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marketresearchfutureus · 5 years ago

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Global School Management System Market (SARS-CoV-2, Covid-19 Analysis) Research Report by Component, Deployment Mode, Application, End User and by Region - Forecast till 2025

A new market study, “Global School Management System Market Size, Status and Forecast 2019-2025” has been featured on Market Research Future.

Companies in the Global School Management System Market are facing issues in keeping their production facilities fully functional due to shortage of staff and resources amidst the COVID-19 (Coronavirus) outbreak. Get a hands-on over key drivers and threats to the Global School Management System Market to make your company future-ready post the pandemic. Avails out reports for exciting prices to learn new opportunities that companies can capitalize on during and after the Coronavirus crisis.

Related Link : https://www.marketresearchfuture.com/sample_request/8398

Market Synopsis

The school management system is a part of enterprise resource planning software mainly used for educational institutions to manage all types of information management systems. It used for various applications including student registration, documentation of grades and analytical marks of each student, payroll management, accounting management, and other applications. Moreover, the student management system covers the overall information of educational institution that includes administration management system, academic management system, and financial management.

With the advancement of technology, the educational institutions are rapidly transforming and adopting the cloud-based software offers with better accessibility features portals. Notably, most of the advancements have been taken place in advanced economies such as the US, Canada, and Western and Central European countries. However, due to lack of human resources, financial constraints, limited options for customization, and lack of expertise are some of the challenges that hinder the growth of the market during the forecast period.

Source: Company Websites, Annual Reports, Primary Interviews, Industry Experts, Whitepapers, Secondary Research, Press Release, and MRFR Analysis

The Global School Management System Market was valued at USD 8.5 billion in 2018; it is expected to reach USD 25.7 Billion by 2025 with a CAGR of 17.7% during the forecast period, 2019–2025.

Key Developments

In September 2019, Ellucian, the leading provider of software and services to enhance higher education, partnered with Gannon University (Gannon), a Catholic university in Erie, Pennsylvania, US to provide Ellucian CRM Advance. The platform offers flexible and native integration features with the university ecosystem helps it to build alumni relationships and support its fundraising activities.

In February 2019, Canvas, a subsidiary of Instructure Inc., partnered with University College Cork to offer virtual learning environment (VLE) for the colleges that help them to enhance its connected infrastructure. Additionally, through this offering the university would emphasize and support student-centered teaching and learning experience with a transformed, responsive and research-led curriculum at its core.

Segmentation

The global school management system market has been segmented based on component, application, deployment mode, end user, and region.

By component, the global school management system market has been bifurcated into solutions and services. Furthermore, by service, the market has been sub-divided into professional services and managed services. The professional services segment has been categorized into consulting and implementation services and training and development services.

By deployment mode, the market has been segmented into cloud and on-premise.

Furthermore, the application has been divided into administration management systems, academic management systems, learning management systems, and financial management systems. Breaking down further, the administration management system has been further categorized into institute management, student management, staff management, library management, inventory, and transportation management system. Lastly, financial management has been further categorized into fee management, accounting management, and payroll management.

Based on end user, the global school management system market has been categorized into schools, universities, community colleges, and others.

Regional Analysis

The global school management system market has been categorized on the basis of geography into North America, Europe, Asia-Pacific, the Middle East and Africa, and South America.

North America accounted for the largest market share in 2018 and expected to dominate the global school management system market during the forecast period. The market growth can be attributed to the increasing adoption of cloud-based learning management systems and the presence of key vendors in the region that include Oracle Corporation, Ellucian Company L.P,and Jenzabar, Inc.

The Asia-Pacific is anticipated to be the fastest-growing geography in the school management system market during the assessment period. The market growth can be attributed to a surge in number of vendors offering ERP based solutions for universities, public and private schools, and management school.

Key Players

MRFR identifies some of the key players of the global school management system market include Blackboard, Inc (US), Skolaro (India), Oracle Corporation (US), Ellucian Company L.P (US), Foradian Technologies (India), Hobsons (US), Jenzabar, Inc (US),PowerSchool (US), Capita SIMS (UK), Classter (Greece), Instructure, Inc (US), McGraw-Hill Education, (US), Cornerstone (US), Schoology (US), and Knewton, Inc. (US).

Competitive Analysis

The vendors operating in the market primarily follow organic and inorganic growth approaches to offer enhanced and seamless cloud-based school management system solutions. The vendors also emphasized strategic collaboration and partnership with the technology partners such as Microsoft Corporation and IBM Corporation to offer enhanced learning management solution. For instance, in 2016, Blackboard, Inc acquired Fronteer, a UK-based education technology company to facilitate the institutions and instructors to develop course content more accessible for learners. Overall, the company has adopted inorganic growth approach through partnership and collaboration with companies such as Moodle, VitalSource, B Human, and IBM to enhance its product offerings.

More Information : https://www.marketresearchfuture.com/reports/school-management-system-market-8398

About Market Research Future: At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Reports (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research and Consulting Services. Contact: Market Research Future +1 646 845 9312 Email: [email protected]

#Global School Management System Market #Global School Management System Market Report #Global School Management System Market Research #covid-19 #component #deployment mode #application #region #forecast

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enclaveresearch · 6 years ago

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New Post has been published on https://enclaveresearch.com/comments-on-the-chainlink-white-paper-part-2/

Comments on The Chainlink White Paper, Part 2

In principle, any contract which can be negotiated through a trusted third party (such as an auction or exchange) can be negotiated directly. So, in some abstract sense, the only remaining “hard” problems in smart contract negotiations are (a) problems considered hard even with a trusted intermediary (for the standard economic reasons), and (b) the task of algorithmically specifying the negotiating rules and output contract terms (This includes cases where an intermediary adds knowledge unavailable to the participants, such as a lawyer giving advice on how to draft a contract). In practice, many problems which can be solved in principle with multiparty computation will re-arise when we implement protocols in an efficient, practical manner. The God Protocols give us a target to shoot for.

The God Protocols

Back in 1997, Nick Szabo published his vision of the God protocol. An unbiased, mathematically trustworthy mediator that perfectly acts in the interest of all parties.

