Big Data
Big Data is the term used to describe the technology and practice of working with data that is large in volume, fast, and diverse. Big Data has been a game-changer for many industries and applications, such as self-driving cars, lifelike robots, and autonomous delivery drones. However, Big Data also faces some challenges and opportunities in the current and future scenarios.
Here are some of the latest trends and findings about Big Data:
01
Smarter, more responsible, scalable AI
Artificial intelligence (AI) is the key to unlocking the value of Big Data, as it can learn from data and generate insights and actions. However, AI also needs to be smarter, more responsible, and scalable to cope with the changing and complex data landscape. This means that AI systems should be able to operate with less data (small data), adapt to new situations (adaptive machine learning), protect privacy and ethics (responsible AI), and scale up efficiently (scalable AI).
02
Small and wide data
​One of the effects of the COVID-19 pandemic is that historical data may not be relevant anymore, as the world has changed drastically. Therefore, Big Data needs to shift from relying on large amounts of historical data (big data) to using smaller and more varied data sources (small and wide data). This requires new techniques and tools to collect, integrate, analyse, and visualise data from different domains, formats, and
03
XOps
Ops is a term that encompasses various forms of operational excellence in data and analytics, such as DataOps, MLOps, ModelOps, DevOps, etc. The goal of XOps is to enable better decision making and turn data and analytics into an integral part of business processes. This involves automating, orchestrating, monitoring, and governing the data and analytics pipelines, as well as aligning them with business objectives and outcomes.
04
Distributed everything
The distributed nature of data and analytics is becoming more evident, as data is generated and consumed by more people and objects across different locations and devices. This requires the flexible relating of data and insights to empower an even wider audience of users and stakeholders. This also involves leveraging cloud computing, edge computing, blockchain, graph technology, and other distributed technologies to enable secure, reliable, and scalable data and analytics solutions.
Data and Analytics
​Data and Analytics (D&A) is the field that deals with the collection, processing, analysis, and visualisation of data to generate insights and actions that support decision making and business outcomes. D&A is constantly evolving and adapting to the changing needs and challenges of organisations and society.
Some of the recent developments and discoveries about D&A are as follows:
01
Value optimization
D&A leaders need to demonstrate the value they deliver for the organisation in business terms, by linking D&A initiatives to the organisation’s strategic priorities and measuring the impact and outcomes. Value optimization requires an integrated set of value-management competencies, such as value storytelling, value stream analysis, ranking and prioritising investments, and value realisation.
02
Managing AI risk
The growing use of AI in D&A has exposed organisations to new risks, such as ethical, legal, social, and technical risks. Managing AI risk is not only about complying with regulations, but also about building trust and confidence among stakeholders and users. This requires effective AI governance and responsible AI practices, such as ensuring transparency, accountability, fairness, privacy, and security of AI systems
03
Observability
Observability is the ability to understand and monitor the behaviour and performance of D&A systems, such as data pipelines, analytics models, and AI applications. Observability enables organisations to identify and resolve issues quickly, optimise resource utilisation, ensure data quality and reliability, and improve user satisfaction. Observability requires the use of tools and techniques that can collect, correlate, analyse, and visualise data from different sources and layers of the D&A system.
04
Data sharing
Data sharing is the practice of sharing data both internally (within or across departments or subsidiaries) and externally (with partners, customers, or third parties). Data sharing can create new sources of value by enabling collaboration, innovation, and insights. However, data sharing also poses some challenges, such as data security, privacy, ownership, governance, and interoperability. Data sharing requires the use of technologies and platforms that can facilitate data exchange, integration, discovery, access, and usage.
Machine Learning
Machine learning (ML) is the branch of AI that enables systems to learn from data and perform tasks that would otherwise require human intelligence. ML has been advancing rapidly in recent years, thanks to the availability of large-scale data, powerful computing resources, and novel algorithms.
To go into detail of some of the points about ML:
01
Efficient streaming language models
Language models are ML systems that can generate natural language texts based on a given context or prompt. However, most existing language models are limited by their fixed vocabulary size and memory capacity, which restricts their ability to handle long and diverse texts. A recent european paper proposes a new approach to train and deploy streaming language models that can process unlimited-length texts efficiently and accurately, by using attention sinks to reduce the computational complexity and memory footprint
02
Decoding speech perception from non-invasive brain recordings
Speech perception is the process of understanding spoken language by the human brain. It is a challenging task for ML systems, especially when the speech signal is noisy or distorted. The same paper demonstrates a novel method to decode speech perception from electroencephalography (EEG) signals, which are non-invasive brain recordings. The method uses contrastive learning to learn a shared representation between speech signals and EEG signals, and achieves state-of-the-art performance on several speech perception tasks.
