Daily Archives: May 8, 2023

Newark, May 08, 2023 (GLOBE NEWSWIRE) -- The Brainy Insights estimates that the USD 36.46 Billion Hyperautomation market will reach USD 168 Billion by 2032. Rising demand of the Hyperautomated technological solutions for reducing business operational costs may propel the growth of the Hyperautomated Market, globally. This not only saves times, but also, energy, labour, and money of the organization. For instance: In May 2019, Thermax Limited adopted the hyperautomation systems for substituting manual chemical mixing process to hyperautomated solution. Thereby reducing the 40man days time to 15-man days time. Consequently, reducing the business operational costs.

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Report Coverage Details

North America to account for the largest market size during the forecast period accounting to 47% of the total market. Whereas Asia Pacific is expected to be the fastest growing region in the period forecasted.

North America emerged as the largest market for the global Hyperautomation market. Owing to increasing adoption of the technologies and entry of new market players in the region. Whereas Asia Pacific region is anticipated to exhibit highest growth rate over the period. Owing to rising investments on IT infrastructure from various countries such as, India, China, Japan. Furthermore, increasing demand for cloud-computing in these countries have also contributed towards the growth of the Hyperautomation market in this region.

Machine Learning (ML) has dominated the market with the most significant market revenue of USD 40 Billion in 2022.

Machine learning has dominated the market. Machine learning is a branch of Artificial Intelligence which primarily uses algorithms & models. Thereby uncovering critical insights and other focus areas. Hence, rising adoption of machine learning, globally with boost the overall growth of the Hyperautomation market.

IT & Telecom accounted for the largest share of the market, with a market revenue of USD 45.23 Billion in 2022.

IT & Telecom segment has dominated the Hyperautomation market. It is also expected to be the fastest growing segment across the globe. Owing to increased adoption of Integrating Robotic Process Automation (RPA). This ultimately helps in simplifying the operational tasks and providing long-term revenue generation opportunities in the period forecasted.

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Latest Development:

In April 2022: Juniper Network entered into a partnership agreement with PP Telecommunication Sdn Bhd (PPTEL). Main objective of this partnership agreement was to provide the company with the solutions that will help them strengthen and build its growth plan. With this the company will be able to fulfil the demand and needs of its end-users and will provide high network communication facilities in the period forecasted.

In February 2022: IBM and SAP entered into partnership agreement. The main objective of this agreement was to provide its consulting services in the areas of hyperautomation technology. Further, this agreement will also provide hybrid cloud solution and disseminate object oriented critical problems from SAP to various regulated and unregulated industries.

Market Dynamics

Drivers: Digitization of traditional manufacturing plants

Rising digitization and automation of the traditional manufacturing plants is one of the major factors that boosts the growth of the Hyperautomation Market. To solve complex data problems and minimizing unwanted efforts of the labour. Various organizations have adapted Hyperautomation to reduce their Operating Expenditure (OPEX) and enhance their productivity and efficiency levels.

Restraint: Scarcity of skilled workers

With constantly evolving technology, their prevails higher demand for the skilled and trained professionals, who manages and streamlines the workflow effectively and efficiently. Therefore, lack of skilled workers may hamper the growth of the Hyperautomation market in the period forecasted.

Opportunity: Increased demand of Hyperautomation to lower overall business operational costs

Rising demand of the Hyperauomated technological solutions for reducing business operational costs may propel the growth of the Hyperautomated Market, globally. This not only saves times, but also, energy, labour, and money of the organization. For instance: In May 2019, Thermax Limited adopted the hyperautomation systems for substituting manual chemical mixing process to hyperautomated solution. Thereby reducing the 40man days time to 15-man days time. Consequently, reducing the business operational costs.

Challenge: Higher installation and maintenance costs

Higher installation and maintenance costs is one of the major challenges that the organizations and other high-tech faces in the current market scenario. With ongoing advancements in Hyperautomation and increasing demand of the same. The companies now know of the complex procedural solutions that needs to be taken care of. Thus, only the Large corporate firms are able to incur the huge installation and maintenance costs of this solution. Which puts MSMEs at par towards taking advantage of the Hyperautomated technology, to a huge extent.