Since then, blockchain has come to provide an immutable, trust-less record where inputs can assemble and outputs be retrieved from. His fully-fledged vision goes much further than that—to a world where countless middlemen in our economy are freed from their dull and monotonous responsibilities. As predicted 22 years later, topics considered in The God Protocol have re-arisen with the advent of blockchain, smart-contract, and secure enclave technology. However, protocol sluggishness and lack of scalability in Szabo’s time remain problems with today’s blockchain:

The first is that this virtual computer is very slow: in some cases, one arithmetic calculation per network message.

The God Protocols

Chainlink has come to address this issue, allowing for computation off-chain while still retaining much of the integrity blockchain provides. Trusted execution environments on the network allow for secure, verifiable processing with much better scaling.

Chainlink’s Town Crier solution also addresses the problem of confidentiality in a smart contract network, finally bringing to fruition a key part of Szabo’s vision:

If mutually confidential auditing ever becomes practical, we will be able to gain high confidence in the factuality of counterparties’ claims and reports without revealing identifying and accompanying information from the transactions underlying those reports.

Knowing that mutually confidential auditing can be accomplished in principle will hopefully lead us to practical solutions to these important problems.

The God Protocols

Intel SGX in conjunction with Town Crier does allow auditors to assess if the user’s application meets specifications and standards. It can then can generate final binaries, and sign them for audit. Enclaves may also process data from sources confidentially, managing sensitive information like user credentials. Many smart contract use-cases are null and void without this protection of privacy.

Szabo’s Hurdles

With the base layer(protocol-level) requirements solved, some higher level software hurdles remain, but are perhaps within reach.

Szabo goes on to illustrate the first remaining problem: “(a) those considered hard even with a trusted intermediary”. He may be referring to arbitration, indemnification, standards of proof, force majeure, and other legal sections that would be difficult to codify in a smart contract. Or, where “human trusted third parties provide insight or knowledge that cannot be provided by a computer.” In contrast with operational clauses, these situations are classified as “Non-operational” according to the International Swaps and Derivatives Association:

Operational clauses generally embed some form of conditional logic – ie, that upon the occurrence of a specified event, or at a specified time, a deterministic action is required.

Non-operational clauses do not embed such conditional logic but that, in some respect, relate to the wider legal relationship between the parties.

ISDA

One example of a non-operational clause is when a party to a contract is required to act in “good faith” or use “reasonable care”. These terms have legal meaning, but are clearly not boolean. What these terms mean is subjective and hard to codify in a way that makes sense. People tend to have varying standards along a spectrum of rigidity and leniency. Many interpretations of law are highly contextual, factoring in the unique circumstances of the case.

Take the example of a standard representation from a party that it is duly organized and validly existing under the laws of the jurisdiction of its organization or incorporation. This is not a statement of conditional logic, and so would not be susceptible to pure Boolean logic. It is a representation of a legal state. But if there were a sufficiently developed ontology for legal contracts, it would be possible to conceive of a world where a computer could understand what is meant by the terms ‘party’, ‘duly organised’, ‘validly existing’, ‘jurisdiction’ and ‘organisation and incorporation’, and could check automatically with relevant company registries whether this representation is correct at the time it is given.

ISDA

A.I. may integrate and play a large role in smart contracts of the future.

Since machine learning has made great progress writing convincing essays and poetry, it’s not unreasonable to think it may be applied to law in the future. Taking the ISDA’s conception a step further, there are millions of case-law records and judgements available for machine learning to develop ontology and reasoning from. Where cost savings outweigh the risks, future A.I. judges may only need to prove it can track alongside human ones at a reasonable rate greater than chance.

Robo-arbitration may be a method for non-operational clauses to be handled in a smart contract. This would be an ideal use-case for Town Crier’s trusted execution environments since the high compute cost would necessitate that it be run off-chain. It would also ensure the integrity of the robo-arbitrator. Innovation in this field has already begun in earnest. For example, Blue J Legal provides such software as Tax Foresight, which uses machine learning to predict how a court would rule in clients various tax scenarios. Such integration on a smartcontract network could prove a powerful tool in reducing the cost of litigation.

youtube

The second piece to the puzzle mentioned by Szabo, “(b) specifying the negotiating rules” could be solved at least in part, by a growing library of legal templates such as those offered by Openlaw and the Accord Project. These allow for reliable contracts to be deployed easily and with recommended and customizable parameters.

The Chainlink whitepaper also mentions an optional “escape hatch” to be used by authorized contract administrators in the event of an unforeseen bug or vulnerability. Again, these can be set up in a number of ways at the discretion of the smart contract users.

An oracle is, a translator for information provided by an outside platform. Oracles provide the necessary data to trigger smart contracts. The other factors which “specify negotiating rules” are how and what oracle feeds are used in determining smart contract outcome(s). The flexibility and security of which has been solved comprehensively by Town Crier:

TC can also perform trusted off-chain aggregation of data from multiple sources, as well as trusted computation over data from multiple sources (e.g., averaging) and interactive querying of data sources (e.g., searching the database of one source in response to the answer of another).

Chainlink White Paper

Today there are contracts that would theoretically be desirable, but are too costly and slow to be implemented in a traditional manner. For example, insurance contracts that need a minimum size to cover administrative and enrollment costs. Still there are other cases where contracts are violated but the cost of litigating them are too high to pursue. These are ideal niches where standardized smart contracts may flourish.

Another niche may be found in developing countries. Just as the anti-inflationary properties of crypto helps citizens of unstable regimes, a substitute legal system might also be used where property rights and rule of law are weak. Sergey Nazarov echoed this in a 2014 interview:

In the current system many businesses can raise money and make promises they won’t keep. Because they realize the banking or legal infrastructure is flawed. In the future, people will simply make a smart contract.

Sergey Nazarov

Modern smart-contract discussion conjures up thoughts of an almost magical process where automation takes over. Panels ask, “Are we getting rid of lawyers?”. The answer is categorically no. It just means fewer people working in back-offices pushing paper; which will likely be balanced with new jobs in implementation and maintenance of smart contracts themselves. Lawyers will be freed up to draft contracts and litigate the most complex cases and where human discernment is necessary. The focus would shift towards choosing and calibrating a growing library of legal templates. These changes will further be accelerated by the official recording of registration, identity, and property rights on blockchain.

Conclusion

The God Protocol is here, and with it, a new era of the internet has begun. A promising shake-up of legal systems around the world, cutting down corruption and unlocking billions in productivity. Adoption could take decades and will no doubt have its skeptics, but it seems a system closely resembling Szabo’s vision is now inevitable.