03
Communicative agents for software development
Software development is a complex and collaborative activity that involves various tasks, such as designing, coding, testing, and documenting. This European paper introduces a new paradigm of using communicative agents to assist software developers in their work. It presents ChatDev, a virtual chat-powered software development company that simulates the waterfall model of software development, where multiple agents converse with each other to accomplish different stages of the project.
04
Instruction-tuned text-to-image diffusion models:
Text-to-image synthesis is the task of generating realistic images from natural language descriptions. It is a challenging task that requires both semantic understanding and visual creativity. The European Team proposes a new method to train text-to-image diffusion models that can generate high-quality images for various instructions, such as depth estimation, segmentation, style transfer, etc. The method uses a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and creates a multi-modal and multi-task training dataset.
AI vs. AGI
Artificial Intelligence (AI) is a broad term for machines that can perform specific tasks based on human cognition. AI can help us automate tasks, optimise processes, enhance customer experience, and generate new insights from big data. However, AI is not limited to specific domains or tasks and is capable of exhibiting general intelligence. This is where Artificial General Intelligence (AGI) comes in.
Some of the developments and discoveries about AI and AGI are as follows:
01
AGI is a subset of AI that aims to create machines with human-like intelligence across a broad spectrum of domains. AGI can also be called “Strong AI” or “Deep AI”, and it can adapt to complex contexts and solve problems that weak AI or narrow AI cannot. AGI machines can reason, plan, learn, understand natural language, and solve problems in a way that is similar to human beings.
02
AGI is still a theoretical pursuit in the field of AI research, and there is no consensus on how to achieve it or when it will be possible. Some researchers believe that AGI can be developed by scaling up existing AI techniques, such as deep learning, reinforcement learning, and federated learning. Others argue that AGI requires new paradigms and breakthroughs, such as symbolic reasoning, common sense, and consciousness.
03
The potential benefits and risks of AGI are also widely debated. Some experts envision that AGI could enhance human capabilities, solve global challenges, and create new sources of value. Others warn that AGI could pose existential threats, ethical dilemmas, and social disruptions. Therefore, many researchers advocate for the responsible and ethical development of AGI, with the involvement of various stakeholders and disciplines.
04
AGI vs. AI is not a binary distinction, but a continuum of intelligence levels. As AI becomes more advanced and general, we need to understand the differences and implications of AGI vs. AI, and prepare for the future of artificial intelligence.
Cloud Adoption & Security
Cloud adoption is the process of migrating applications, data, and infrastructure to cloud-based platforms and services. It can offer many benefits for organisations, such as scalability, flexibility, cost-efficiency, and innovation. However, cloud adoption also poses some security challenges and risks, such as data breaches, misconfigurations, compliance issues, and multi-cloud complexity.
To put it into numbers:
01
​Cloud adoption is still a top IT priority for organisations nowadays. In The State of Cloud Adoption report, over half of the study participants are stating that security requirements and integrations with on-premise technology are their biggest hurdles. According to the report by Security Compass, 65% of organisations had increased their cloud usage due to the COVID-19 pandemic, while 40% have accelerated their cloud migration plans.
02
​Cloud security remains a major concern for organisations, as 92% of respondents are at least moderately concerned about the security of public clouds. The 2021 Cloud Security Report by Fortinet and Cybersecurity Insiders reveals that the biggest cloud security threats are misconfiguration (67%), unauthorised access (61%), insecure interfaces (55%), and account hijacking (50%).
03
Cloud security posture management (CSPM) can help organisations detect and remediate cloud misconfigurations, which are one of the leading causes of breaches and outages. CSPM can also support organisations to ensure data protection and privacy, comply with regulations, and optimise cloud resources
04
Data encryption is another important trend to ensure customer data protection before it reaches the cloud. Organisations are increasingly adopting bring your own key (BYOK) encryption systems, which allow them to encrypt their data and retain control over the encryption keys. However, organisations need to be careful about choosing the right encryption solution, as some may upload the keys to the cloud security platform, which can compromise the data security.
05
​Multi-cloud environments are becoming more common, as 76% of organisations are using two or more cloud providers. However, managing multi-cloud security is challenging, as it requires consistent policies, visibility, and control across different cloud platforms and services. Organisations need to adopt a single cloud security platform that can provide a unified dashboard, configuration, and governance for multi-cloud environments.
