeon’s robust and market-leading platform and make it more intelligent through the application of advanced analytics, AI, and Health Catalyst Ignite™ data processing capabilities. Health Catalyst expects this combination will further strengthen and differentiate its core focus on clinical improvement and ambulatory operations, in addition to supporting its current and future Tech-Enabled Managed Services (TEMS) partnerships.

Health Catalyst also anticipates that Lumeon’s presence in the United Kingdom will strengthen its ability to expand and more effectively pursue new opportunities in the region and potentially in other international markets.

Read Full News @ https://ai-techpark.com/health-catalyst-finalizes-acquisition-of-lumeon/

Related Article - Top Five Popular Cybersecurity Certifications

IT companies and consulting firms are on a relentless quest to stay innovative in a rapidly evolving digital world. Industries worldwide are embracing the digital landscape, using AI to help transform their operations and adapt to new challenges.

Digital transformation integrates digital technologies into all operational areas, streamlining processes, enhancing customer interactions, fostering a forward-thinking work culture, and improving overall strategic planning. By embracing digital transformation, companies have the potential to save money while maximizing efficiency.

A Grim Reality: Economic Challenges and Layoffs

In response to economic challenges, including significant layoffs in the tech sector, companies must innovate and adapt swiftly. Digital transformation, especially through AI, provides a lifeline.

In 2023, 1,186 tech companies laid off 262,682 employees and this year alone, 168 tech companies have laid off 42,324 employees. Major consulting firms are also at risk. This is forcing them to stay ahead of the curve and innovate before it is too late.

Why Digital Transformation Matters

Digital transformation, especially when incorporating AI, can be a strategic solution for the challenges in the tech sphere. Imagine this: a mid-sized IT company experiencing fast-declining revenues and an increase in operational costs integrates AI into its workflow. AI acts as a catalyst to streamline processes and reduce manual errors while freeing up time for employees to focus on more strategic tasks. This results in increased productivity, efficiency and profitability. This is what companies need to stay ahead of competition.

By 2027, AI tools are expected to be used for digital transformation to cut process costs in half and reduce modernization expenses by 70%.

But despite its potential, digital transformation is much more difficult for companies to adopt than it seems. Only 35% of businesses have successfully adopted digital transformation efforts which highlights a pressing issue: many organizations are not fully prepared to embrace digital change and integration.

Digital transformation is the future of business. By embracing it now, companies can turn challenges into growth opportunities and thrive in the evolving digital landscape. IT companies looking to protect and evolve their operations can rely on this approach to ultimately tackle economic challenges and layoffs. By investing in skill building, promoting innovation and planning accordingly, organizations can turn the challenges they face to opportunities of growth. While adopting digital transformation strategies may be difficult now, it is the future of business. Companies who embrace it can thrive in the evolving digital landscape.

To Know More, Read Full Article @ https://ai-techpark.com/ai-drives-digital-evolution/

Related Articles -

Emergence of Smart Cities in 2024

Top Five Best Data Visualization Tools

Trending Category - Mobile Fitness/Health Apps/ Fitness wearables

In the context of the increasing prominence of decisions based on big data, companies are perpetually looking for the best approaches to effectively utilize their data resources truly. Introduce the idea of Unified Data Fabric (UDF), a new and exciting proposition that provides a unified view of data and the surrounding ecosystem. In this blog, we will uncover what UDF is, its advantages and thinking why it is set out to transform the way companies work with data.

What is Unified Data Fabric?

A Unified Data Fabric or Datalayer can be described as a highest form of data topology where different types of data are consolidated. It is an abstract view of the data accessible across all environment – on-premises, in the Cloud, on the Edge. Therefore, organizations are able to better leverage data and not micromanage the issues of integration and compatibility by abstracting over the underlying complexity through UDF.

The Need for UDF in Modern Enterprises

Today, elite business organizations are more involved in managing massive data from multiple fronts ranging from social media platforms to IoT, transactions, and others. Recent data management architectures have had difficulties in capturing and managing such data in terms of volume, variety, and velocity. Here’s where UDF steps in:

Seamless Integration: UDF complements the original set up by removing the barriers that create organizational and structural data separation.

Scalability: This makes it easy for UDF to expand with data as organizations carry out their activities without performance hitches.

Agility: It also enables an organization reposition itself rapidly when it comes to the data environment of an organization, hence it becomes easier to integrate new data sources or other analytical tools.

Unified Data Fabric for Seamless Data Access and Management

In the context of algorithmization of management and analytics-based decision making, more often than not, companies and enterprises are in a constant search for ways to maximize the value of their data. Introduce the idea of a Unified Data Fabric (UDF) – a relatively new idea that could help in achieving consistent and integrated data processing across various platforms. Let’s dive a bit deeper on understanding what is UDF, what it can bring to businesses, and why it will redefine data processing.

UDF is likely to be more significant as organizations proceed with the integration of advanced technology. The usefulness of being able to present and manipulate data as easily as possible will be a major force behind getting data back into dynamic uses whereby businesses can adapt to change and remain competitive in the market.

To Know More, Read Full Article @ https://ai-techpark.com/unified-data-fabric-for-data-access-and-management/

Related Articles -

AI in Drug Discovery and Material Science

Top Five Best AI Coding Assistant Tools

Trending Category - Mental Health Diagnostics/ Meditation Apps