“We heard the same concern over and over again: a panic over giving up control.” is how @Benioff described initial reactions to SaaS replacing on-premise, until the value of SaaS won out. It'll be the same story with Smart Contracts, as their ability to deliver value improves

— Sergey Nazarov (@SergeyNazarov) February 20, 2018

#chainlink #future of law #robo arbitration #secure enclaves #smart contracts #town crier #Tech

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increaseprofitonline2017-blog · 6 years ago

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#212 Adam Perlow & Asher Manning: Zen Protocol - A Decentralized Financial System [ad_1]

We were joined by Founder Adam Perlow and Developer Asher Manning of Zen, a public blockchain project focused on building a decentralized financial system. The core premise of Zen is that none of the public blockchain networks are focused on financial asset. Zen is aiming to fill that gap through a Bitcoin-like UTXO architecture that supports multiple asset and smart contracts to enforce complex ownership rules.

We talked through their original design choices, their use of formal verification, connection to Bitcoin and vision for a fully decentralized financial system.

Topics discussed in this episode:

Why existing public blockchains are ill-suited for financial instruments

Why Zen chose to use a Bitcoin-like UTXO architecture

How Zen uses formal verification to allow smart contracts without a virtual machine or needing gas

How the Active Contract Set reduces the burden on the miners

Walking through creating, trading and settling a call option on Zen

How Zen allows payments to be settled in Bitcoin

Getting data from the outside world with oracles

Zen's use of Proof-of-Work regulated by on-chain governance

Links mentioned in this episode:

Zen Protocol - A Financial Engine

Zen Protocol - Whitepaper

Zen Protocol - Deck

Google Campus Presentation - YouTube

Zen - Alpha Version

Zen Protocol Founders Film - YouTube

Oracles and Zen ?'" Zen Protocol

Zen?'?s contract lifecycle ?'" Zen Protocol

Sponsors:

Shapeshift: Buy and sell alt coins instantly and securely without a centralized exchange - http://epicenter.tv/shapeshift

Support the show, consider donating:

BTC: 1CD83r9EzFinDNWwmRW4ssgCbhsM5bxXwg (https://epicenter.tv/tipbtc)

ETH: 0x8cdb49ca5103Ce06717C4daBBFD4857183f50935 (https://epicenter.tv/tipeth)

This episode is also available on :

Epicenter.tv

YouTube

Souncloud

Watch or listen, Epicenter is available wherever you get your podcasts.

Epicenter is hosted by Brian Fabian Crain, S?ƒbastien Couture & Meher Roy.

[ad_2] Source link Source URL: https://www.increaseprofitonline.com/2017/12/07/212-adam-perlow-asher-manning-zen-protocol-a-decentralized-financial-system/

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ketan-wagh121-blog · 5 years ago

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Risk Analytics Market: 2019 Latest Demand, Share, Techniques, Applications Analysis and 2025 Global Industry Growth Forecast Report

Global Risk Analytics Industry

New Study On “2019-2025 Risk Analytics Market Global Key Player, Demand, Growth, Opportunities and Analysis Forecast” Added to Wise Guy Reports Database

Global Risk Analytics Market-Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2018 - 2025 Research Methodology Sheer Analytics and Insights' all degree research methodology represents the analytical rigor of our research process. It offers a complete view of industry trends, opportunities and challenges by integrating all the major factors. We identify the major drivers and restraints for every region (North America, Latin America, Europe, Asia Pacific, & Middle East) of any particular market with a weightage value of how it is impacting the market. For each driver and restraint, we provide weightage in short term, medium term, and long term. Here the driver acts as a pull factor and restraint as a push factor.

Request Free Sample Report @ https://www.wiseguyreports.com/sample-request/3750815-global-risk-analytics-market-by-risk-type-market

Primary Research Key players in the market are identified through review of secondary sources such as industry whitepapers, annual reports, published reports by credible agencies, financial reports and published interviews of Key Opinion Leaders (KOLs) from leading companies. During the primary interviews, KOLs also suggested some producers that are included under the initial scope of the study. We further refined company profile section by adding suggested producers by KOLs. KOLs include Chief Executive Officer (CEO), general managers, vice presidents, sales directors, market executives, R&D directors, product managers, procurement managers, export managers etc. During the research process, all the major stakeholders across the value chain are contacted for conducting primary interviews.

Report Description The report covers the analysis and forecast of the Risk Analytics market on global as well as regional level. The study provides historic data for 2017 along with the forecast for the period between 2018 and 2025 based on revenue (US$ Mn). A comprehensive analysis of the market dynamics that is inclusive of market drivers, restraints, and opportunities is part of the report. Additionally, the report includes potential opportunities in the Risk Analytics market at the global and regional levels. Market dynamics are the factors which impact the market growth, so their analysis helps understand the ongoing trends of the global market. Therefore, the report provides the forecast of the global market for the period from 2017 to 2025, along with offering an inclusive study of the Risk Analytics market. Market Push & Pull Factors

The study provides a detailed view of the Risk Analytics market, by segmenting it based on risk type, component, end-use industry and regional demand. Ultra-light weight and superior thermal resistivity features propel the demand of genetyping assay market. Additionally, the multi-functional development of Risk Analytics fuels the demand of this market. The competitive profiling of the key players in the global market across five broad geographic regions is included in the study. These include different business strategies adopted by the leading players and their recent developments. The report provides the size of the Risk Analytics market in 2017 and the forecast for the next eight years up to 2025. The size of the global Risk Analytics market is provided in terms of revenue. Market revenue is defined in US$ Mn. The market dynamics prevalent in North America, Europe, Asia Pacific, Middle East and Africa and Latin America has been taken into account in estimating the growth of the global market. Market estimates for this study have been based on revenue being derived through regional pricing trends. The Risk Analytics market has been analyzed based on expected demand. Bottom-up approach is done to estimate the global revenue of the Risk Analytics market, split into regions. Based on risk type, component and end-use industry the individual revenues from all the regions are summed up to achieve the total market revenue (TMR) for Risk Analytics. Companies were considered for the market share analysis, based on their innovation and application and revenue generation. In the absence of specific data related to the sales of Risk Analytics several privately held companies, calculated assumptions have been made in view of the company's penetration and regional presence. The report covers a detailed competitive outlook that includes the market share and company profiles of key players operating in the global market. Key players profiled in the report include Accenture PLC, ACL Services, Genpact, IBM Corporation, Misys, Numerix LLC, Oracle Corporation, Provenir, Riskdata S. A, SAP SE, SAS Institute, Inc and Tata Consultancy Services Ltd. Report Scope