DevOps & Application Security
DevOps & Application Security is the field that deals with the integration of security practices and tools into the DevOps lifecycle, from planning to deployment and monitoring. DevOps & Application Security aims to improve the quality, reliability, and security of applications, as well as the collaboration and efficiency of development and operations teams.
The latest development within DevOps & Application Security includes the following:
01
Security integration into DevOps practices is ongoing, with 51% of organisations actively involved in this process. Major roadblocks include a lack of security expertise, automation, and actionable feedback, as well as an overabundance of false positives.
02
​Cloud-native application protection platforms (CNAPPs) are emerging as a new category of solutions that provide comprehensive security for cloud-native applications, such as microservices, containers, and serverless functions. CNAPPs combine capabilities such as vulnerability scanning, runtime protection, compliance monitoring, and incident response.
03
State of DevOps in 2023: The Complete Report Roundup by Splunk reveals that security is one of the key drivers of DevOps success, as well as one of the main challenges. The report identifies four levels of security integration: reactive, proactive, automated, and self-service. It also provides best practices and recommendations for achieving each level.
04
DORA 2022 Accelerate State of DevOps Report by Google Cloud focuses on software supply chain security, which is the process of ensuring the integrity and security of software components and artefacts throughout the development and delivery stages. The report uses the SLSA framework and the NIST SSDF to explore both the technical and non-technical aspects that influence software security practices. The report also finds that high-trust, low-blame cultures are more likely to adopt emerging security practices than low trust, high-blame cultures.
05
Azure DevOps July 2023 updates introduce several new features and improvements for application security, such as dismissing dependency scanning alerts in Advanced Security, removing “Edit policies” permission when creating a new branch in Azure Repos, and adding support for Azure Key Vault references in Azure Pipelines.
06
Four Trends Shaping The Future Of Application Security by Forbes predicts that application security will be influenced by trends such as zero trust architecture, AI-powered threat detection, shift-left security testing, and cloud-native security solutions. The article also suggests that organisations need to adopt a holistic and proactive approach to application security that covers the entire DevOps lifecycle.
Java & Java Application Performance
Java is one of the most widely used programming languages in the world, but it also has some challenges and limitations when it comes to performance. However, Java has been constantly evolving and improving over the years, thanks to the efforts of the Java community and the innovations of the Java Virtual Machine (JVM).
What has been changing within Java lately:
01
Java 21 is the latest long-term support (LTS) release of Java, which was released on September 19, 2023. It includes several new features and enhancements that make it a compelling choice for developers, such as support for sealed classes and interfaces, improvements to the garbage collector, updates to the JDK tools, and support for the latest versions of operating systems.
02
Cloud-native development with Java is a paradigm shift in the way applications are built, deployed, and managed. It involves using cloud-based platforms and services, such as microservices, containers, and serverless functions, that can provide scalability, flexibility, cost-efficiency, and innovation. However, cloud-native development also poses some performance challenges, such as latency, reliability, security, and observability.
03
The State of the Java Ecosystem Report by New Relic provides context and insights into the current state of the Java ecosystem. It examines various categories, such as Java version adoption, containerization, compute settings in containers, garbage collection algorithms, and more. It also provides best practices and recommendations for achieving optimal performance with Java.
04
​The Java Developer Productivity Report by JRebel reveals how Java developers work and what tools they use. It covers topics such as trends in microservices adoption and usage, CI/CD build times and commit frequencies, popular frameworks, application servers, virtual machines, and more. It also analyses how developer productivity is affected by various factors, such as challenges and roadblocks.
05
​The Future of Java: Top 5 Java Trends article by Medium predicts that Java will be influenced by trends such as artificial intelligence and machine learning, cloud computing and microservices, low-code and no-code platforms, reactive programming and concurrency models, and modularization and interoperability.
06
Since Oracle introduced several significant changes in the licensing model of Java, several alternatives gained more popularity. One amongst them is the American company Azul, who offers besides a standard Java version also a high performance JVM. This product eliminates Garbage Collection Pauses and can handle up to 20 TB of heap. Forrester’s 2020 study reveals that deploying Azul Platform Prime (formerly Zing) and with this eliminating the stalls, jitter, and latency outliers caused by Java Garbage Collection, enterprises saved $2M over 3 years. This makes an ROI 224%.