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Some of the major players operating in the Hyperautomation market are:

UiPath Wipro Ltd. Tata Consultancy Services Ltd. Mitsubishi Electric Corporation OneGlobe LLC SolveXia Appian Automation Anywhere Inc. Allerin Tech Pvt. Ltd. PagerDuty, Inc. Honeywell International Inc

Key Segments cover in the market:

By Type:

Biometrics Machine Learning Context-Aware Computing Natural Learning Generation Chatbots Robotic Process Automation

By End-User:

BFSI Retail IT & Telecom Education, Automotive Manufacturing Healthcare & Life Science

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About the report:

The global Hyperautomation market is analysed based on value (USD trillion). All the segments have been analysed on a worldwide, regional, and country basis. The study includes the analysis of more than 30 countries for each part. The report offers an in-depth analysis of driving factors, opportunities, restraints, and challenges for gaining critical insight into the market. The study includes porter's five forces model, attractiveness analysis, raw material analysis, supply, demand analysis, competitor position grid analysis, distribution, and marketing channels analysis.

About The Brainy Insights:

The Brainy Insights is a market research company, aimed at providing actionable insights through data analytics to companies to improve their business acumen. We have a robust forecasting and estimation model to meet the clients' objectives of high-quality output within a short span of time. We provide both customized (clients' specific) and syndicate reports. Our repository of syndicate reports is diverse across all the categories and sub-categories across domains. Our customized solutions are tailored to meet the clients' requirements whether they are looking to expand or planning to launch a new product in the global market.

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Avinash DHead of Business DevelopmentPhone: +1-315-215-1633Email: sales@thebrainyinsights.comWeb: http://www.thebrainyinsights.com

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Hyperautomation Market to Grow at CAGR of 16.5% through 2032 ... - GlobeNewswire

Now the students from Government schools in Uttar Pradesh will study and read about e-governance, artificial intelligence, cryptocurrency, drone technology, and information technology (IT) advancements.

According to the board secretary Dibyakant Shukla, Prayagraj-headquartered Uttar Pradesh Madhyamik Shiksha Board has updated the syllabus in accordance with the National Education Policy 2020 for classes 9 to 12 and uploaded it on its official website for the convenience of the students. The changes are specifically made in the curriculum for computer learning, which is taught in 28000 schools of UP board.

The syllabus is revised with the guidance and approval of subject experts. Its a significant change as it doesnt follow the current course prescribed by the National Council of Educational Research and Training (NCERT).The experts have replaced traditional computer programming languages such as C++ and HTML with Python and Java for class 11 and 12 students. This decision was made because HTML and C++ languages are not practised these days; instead, Core Java, Robotics and Drone Technology are introduced in the Class 12 syllabus.The class 11 students will study the Internet of Things (IoT), artificial intelligence, blockchain technology, augmented and virtual reality, 3-D printing and cloud computing.

Apart from HTML and C++, the board has also removed chapters on computer generations, history, and types of computers because of their irrelevance. The class 10 students will study ways to avoid hacking, phishing and cyber fraud. They will also be taught about artificial intelligence, drone technology and cyber security.Even students will study e-governance as a part of their curriculum.

Now class 9 students will be taught programming techniques, computer communication and networking, which class 10 students earlier studied.

While talking about the recent changes in the syllabus Biswanath Mishra, UP Board has made important changes in the syllabus of computer as a subject for students of classes 9 to 12. Students will now be taught modern topics like cryptocurrency, drone technology, artificial intelligence, hacking, fishing and cloud computing. This will prepare them as per the requirement of modern times. He teaches computers at Shiv Charan Das Kanhaiya Lal Inter College, Attarsuiya, Prayagraj.

The rest is here:

UP Board modernises computer learning in schools, introduces basics of AI, drone technology - Organiser

Key Takeaways

In this article, I aim to share some of the practical lessons we have learned while constructing our architecture at Monzo. We will delve into both our successful endeavors and our unfortunate mishaps.

We will discuss the intricacies involved in scaling our systems and developing appropriate tools, enabling engineers to concentrate on delivering the features that our customers crave.

Our objective at Monzo is to democratize access to financial services. With a customer base of 7 million, we understand the importance of streamlining our processes and we have several payment integrations to maintain.

Some of these integrations still rely on FTP file transfers, many with distinct standards, rules, and criteria.