The global risk analytics market has been segmented into: Global Risk Analytics Market: By Risk Type • Market Risk • Credit Risk • Operational Risk • Portfolio Risk • Financial Risk • Others Global Risk Analytics Market: By Component • Software & Tools • Services • On-Premise • On Cloud Global Risk Analytics Market: By End-Use Industry • BFSI • Government • IT & Telecom • Transportation • Retail • Others Global Risk Analytics Market: by Geography • North America o U.S. o Canada o Mexico • Europe o U.K. o France o Germany o Italy o Spain o Rest of Europe • Asia Pacific o India o China o Japan o Rest of Asia Pacific • Middle East and Africa o South Africa o Rest of Middle East and Africa • Latin America o Brazil o Rest of Latin America Questions answered in the Risk Analytics market research report: 1. What are risk analytics? 2. What is the global risk analytics market size? 3. What are the market driving factors behind the global risk analytics market? 4. What are the market trends and forecast for the global risk analytics market? 5. What are the global trends and forecasts based on market research and analysis of global risk analytics market segmentation by risk type? 6. What are the global trends and forecasts based on market research and analysis of global risk analytics market segmentation by component?

For Detailed Reading Please visit WiseGuy Reports @ https://www.wiseguyreports.com/reports/3750815-global-risk-analytics-market-by-risk-type-market

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atomicjellyfishdelusion · 5 years ago

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Supply Chain Analytics Market to Witness over X.X% Growth 'in Revenue During the COVID-19 Pandemic

Supply Chain Analytics Market Overview:

The demand for analytics continues and the importance of visualizing and understanding data is more vital for decision-making than ever before. Analytics is being applied across the supply chain—from sourcing of raw materials at optimized cost, forecasting new products demand, smart manufacturing to distributing finished products through optimal vehicle routes and fleets, and providing excellent customer service through predictive models. Supply chain optimization is the top most priority for an organization that is trying to reduce operational cost, sustain business growth and increase customer satisfaction.

Supply Chain Analytics provide comprehensive visibility in every aspect of the company’s supply chain, including supplier performance, raw materials procurement, inventory management, finished goods, and delivery effectiveness. Financial and service experts can gain visibility in their inventories and transportation, logistics and warehousing costs with the help of supply chain analytics to improve the customer satisfaction and profits. Supply Chain Analytics helps in improving the overall operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational and tactical levels of an organization.

Request For Report Sample@ https://www.trendsmarketresearch.com/report/sample/9899

Market Analysis:

According to Infoholic Research, the Supply Chain Analytics Market is expected to reach $9.43 billion by 2024, growing at a CAGR of around 18.62% during the forecast period. The market is expected to witness a surge in the next few years, the growing need to improve the end-to-end visibility on supply chain operations such as orders, shipments, and inventory, the real-time analysis of supply chain operations and the increase in forecast accuracy has further pushed the growth of supply chain analytics market during the forecast period.

Market Segmentation Analysis:

The report provides a wide-ranging evaluation of the market. It provides in-depth qualitative insights, historical data, and supportable projections and assumptions about the market size. The projections featured in the report have been derived using proven research methodologies and assumptions based on the vendor’s portfolio, blogs, whitepapers, and vendor presentations. Thus, the research report serves every side of the market and is segmented based on Regional markets, Solution, Service, Deployment, Enterprise Size and End User.

Competitive Analysis

The report covers and analyzes the Supply Chain Analytics market. As customers are in need of better and comprehensive solutions, the market will witness an increase in the number of strategic partnerships for better product development. With a large pool of startups offering customized solutions, the market is still very fragmented and will consolidate as mergers and acquisitions happen during the forecast period.

The report contains an in-depth analysis of the vendor profiles, which include financial health, business units, key business priorities, SWOT, strategy; The prominent vendors covered in the report include IBM Corporation, SAP SE, SAS Institute Inc, Capgemini, Accenture, Oracle Corporation, Kinaxis, Bristlecone and others. The vendors have been identified based on the portfolio, geographical presence, marketing & distribution channels, revenue generation, and significant investments in R&D.

Companies including IBM Corporation, SAP SE, Capgemini, Accenture are the key players in the Supply Chain Analytics market. IBM is working with clients and developers across multiple industries to use blockchain to transform how business is done in areas such as banking and financial services and supply chain. SAP recently announced new products, services, and partnerships centered on advanced technologies such as artificial intelligence (AI) and analytics. BPCL is investing in digital technologies across the manufacturing and distribution supply chain and Capgemini was chosen as a key partner to assist BPCL in setting up a Shared Services Center (SSC).

The report also includes the complete insight of the industry, and aims to provide an opportunity for the emerging and established players to understand the market trends, current scenario, initiatives taken by the government, and the latest technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and to take informed decisions.

Get Complete TOC with Tables and Figures@ https://www.trendsmarketresearch.com/report/discount/9899

Regional Analysis

The Americas hold the largest chunk of market share in 2017 and is expected to dominate the Supply Chain Analytics market during the forecast period. With the increasing presence supply chain analytics solutions providers in the region and the need for increasing operational efficiency and reducing the costs of maintaining supply chains, modernizing logistics, transportation and warehouse operations, the market will experience a steep rise in this region.

Benefits

The report provides an in-depth analysis of the Supply Chain Analytics market. Supply chain analytics solutions plays an important role in an organization as it helps in achieving the business growth, enhances profitability and increases market share by receiving valuable insights from the raw data that further help end-users to make data driven decisions, and provides a combined view of the supply chain. Such solutions provide organizations with a 360-degree visibility into their supply chain and helps in reducing inventory and warehousing cost, improves sustainability and profitability in the long run. The report discusses the Solution, Application, Service, End user, and regions related to this market. Further, the report provides details about the major challenges impacting the market growth.

