There are many factors that have started making businesses restless and eager to dive into the newest intelligent technologies for their Data Management practices. The business operators have sighed with relief knowing that they no longer have to engage dedicated talents for advanced model development or cloud infrastructure planning. The idea of "managed (hosted) Data Management" suddenly became the No. 1 priority of all businesses. From public utility sectors to finance and healthcare, smart solutions flooded all sectors. Closely following, the advanced technology platforms-as-a-service market globally is forecast to reach about $11 billion by 2023 and surpass "$88,500 million by the end of 2025."
Remember the time when the software development industry realized that a single person can take on multiple technologies glued tightly with each other and came up with the notion of a Full Stack Developer -- someone who does data modelling, writes backend code and also does front end work. Something similar has happened to the data industry with the birth of a Data Engineer almost half a decade ago. For many, the Full Stack Developer remains a mythical creature because of the never-ending list of technologies that cover frontend, backend and data. One of the reasons for that could be the fact that visualisation (business intelligence) has become a massive field in its own right. A Data Engineer is supposed to build systems to make data available, make it useable, move it from one place to another and so on.
MySQL, the open source relational database that came to Oracle through the Sun Microsystems acquisition, originated as a relatively simple relational database that was known for one task: transaction processing. In an announcement today, Oracle is unveiling an extended version of MySQL that takes it into data warehousing territory. It is releasing a new managed MySQL database service on Oracle Cloud Infrastructure (OCI) that will support both transaction and analytic processing workloads. That creates a key change for MySQL users. With few if any analytic options open, MySQL users typically resorted to ETL to move data to a separate database if they needed a data warehouse.
It was clear to the University of New South Wales (UNSW) that at the end of 2018, when it was developing its data strategy, it needed to improve the turnaround time it took to get information into the hands of decision makers. But to do that, the university had to set up a cloud-based data warehouse, which it opted to host in Microsoft Azure. The cloud-based warehouse now operates alongside the university's legacy data warehouse, which is currently hosted in Amazon Web Service's (AWS) EC2. "Our legacy data warehouse has been around for 10 to 15 years. But we started looking at what platforms can let us do everything that we do now, but also allows us to move seamlessly into new things like machine learning and AI," UNSW chief data and insights officer and senior lecture at the School of Computer Science and Engineering, Kate Carruthers said, speaking to ZDNet.
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In reviewing this year's batch of announcements for MongoDB's online user conference, there's a lot that fills the blanks opened last year as reported by Stephanie Condon. But the sleeper is unifying a platform that has expanded over the past few years with mobile and edge processing capabilities, not to mention a search engine, and the reality that Atlas, its cloud database-as-a-service (DBaaS) service, is now comprising the majority of new installs. Last year, MongoDB announced previews of Atlas Data Lake, the service of MongoDB's cloud service that lets you target data stored in Amazon S3 cloud storage; full text search, plans to integrate the then recently-acquired mobile Realm database platform with the Stretch serverless development environment; and autoscaling of MongoDB's Atlas cloud service. This year, all those previews announced last year are now going GA. Rounding it out is the announcement of the next release of MongoDB, version 4.4, that includes some modest enhancements with querying and sharding. The cloud is clearly MongoDB's future.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
In the current age of cloud computing, there is now a multitude of mature services available -- offering security, scalability, and reliability for many business computing needs. What was once a colossal undertaking to build a data center, install server racks, and design storage arrays has given way to an entire marketplace of services that are always just a click away. One leader in that marketplace is Amazon Web Services, which consists of 175 products and services in a vast catalog that provides cloud storage, compute power, app deployment, user account management, data warehousing, tools for managing and controlling Internet of Things devices, and just about anything you can think of that a business needs. AWS really grew in popularity and capability over the last decade. One reason is that AWS is so reliable and secure.
IT-OT integration is at the core of Industry 4.0, as many use cases require combining and reasoning with data from both OT and IT systems in utilizing data science models, advanced analytics, machine learning and AI to enable insights based cognitive and digital ways of working. As part of the digital transformation, few of the leading industrial products, oil and gas, downstream & chemicals manufacturing companies have already embarked on this journey by initiating data engineering and data integration efforts, developing or implementing data information management systems and by building massive plant and enterprise data lakes. These will facilitate implementation of advanced analytics and AI use case pilots / MVPs for integrated and collaborative operations and scaling up to production to realize the proposed business benefits. At the same time, many of the enterprise and industrial systems have been or are being transformed and migrated to public and private clouds / datacenters due to the cost and efficiency and strategic advantages. Given the above context, the leading companies need to start thinking in terms of "Cloud" and "Edge" computing capabilities with an objective to "centralize where you can in public & private clouds, distribute when you have to the edge".
With the rapid development of virtualization techniques, cloud data centers allow for cost effective, flexible, and customizable deployments of applications on virtualized infrastructure. Virtual machine (VM) placement aims to assign each virtual machine to a server in the cloud environment. VM Placement is of paramount importance to the design of cloud data centers. Typically, VM placement involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different constituents including cloud administrators and customers that might have disparate preferences while opting for a placement solution. Thus, it is often valuable to not only return an optimized solution to the VM placement problem but also a solution that reflects the given preferences of the constituents. In this paper, we provide a detailed review on the role of preferences in the recent literature on VM placement. We further discuss key challenges and identify possible research opportunities to better incorporate preferences within the context of VM placement.