We continuously iterate on these systems to ensure that we can roll out new features to our customers without exposing the underlying complexities and restricting our product offerings.

In September 2022, we became direct participants in the Bacs scheme, which facilitates direct debits and credits in the UK.

Monzo had been integrated with Bacs since 2017, but through a partner who handled the integration on our behalf.

Last year we built the integration directly over the SWIFT network, and we successfully rolled it out to our customers with no disruption.

This example of seamless integration will be relevant throughout this article.

A pivotal decision was to build all our infrastructure and services on top of AWS, which was unprecedented in the financial services industry at the time. While the Financial Conduct Authority was still issuing initial guidance on cloud computing and outsourcing, we were among the first companies to deploy on the cloud. We have a few data centers for payment scheme integration, but our core platform runs on the services we build on top of AWS with minimal computing for message interfacing.

With AWS, we had the necessary infrastructure to run a bank, but we also needed modern software. While pre-built solutions exist, most rely on processing everything on-premise. Monzo aimed to be a modern bank, unburdened by legacy technology, designed to run in the cloud.

The decision to use microservices was made early on. To build a reliable banking technology, the company needed a dependable system to store money. Initially, services were created to handle the banking ledger, signups, accounts, authentication, and authorization. These services are context-bound and manage their own data. The company used static code generation to marshal data between services, which makes it easier to establish a solid API and semantic contract between entities and how they behave.

Separating entities between different database instances is also easier with this approach. For example, the transaction model has a unique account entity but all the other information lives within the account service. The account service is called using a Remote Procedure Call (RPC) to get full account information.

During the early days of Monzo, before the advent of service meshes, RPC was used over RabbitMQ, which was responsible for load balancing and deliverability of messages, with a request queue and a reply queue.

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Figure 1: Rabbit MQ in Monzos early days

Today, Monzo uses HTTP requests: when a customer makes a payment with their card, multiple services get involved in real-time to decide whether the payment should be accepted or declined. These services come from different teams, such as the payments team, the financial crime domain team, and the ledger team.

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Figure 2: A customer paying for a product with a card

Monzo doesn't want to build separate account and ledger abstractions for each payment scheme, so many of the services and abstractions need to be agnostic and able to scale independently to handle different payment integrations.

We made the decision early on to use Cassandra as our main database for services, with each service operating under its own keyspace. This strict isolation between keyspaces meant that a service could not directly read data from another service.

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Figure 3: Cassandra at Monzo

Cassandra is an open-source NoSQL database that distributes data across multiple nodes based on partitioning and replication, allowing for dynamic growth and shrinking of the cluster. It uses timestamps and quorum-based reads to provide stronger consistency, making it an eventually consistent system with last-write wins semantics.

Monzo set a replication factor of 3 for the account keyspace and defined a query with a local quorum to reach out to the three nodes owning the data and return when the majority of nodes agreed on the data. This approach allowed for a more powerful and scalable database, with fewer issues and better consistency.

In order to distribute data evenly across nodes and prevent hot partitions, it's important to choose a good partitioning key for your data. However, finding the right partitioning key can be challenging as you need to balance fast access with avoiding duplication of data across different tables. Cassandra is well-suited for this task, as it allows for efficient and inexpensive data writing.

Iterating over the entire dataset in Cassandra can be expensive and transactions are also lacking. To work around these limitations, engineers must be trained to model data differently and adopt patterns like canonical and index tables: data is written in reverse order to these tables, first to the index tables, and then to the canonical table, ensuring that the writes are fully complete.

For example, when adding a point of interest to a hotel, the data would first be written to the pois_by_hotel table, then to the hotels_by_poi table, and finally to the hotels table as the canonical table.

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Figure 4: Hotel example, with the hard-to-read point of interests table

Although scalability is beneficial, it also brings complexity and requires learning how to write data reliably. To mitigate this, we provide abstractions and autogenerated code for our engineers. To ensure highly available services and data storage, we utilize Kubernetes since 2016. Although it was still in its early releases, we saw its potential as an open-source orchestrator for application development and operations. We had to become proficient in operating Kubernetes, as managed offerings and comprehensive documentation were unavailable at the time, but our expertise in Kubernetes has since paid off immensely.