Supply Chain Analytics Market by Solution

Sales & operations planning analytics

supply chain planning & procurement

Demand planning analytics

Transportation & Logistics analytics

Inventory planning & optimization analytics

visualization & reporting

Supply Chain Analytics Market by Services

Professional Service

Managed service

<<< Get COVID-19 Report Analysis >>> https://www.trendsmarketresearch.com/report/covid-19-analysis/9899

Supply Chain Analytics Market by Deployment

On- premise

On- cloud

Supply Chain Analytics Market Enterprise size

Small & Medium Enterprise

Large Enterprise

Supply Chain Analytics Market by Enduser

Retail & consumer packaged goods

Manufacturing

Healthcare & Life science

Transportation

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danielmarkharrison · 8 years ago

Text

#212 Adam Perlow & Asher Manning: Zen Protocol - A Decentralized Financial System

We talked through their original design choices, their use of formal verification, connection to Bitcoin and vision for a fully decentralized financial system.

Topics discussed in this episode:

Why existing public blockchains are ill-suited for financial instruments

Why Zen chose to use a Bitcoin-like UTXO architecture

How Zen uses formal verification to allow smart contracts without a virtual machine or needing gas

How the Active Contract Set reduces the burden on the miners

Walking through creating, trading and settling a call option on Zen

How Zen allows payments to be settled in Bitcoin

Getting data from the outside world with oracles

Zen's use of Proof-of-Work regulated by on-chain governance

Links mentioned in this episode:

Zen Protocol - A Financial Engine

Zen Protocol - Whitepaper

Zen Protocol - Deck

Google Campus Presentation - YouTube

Zen - Alpha Version

Zen Protocol Founders Film - YouTube

Oracles and Zen ?'" Zen Protocol

Zen?'?s contract lifecycle ?'" Zen Protocol

Sponsors:

Shapeshift: Buy and sell alt coins instantly and securely without a centralized exchange - http://ift.tt/2thJTEU

Support the show, consider donating:

BTC: 1CD83r9EzFinDNWwmRW4ssgCbhsM5bxXwg (http://ift.tt/2rBuL3j)

ETH: 0x8cdb49ca5103Ce06717C4daBBFD4857183f50935 (http://ift.tt/2r5NyAq)

This episode is also available on :

Epicenter.tv

YouTube

Souncloud

Watch or listen, Epicenter is available wherever you get your podcasts.

Epicenter is hosted by Brian Fabian Crain, S?ƒbastien Couture & Meher Roy.

from The Let's Talk Bitcoin Network http://ift.tt/2AdCR85 via IFTTT

#The Let's Talk Bitcoin Network

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marketresearchfutureus · 5 years ago

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Global School Management System Market Research Report: by Component, Application, End User and by Region - Forecast till 2025

A new market study, titled “Global School Management System Market Size, Status and Forecast 2020-2023” has been featured on Market Research Future.

Global School Management System Market Research Report offer detailed insights on the impact of COVID-19 at an industry level, a regional level, and subsequent supply chain operations. This customized report will also help clients keep up with new product launches in direct & indirect COVID-19 related markets, upcoming vaccines and pipeline analysis, and significant developments in vendor operations and government regulations.

Related Link : https://www.marketresearchfuture.com/sample_request/8398

Source: Company Websites, Annual Reports, Primary Interviews, Industry Experts, Whitepapers, Secondary Research, Press Release, and MRFR Analysis

The Global School Management System Market was valued at USD 8.5 billion in 2018; it is expected to reach USD 25.7 Billion by 2025 with a CAGR of 17.7% during the forecast period, 2019–2025.

Key Developments

Segmentation

The global school management system market has been segmented based on component, application, deployment mode, end user, and region.

By deployment mode, the market has been segmented into cloud and on-premise.

Based on end user, the global school management system market has been categorized into schools, universities, community colleges, and others.

Regional Analysis

The global school management system market has been categorized on the basis of geography into North America, Europe, Asia-Pacific, the Middle East and Africa, and South America.

Key Players

More Information : https://www.marketresearchfuture.com/reports/school-management-system-market-8398

#Global School Management System Market #Global School Management System Market Report #Global School Management System Market Research #covid-19 #component #application #forecast

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ketan-wagh121-blog · 5 years ago

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Global Risk Analytics - Global Industry Size, Share, Trends, Analysis and Forecast 2019 – 2025

Global Risk Analytics Industry

New Study On “2019-2025 Risk Analytics Market Global Key Player, Demand, Growth, Opportunities and Analysis Forecast” Added to Wise Guy Reports Database

Request Free Sample Report @ https://www.wiseguyreports.com/sample-request/3750815-global-risk-analytics-market-by-risk-type-market

For Detailed Reading Please visit WiseGuy Reports @ https://www.wiseguyreports.com/reports/3750815-global-risk-analytics-market-by-risk-type-market

Norah Trent WISEGUY RESEARCH CONSULTANTS PVT LTD 8411985042

NOTE : Our team is studying Covid-19 and its impact on various industry verticals and wherever required we will be considering Covid-19 footprints for a better analysis of markets and industries. Cordially get in touch for more details.

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globalmediacampaign · 5 years ago

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Disaster recovery on Amazon RDS for Oracle using AWS DMS