In mid-2016, the decision was made to switch to HTTP and use Linkerd for service discovery and routing. This improved load balancing and resiliency properties, especially in the event of a slow or unreliable service instance.

However, there were some problems, such as the outage experienced in 2017 when an interaction between Kubernetes and etcd caused service discovery to fail, leaving no healthy endpoints. This is an example of teething problems that arise with emerging and maturing technology. There are many stories of similar issues on k8s.af, a valuable resource for teams running Kubernetes at scale. Rather than seeing these outages as reasons to avoid Kubernetes, they should be viewed as learning opportunities.

We initially made tech choices for a small team, but later scaled to 300 engineers, 2500 microservices, and hundreds of daily deployments. To manage that, we have separate services and data boundaries and our platform team provides infrastructure and best practices embedded in core abstractions, letting engineers focus on business logic.

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Figure 5: Shared Core Library Layer

We use uniform templates and shared libraries for data marshaling, HTTP servers, and metrics, providing logging, and tracing by default.

Monzo uses various open-source tools for their observability stacks such as Prometheus, Grafana, OpenTelemetry, and Elasticsearch. We heavily invest in collecting telemetry data from our services and infrastructure, with over 25 million metric samples and hundreds of thousands of spans being scraped at any one point. Every new service that comes online immediately generates thousands of metrics, which engineers can view on templated dashboards. These dashboards also feed into automated alerts, which are routed to the appropriate team.

For example, the company used telemetry data to optimize the performance of the new customer feature Get Paid Early. When the new option caused a spike in load, we had issues with service dependencies becoming part of the hot path and not being provisioned to handle the load. We couldn't statically encode this information because it continuously shifted, and autoscaling wasn't reliable. Instead, we used Prometheus and tracing data to dynamically analyze the services involved in the hot path and scale them appropriately. Thanks to the use of telemetry data, we reduced the human error rate and made the feature self-sufficient.

Our company aims to simplify the interaction of engineers with platform infrastructure by abstracting it away from them. We have two reasons for this: engineers should not need to have a deep understanding of Kubernetes and we want to offer a set of opinionated features that we actively support and have a strong grasp on.

Since Kubernetes has a vast range of functionalities, it can be implemented in various ways. Our goal is to provide a higher level of abstraction that can ease the workload for application engineering teams, and minimize our personnel cost in running the platform. Engineers are not required to work with Kubernetes YAML.

If an engineer needs to implement a change, we provide tools that will check the accuracy of their modifications, construct all relevant Docker images in a clean environment, generate all Kubernetes manifests, and deploy everything.

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Figure 6: How an engineer deploys a change

We are currently undertaking a major project to move our Kubernetes infrastructure from our self-hosted platform to Amazon EKS, and this transition has also been made seamless by our deployment pipeline.

If you're interested in learning more about our deployment approach, code generation, and our service catalog, I gave a talk at QCon London 2022 where I discussed the tools we have developed, as well as our philosophy towards the developer experience.

The team recognizes that distributed systems are prone to failure and that it is important to acknowledge and accept it. In the case of a write operation, issues may occur and there may be uncertainty as to whether the data has been successfully written.

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Figure 7: Handling failures on Cassandra

This can result in inconsistencies when reading the data from different nodes, which can be problematic for a banking service that requires consistency. To address this issue, the team has been using a separate service running continuously in the background that is responsible for detecting and resolving inconsistent data states. This service can either flag the issue for further investigation or even automate the correction process. Alternatively, validation checks can be run when there is a user-facing request, but we noticed that this can lead to delays.

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Figure 8: Kafka and the coherence service

Coherence services are beneficial for the communication between infrastructure and services: Monzo uses Kafka clusters and Sarama-based libraries to interact with Kafka. To ensure confidence in updates to these libraries and Sarama, coherence services are continuously run in both staging and production environments. These services utilize the libraries like any other microservice and can identify problems caused by accidental changes to the library or Kafka configuration before they affect production systems.

Investment in systems and tooling is necessary for engineers to develop and run systems efficiently: the concepts of uniformity and "paved road" ensure consistency and familiarity, preventing the development of unmaintainable services with different designs.