AWS Database Migration Service (AWS DMS) helps you migrate data from databases on-premises to Amazon Relational Database Service (RDS). You can also use it to migrate data between heterogeneous or homogeneous database engines, among other things. Businesses of all sizes use AWS to enable faster disaster recovery (DR) of their critical IT systems without having to set up a second physical site. A DR solution depends upon the RTO/RPO. For more information about best practices, see New Whitepaper: Using AWS for Disaster Recovery. This post explores how to use AWS DMS to set up a DR solution for Oracle databases running on the RDS platform. Why AWS DMS for disaster recovery? A DR solution in which the primary database instance is running on AWS, requires a cross-region replication mechanism. AWS DMS supports live migration of data from RDS to anywhere, including a different Region. You can use this feature to set up a separate RDS instance in a different Region to serve as a DR database. While there are other options to set up a DR site for Oracle on RDS, such as Oracle Golden Gate, AWS DMS provides a low-cost, simple, all-native approach. Additionally, there are no dependencies on using the Enterprise or Standard edition. AWS DMS supports full load migration and change data capture (CDC), thus enabling continuous data replication. Another possibility when using AWS DMS is that unlike physical replication, which works on the database instance level, AWS DMS is a logical replication solution and you can migrate a subset of your database such as schemas or a set of tables. In instances using Oracle, AWS DMS determines and tracks data changes by reading the transaction log using the Oracle LogMiner API or binary reader API. AWS DMS reads ongoing changes from the online or archive redo logs based on the system change number (SCN). For more information, see Working with an Amazon-Managed Oracle Database as a Source for AWS DMS. Enable database-level supplemental logging to capture changes for replication. Similarly, you must enable supplemental logging for each table that you want to migrate. Enable supplemental logging primary key for tables with a primary key, and supplemental all column for tables without a primary key or unique key. ARCHIVELOG MODE should be on to provide information to LogMiner. DMS uses LogMiner by default to read information from the archive logs to capture changes. DMS also offers a choice of using LogMiner or Binary Reader for reading the redo logs. Both approaches have merits and flaws, so the choice depends heavily on the replication requirements. For more information, see Using Oracle LogMiner or Oracle Binary Reader for Change Data Capture (CDC). AWS DMS allows you to do a full-load migration, CDC, or a full-load migration with CDC. However, while setting up a DR RDS instance, it may be more efficient to pre-load the target instance using RDS snapshots. This can save costs because you don’t need a replication instance for doing the full load and especially if the size of the database is large. You can copy an RDS database snapshot across Regions. This post outlines the steps required to set up a DR RDS instance and perform continuous replication to achieve near-zero RTO/RPO using a sample RDS for Oracle instance. Things to remember before using DMS as a DR solution AWS DMS captures ongoing changes to the source data store and applies those on the target data store. While this feature can be leveraged to fulfill a DR use case, there are some points to note. Performance: Performance of your DR solution using AWS DMS may vary for each database, as there are a number of factors that affect the performance of your migration. These include resource availability on the source, available network throughput, resource capacity of the replication server, ability of the target to ingest changes, type and distribution of source data, number of objects to be migrated, and so on. The performance of your migration will be limited by one or more bottlenecks you encounter along the way. Please refer to AWS Database Migration Service Best Practices for more information on performance considerations. Also note the fact that since AWS DMS is a logical replication solution, performance would depend on the nature of data and transactions on the source. AWS DMS uses Oracle LogMiner for change data capture (CDC) by default. Alternatively, you can use Oracle Binary Reader, which greatly improves performance and reduces the load on the Oracle server when compared with LogMiner for certain considerations. Limitations of source/target: AWS DMS does not replicate DDL operations such as ADD, DROP, EXCHANGE, or TRUNCATE and data changes resulting from partition or subpartition operations. There are limitations on using the ALTER TABLE command as well. For a complete list of limitations, see Limitations on Using Oracle as a Source for AWS DMS. Similarly, AWS DMS doesn’t create schema on the target RDS for Oracle database. So, in a DR scenario, it is recommended that a target database instance is created by restoring an RDS snapshot, which also takes care of a one-time data load. Alternately, Oracle Data Pump can be used. This blog uses the former approach. For a complete list of limitations, see Limitations of Oracle as a target for AWS DMS. Latency: AWS DMS uses either the Oracle LogMiner API or binary reader API to read ongoing changes. A busy source database that generates a large number of redo logs can result in significant latency. Binary reader bypasses LogMiner and reads the logs directly. Therefore, it is important to weigh the options between LogMiner and binary reader. Additionally, long running transactions can lead to source latency as AWS DMS only reads incoming changes from the transaction logs, but AWS DMS forwards only committed changes to the target during ongoing replication. The other consideration is LOB data. AWS DMS migrates LOB data for ongoing replication in two phases. First, AWS DMS creates a new row in the target table with all columns except those that have LOBs. Then, AWS DMS updates the rows that have LOBs. If you have a source database that frequently updates tables that have LOB columns, then you might see source latency. Latency is also possible due to the target database characteristics. It could be because there are no primary keys or indexes on the target. As AWS DMS uses logical replication, if the required indexes aren’t in place, then changes like UPDATEs and DELETEs can result in full table scans. Full table scans can cause performance issues on the target and result in target latency. Additional considerations are resource bottlenecks on the source and the target database instances as well as the replication instance. In situations where there are defined SLAs around RTO/RPO requirements, latency is a key consideration. Customers are encouraged to test this solution to arrive at their own benchmarks. They should weigh the different options to replicate the data from primary instance to the DR instance such as Oracle Golden Gate, Cross-region Read Replicas to see what works best for them. You can use the CloudWatch service metric for CDCLatencySource and CDCLatencyTarget to monitor the replication latency for an AWS DMS task. Monitoring: You can use Amazon CloudWatch and metrics to monitor the progress of your DMS task, the resources used, and the network activity used. You can monitor replication host metrics, replication task metrics, and table metrics by enabling Amazon CloudWatch logging. Basic CloudWatch statistics for each task, including the task status, percent complete, elapsed time, and table statistics can be obtained from AWS DMS console. Performance metrics for Replication Instance includes CPUUtilization, FreeStorageSpace, FreeableMemory, and many more. Replication task-related metrics such as incoming and committed changes and latency between the replication host and source and target databases are included. Table metrics include those that are in the process of being migrated, the number of insert, update, delete etc. Amazon CloudWatch alarms and events can be set up to more closely track the migration. Detailed guidance on debugging the migrations can be obtained from Debugging your AWS DMS Migrations. Architecture overview This architecture involves a primary RDS for Oracle instance running in Region1 in its own VPC and subnet. AWS Database Migration Service, which is used to set up the replication to the DR Region, is running in a separate EC2 instance in the same VPC. AWS DMS runs the database migration jobs that replicate data. The target RDS for Oracle instance is running in Region2 in its own VPC and a public subnet. This instance is created from a snapshot of the primary RDS instance and replicated real time with the primary using CDC. The following diagram shows the architecture of this solution, which sets up multi-region DR on RDS for Oracle using AWS DMS. Solution overview This solution contains the following steps: Set up the primary RDS for Oracle instance in the Region that you choose. This step is optional. In a real-world scenario, the primary database is already set up. Configure a user for AWS DMS to use with appropriate permissions on the source database. Additionally, enable the supplemental logging on the objects. Make sure that the database is quiesced so that there are no updates. One way to do this is to perform this during the scheduled maintenance window. Alternatively, instead of RDS snapshots, consider using the Oracle export and import function. This post uses the RDS snapshot approach, in which it is assumed that you perform the steps during the maintenance window. Before creating the manual snapshot, obtain the SCN number of the database. You use a snapshot of the instance to do an initial migration of the database. You can copy the manual snapshot to DR’s Region. For more information, see Copying a Snapshot. After the snapshot is copied, a new RDS instance is provisioned in the DR Region using this snapshot. Set up the AWS DMS replication to use CDC with the SCN number captured previously as the starting point. This post uses a Single-AZ deployment of both RDS and AWS DMS. In production environments, it is recommended that you choose a Multi-AZ configuration for both. Also, the replication instance is placed in a public subnet to access the target RDS instance. Avoid this in production scenarios, and explore other options such as peering. Step 1: Setting up the sample primary RDS for Oracle instance To begin, run the following CloudFormation template. This template is an example of how to set up DR on RDS for Oracle with AWS DMS. This script provisions a new VPC and subnets along with the RDS for Oracle instance in the Region in which you execute the script. The script also provisions a Windows instance with SQL Developer installed to use with the Oracle database. The following table summarizes the parameters required for running the script. Parameter label (name) Default Description Stack Name Requires input Any name RDSInstanceType Requires input Select from the drop-down box as per your needs EC2ServerInstanceType Requires input Select from the drop-down box as per your needs KeyName Requires Input Name of the existing key pair to enable access to instance. Select from the drop-down box. VpcCIDR 10.0.0.0/16 CIDR block for the VPC Subnet1CIDR (PrivateSubnet1CIDR) 10.0.0.0/19 CIDR block for the private subnet 1 Private Subnet 2 CIDR (PrivateSubnet2CIDR) 10.0.32.0/19 CIDR block for the private subnet 2 The following screenshot shows the parameters entry screen. This post provides a sample database that you can use to test the configuration. You can also make modifications to the database by inserting rows in any of the tables. You are now are ready to create a DR instance and set up a real-time replication mechanism to keep both databases closely in sync. Navigate to the RDS instance that you created. To obtain the endpoint name of the instance, look at the SourceOracleEndpoint key in the CloudFormation Outputs section. Similarly, obtain the DNS endpoint of this instance from SourceEC2PublicDNS. Use Remote Desktop software to connect to the Windows instance provisioned by the CloudFormation script that was run above. This instance already has SQL Developer installed on it. Next, configure a new database connection using SQL Developer to connect to the RDS for Oracle instance created above. Given below are the details: Name. NewOracleSource Database type. Oracle Username. dbmaster Password. dbmaster123 Connection Type. Basic Hostname. <> Port. 1521 SID. OracleDB Now click on ‘Test’. It should display ‘success’ at the bottom of the screen. The following screenshot shows the connection details. Step 2: Configuring the user for AWS DMS to use After you are connected, enter the following code to grant the following privileges to the AWS DMS user to access the source Oracle endpoint: GRANT SELECT ANY TABLE to DMS_USER; GRANT SELECT on ALL_VIEWS to DMS_USER; GRANT SELECT ANY TRANSACTION to DMS_USER; GRANT SELECT on DBA_TABLESPACES to DMS_USER; GRANT SELECT on ALL_TAB_PARTITIONS to DMS_USER; GRANT SELECT on ALL_INDEXES to DMS_USER; GRANT SELECT on ALL_OBJECTS to DMS_USER; GRANT SELECT on ALL_TABLES to DMS_USER; GRANT SELECT on ALL_USERS to DMS_USER; GRANT SELECT on ALL_CATALOG to DMS_USER; GRANT SELECT on ALL_CONSTRAINTS to DMS_USER; GRANT SELECT on ALL_CONS_COLUMNS to DMS_USER; GRANT SELECT on ALL_TAB_COLS to DMS_USER; GRANT SELECT on ALL_IND_COLUMNS to DMS_USER; GRANT SELECT on ALL_LOG_GROUPS to DMS_USER; GRANT LOGMINING TO DMS_USER; The following screenshot shows this code in the SQL Developer console. Additionally, enter the following code: exec rdsadmin.rdsadmin_util.grant_sys_object('V_$ARCHIVED_LOG','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$LOG','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$LOGFILE','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$DATABASE','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$THREAD','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$PARAMETER','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$NLS_PARAMETERS','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$TIMEZONE_NAMES','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$TRANSACTION','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('DBA_REGISTRY','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('OBJ$','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('ALL_ENCRYPTED_COLUMNS','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$LOGMNR_LOGS','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('V_$LOGMNR_CONTENTS','DMS_USER','SELECT'); exec rdsadmin.rdsadmin_util.grant_sys_object('DBMS_LOGMNR','DMS_USER','EXECUTE'); To retain archived redo logs of the source Oracle database instance for 24 hours, enter the following query: exec rdsadmin.rdsadmin_util.set_configuration('archivelog retention hours',24); To enable database-level supplemental logging, enter the following query: exec rdsadmin.rdsadmin_util.alter_supplemental_logging('ADD'); To enable PRIMARY KEY logging for tables that have primary keys, enter the following query: exec rdsadmin.rdsadmin_util.alter_supplemental_logging('ADD','PRIMARY KEY'); To add supplemental logging for tables that don’t have primary keys, enter the following queries: alter table dms_sample.nfl_stadium_data add supplemental log data (ALL) columns; alter table dms_sample.mlb_data add supplemental log data (ALL) columns; alter table dms_sample.nfl_data add supplemental log data (ALL) columns; Step 3: Creating a manual snapshot of the source RDS instance Make sure that the database is quiesced, meaning that no updates are possible on the database. To get the SCN number of the database, enter the following query: select CURRENT_SCN from v$database; Note the output of the preceding query. It is in the form of a number, such as 3829791. This step is required to set a starting point for the CDC while configuring the AWS DMS replication task. For more information, see Creating Tasks for Ongoing Replication Using AWS DMS. You can now set up the DR instance and load the initial data. To do that, use the RDS Snapshots feature. Complete the following steps: On the AWS Management Console, go to the source RDS database you created. Choose Snapshots. From the Actions drop-down, choose Copy Snapshot. Under Source DB Snapshot, for Destination Region, choose your DR. This post chooses Asia Pacific (Sydney). Give a name in the field New DB Snapshot