From day one, Monzo focuses on getting new engineers onto the "paved road" by providing a documented process for writing and deploying code and a support structure for asking questions. The onboarding process is defined to establish long-lasting behaviors, ideas, and concepts, as it is difficult to change bad habits later on. Monzo continuously invests in onboarding, even having a "legacy patterns" section to highlight patterns to avoid in newer services.

While automated code modification tools are used for smaller changes, larger changes may require significant human refactoring to conform to new patterns, which takes time to implement across services. To prevent unwanted patterns or behaviors, Monzo uses static analysis checks to identify issues before they are shipped. Before making these checks mandatory, we ensure that the existing codebase is cleaned up to avoid engineers being tripped up by failing checks that are not related to their modifications. This approach ensures a high-quality signal, rather than engineers ignoring the checks. The high friction to bypass these checks is intentional to ensure that the correct behavior is the path of least resistance.

In April 2018, TSB, a high-street bank in the UK, underwent a problematic migration project to move customers to a new banking platform. This resulted in customers being unable to access their money for an extended period, which led to TSB receiving a large million fine, nearly 33 million in compensation to customers, and reputational damage. The FCA report on the incident examines both the technological and organizational aspects of the problem, including overly ambitious planning schedules, inadequate testing, and the challenge of balancing development speed with quality. While it may be tempting to solely blame technology for issues, the report emphasizes the importance of examining organizational factors that may have contributed to the outage.

Reflecting on past incidents and projects is highly beneficial in improving operations: Monzo experienced an incident in July 2019, when a configuration error in Cassandra during a scale-up operation forced a stop to all writes and reads to the cluster. This event set off a chain reaction of improvements spanning multiple years to enhance the operational capacity of the database systems. Since then, Monzo has invested in observability, deepening the understanding of Cassandra and other production systems, and we are more confident in all operational matters through runbooks and production practices.

Earlier I mentioned the early technological decisions made by Monzo and the understanding that it wouldn't be an easy ride: over the last seven years, we have had to experiment, build, and troubleshoot through many challenges, and this process continues. If an organization is not willing or able to provide the necessary investment and support for complex systems, this must be taken into consideration when making architectural and technological choices: choosing the latest technology or buzzword without adequate investment is likely to lead to failure. Instead, it is better to choose simpler, more established technology that has a higher chance of success. While some may consider this approach to be boring, it is ultimately a safer and more reliable option.

Teams are always improving tools and raising the level of abstraction. By standardizing on a small set of technological choices and continuously improving these tools and abstractions, engineers can focus on the business problem rather than the underlying infrastructure. It is important to be conscious when systems deviate from the standardized road.

While there's a lot of focus on infrastructure in organizations, such as infrastructure as code, observability, automation, and Terraform, one theme often overlooked is the bridge between infrastructure and software engineers. Engineers don't need to be experts in everything and core patterns can be abstracted away behind a well-defined, tested, documented, and bespoke interface. This approach saves time, promotes uniformity, and embraces best practices for the organization.

Showing different examples of incidents, we highlighted the importance of introspection: while many may have a technical root cause, it's essential to dig deeper and identify any organizational issues that may have contributed. Unfortunately, most post-mortems tend to focus heavily on technical details, neglecting the organizational component.

It's essential to consider the impact of organizational behaviors and incentives on the success or failure of technical architecture. Systems don't exist in isolation and monitoring, and rewarding the operational stability, speed, security, and reliability of the software you build and operate is critical to success.

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Banking on Thousands of Microservices - InfoQ.com

Here is the comparison between the most in-demand fields Cyber Security vs. Data Science

Todays IT-intensive environment has taught us two important lessons: we need solutions to transform tidal surges of data into something that organizations can utilize to make educated decisions. We must safeguard that data and the networks on which it is stored.

As a result, we have the fields of data science and cyber security. So, which is the better job path? You wont get far if you approach the debate between cyber security vs. data science in terms of which field is more in demand. Both fields are in desperate need of a workforce.

Cyber security is the discipline of securing data, devices, and networks against unauthorized use or access while assuring and maintaining information availability, confidentiality, and integrity. A career in cybersecurity entails entering a thriving industry with more available positions than qualified applicants.

Data science combines domain knowledge, programming abilities, and mathematical and statistical knowledge to generate usable, relevant insights from massive amounts of unstructured data, often known as Big Data.