Identifier. In this case it is source-snapshot. Leave defaults for all the other fields. Choose Copy Snapshot. RDS automatically shows the snapshot in the destination Region. Make a note of the ARN of the snapshot. Step 4: Creating an RDS instance in the DR Region Run the following CloudFormation template. This sets up an RDS instance for DR purposes in a different Region, using the snapshot you created. The following table summarizes the parameters needed for the template. Parameter label (name) Default Description Stack Name Requires input Any name ClientIP Requires input The IP address of the client machine that is used to connect to the RDS for Oracle Database DBSnapshotId Requires input The RDS snapshot that is available within the Region; created by the previous steps The following screenshot shows the parameters entry screen. You can obtain the endpoint details of the target RDS instance from the Outputs section of the CloudFormation screen on the AWS Management Console. After you provision the RDS instance in the DR Region, verify the connection to the new instance from the same Windows client instance that you provisioned in Step 1. The SID is TargetDB and username and password remain the same as the source instance. Also check that both databases are in sync by running some test SQLs. The next step is to capture and replicate the changes from the source to the target using AWS DMS. The following CloudFormation template takes the details of the source and target RDS instances as parameters and creates the AWS DMS replication resources required to perform the CDC. You can set up the replication instance in a Region in which the primary database instance is running or in a DR Region. If you are only migrating or replicating a subset of data using filters or transformations, you should keep the replication instance on the source side, because the amount of data transferred over the network to the DR Region is less. For this post, and in other cases of full database migration and ongoing replication, you can keep the AWS DMS replication instance on either side. The following table summarizes the parameters needed for the template. Parameter label (name) Default Description Stack Name Requires input Any name ExistsDMSCloudwatchRole N If the dms-cloudwatch-logs-role is already present in your account, enter Y; otherwise leave the default ExistsDMSVPCRole N If the dms-vpc-role exists in your account, enter Y; otherwise leave the default OracleRDSSourceEndpoint Requires input The endpoint of the source RDS for Oracle Database OracleRDSSourcePort 1521 The port of the source RDS for Oracle Database OracleRDSSourceUser Requires input The user for the target RDS for Oracle Database OracleRDSSourcePassword Requires input The password for the preceding user OracleRDSTargetEndpoint Requires input The endpoint of the target RDS for Oracle Database OracleRDSTargetPort 1521 The port of the source RDS for Oracle Database OracleRDSTargetUser Requires input The user for the target RDS for Oracle Database OracleRDSTargetPassword Requires input The password for the preceding user SCNNumber Requires input System change number of Oracle; follow the instructions in the document to obtain this SourceDatabase Requires input The source RDS for Oracle Database name TargetDatabase Requires input The target RDS for Oracle Database name VPC Requires input VPC ID of the existing Virtual Private Cloud that you set up in the previous step Subnets Requires input Select the subnets for the DMS instance from the drop-down The following screenshot shows the parameters entry screen. After you execute the script successfully, you have created all the AWS DMS resources you must perform the CDC, and the replication task should be running. You can find these resources by navigating to the AWS DMS screen within the console. It takes a few minutes before the status of the replication task changes from Ready to Replication Ongoing. If it doesn’t automatically change, complete the following steps: On the DMS console, select the task. From the Actions drop-down, choose Restart/Resume. Step 5: Testing the CDC You can now test if your replication is working. To insert dummy rows on the source and verify that they are instantly replicated to the target, run the following SQLs: INSERT ALL INTO dms_sample.sport_type (name,description) VALUES ('hockey', 'A sport in which two teams play against each other by trying to more a puck into the opponents goal using a hockey stick') INTO dms_sample.sport_type (name,description) VALUES ('basketball', 'A sport in which two teams of five players each that oppose one another shoot a basketball through the defenders hoop') INTO dms_sample.sport_type (name,description) VALUES ('soccer','A sport played with a spherical ball between two teams of eleven players') INTO dms_sample.sport_type (name,description) VALUES ('volleyball','two teams of six players are separated by a net and each team tries to score by grounding a ball on the others court') INTO dms_sample.sport_type (name,description) VALUES ('cricket','A bat-and-ball game between two teams of eleven players on a field with a wicket at each end') SELECT * FROM dual; COMMIT; SELECT * FROM dms_sample.sport_type; The AWS DMS task keeps the target Oracle database up to date with source database changes. The latency is close to zero when the target has caught up to the source. Triggering the DR With the preceding setup in place, in case of disaster, you would typically make the following changes so that applications can reach the secondary database (the new primary) and serve the requests efficiently: Modify DNS configurations or use the Amazon Route 53 Active-Passive failover feature. When failover occurs, the secondary database instance in a DR Region is now a new primary database instance. Scale up the new primary RDS instance to the capacity required by the application (consider licensing restrictions that apply for your Oracle database if you are using a BYOL model). Turn on the Multi-AZ option for the new primary database instance. Copy the RDS snapshot to another Region and note the SCN number, using the technique discussed previously. Launch the CloudFormation template to create a new secondary database in a target Region. Launch the CloudFormation template to set up an AWS DMS Replication instance, create, and run AWS DMS tasks from an SCN number you specified from the new primary database instance. For more information about reducing replication lag and making DMS replication highly available in case of AZ failure, see Best Practices for AWS Database Migration Service. Summary This post discussed how to set up a DR solution for your RDS for Oracle databases using DMS cost-effectively. AWS DMS captures ongoing changes to the source data store and applies those on the target data store. However, in situations where there are defined SLAs around RPO requirements, customers are encouraged to test the solution to arrive at their own benchmarks for this. They should weigh the different options to replicate the data from primary instance to the DR instance such as Oracle Golden Gate or Cross-region Read Replicas to see what works best for them. To familiarize yourself more on AWS Database Migration Service, please visit Getting started with AWS Database Migration Service. Please share your thoughts in the comments section on how this approach worked for you or your customers. About the Authors Madhuri Susarla is a Solutions Architect in the partner team of AWS. She is passionate about working with large Global System Integrators to create value in the era of cloud computing. She dabbled in multiple cloud platforms. She believes in enabling reusability by way of content creation and public speaking. She enjoys playing with her three boys, while struggling to keep up with their soccer, knowledge, and gastronomic choices. Ejaz Sayyed is a Partner Solutions Architect with the Global System Integrator (GSI) team at Amazon Web Services. His focus areas include AWS database services as well as database and data warehouse migrations on AWS. Recently, he is also supporting GSIs building data lakes on AWS for our customers. https://probdm.com/site/MzUyNg

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