A career in data science includes carrying out data processing responsibilities, data scientists often use algorithms, processes, tools, scientific methods, techniques, and systems, and then apply the derived insights across multiple domains.

Data science and cyber security are inextricably linked since the latter demands the defences and protection that the former supplies. To obtain their conclusions and assure the security of the resultant processed information, data scientists require clean, uncompromised data. As a result, the area of data science looks to cyber security to assist protect the information in any form.

For someone interested in a career in one of the more intriguing and busy IT disciplines, cyber security and data science present fantastic chances. The career trajectories in both fields are comparable.

Experts in cyber security often begin their careers with a bachelors degree in computer science, information technology, cyber security, or a related profession. Aspirants in the field of cyber security should also be proficient in fundamental subjects like programming, cloud computing, and network and system administration.

The prospective cyber security specialist joins a corporation as an entry-level employee after graduating. After a few years of work experience, its time to apply for a senior position, which normally calls for a masters degree and certification in a variety of cybersecurity-related fields.

Cyber security experts choose career paths like security analyst, ethical hacker, chief information security officer, penetration tester, security architect, and IT security consultant.

Data scientists demand more formal education than cyber security specialists. A masters or even a bachelors degree isnt required for cybersecurity professionals, though having those resources helps. A bachelors degree in data science, computer science, or a similar branch of study is required for most data science professions. After a few years in an entry-level role, the ambitious data scientist should seek a masters degree in Data Science, reinforced by a few relevant certifications, and apply for a position as a senior data analyst.

Data science experts choose career paths like data engineer, marketing manager, data leader, product manager, and machine learning leader.

According to Glassdoor, the average yearly salary for cyber security specialists in the United States is US$94,794, whereas this figure is 110,597 in India.

In the field of data science, Indeed reports that US-based data scientists make an average salary of US$124,074 annually, while their Indian counterparts earn an average salary of US$830,319 annually.

Depending on demand, the hiring of certain individuals, and the location, these numbers frequently change.

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Cyber Security vs. Data Science Which Is the Right Career Path? - Analytics Insight

Amazons cloud service, Amazon Web Services (AWS), has announced plans to invest a further R30.4-billion in its cloud infrastructure in South Africa by 2029. It has already invested R15.6-billion in the country.

In a new economic impact study outlining Amazons investment in its AWS Africa (Cape Town) region since 2018, the group estimates its total investment of R46-billion between 2018 and 2029 will add at least R80-billion in gross domestic product to the South African economy. It will also help to support about 5,700 full-time equivalent (FTE) jobs at local vendors each year.

The FTE jobs are supported across the data centre supply chain, such as telecommunications, non-residential construction, electricity generation, facilities maintenance and data centre operations.

AWS provides cloud computing or on-demand delivery of IT resources over the internet which allows customers to access computing power, data storage and other services with pay-as-you-go pricing, as opposed to the traditional contract-based IT model.

Many of South Africas public sector institutions make use of AWS.

GovChat, SAs largest citizen-government engagement platform, provides a conversational interface that integrates voice and text into applications and provides a unified platform that citizens can use to connect with the government.

Wits University, SAs largest research university, has adopted a cloud-first approach to its IT strategy, using technology to enhance all its core processes.

Other AWS clients include Absa, Investec, Medscheme, MiX Telematics, Old Mutual Limited, Pick n Pay, Standard Bank, Pineapple and Travelstart.

Amazon is also steaming ahead with its retail marketplace in South Africa, with an expected launch towards the end of the year.

On 28 April 2023, Bloomberg reported that Amazon had warned that growth in its cloud computing business was continuing to cool.

AWS revenue rose 16% to $21.4-billion in the first quarter, as Amazon reported stronger-than-expected profits and sales in the period.

Last week, Amazon executives jolted investors by admitting that sales growth in the cloud computing unit had slowed. Some analysts have speculated that as companies seek to trim technology costs, AWS growth could sink to single digits, according to the report.

Amazons chief financial officer, Brian Olsavsky, told reporters that AWS was less profitable now than it was a year ago, partly owing to discounts offered in exchange for longer-term contracts. BM/DM

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DIGITAL PROMISE: Amazon pledges further R30bn SA investment ... - Daily Maverick