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Month: April 2021

Electrical Flexibility: what is it?

MMS Founder
MMS • RSS

Article originally posted on Data Science Central. Visit Data Science Central

EPOCH 1618845450

NEURAL POWER [mW]: Introduction of the subject matter.
GOAL: Discuss solutions, methodologies, systems, projects to support the Energy Transition towards Energy Convergence.
TARGET: Operators, Customers, Regulators, Lawmakers, Inventors, Academics, Scientists, Enthusiasts.
MARKET: Energy Market.
TAG: #Epoch #ISOPROD #ISOCONF #Digitization #DemandResponse #Demand #Baseline #Methodology #Flexibility #Renewable #EnergyTransition #Optimization

CREDITS: [1] Chris Lawton from StockSnap; [2] dashu83 from it.freepik.com; [3] rawpixel.com from it.freepik.com; [4] d3images from it.freepik.com.

GLOSSARY

Transmission System Operator (TSO). Transmission System Operator is a natural or legal person responsible for operating, ensuring the maintenance of and, if necessary, developing the transmission system in a given area and, where applicable, its interconnections with other systems, and for ensuring the long-term ability of the system to meet reasonable demands for the transmission of electricity. [^1]
Virtual Enabled Mixed Unit (VEMU). Aggregate (also known as industrial districts) consisting of production, consumption and storage plants that participate in the #Flexibility processes, governing the use of energy according to the actual power needs. Storage systems functional to electric mobility are also part of the UVAM pilot project, as these are considered to be completely comparable to other storage systems. [^2]
ISOPROD. Electric load profile of the Consumption Units (CU) (mapped within the industrial-type production process) built respecting all the constraints of the process itself, i.e. the production performance index (Qty / h).
ISOCONF. Electric load profile of the Consumption Units (CU) (mapped within the supply chain responsible for providing environmental services) built respecting all system constraints, i.e. the environmental conditions to be supplied (*temperature, humidity,…*).

Problem

The power grid is focused on scheduling production based on the forecast of consumption. However, information exchanges take place exclusively between system operators without the active involvement of consumers (industrial, residential customers), therefore the actual knowledge of #Demand not ables to plan the delicate network balance in advance, these continuous imbalances have the following consequences:
  • increase in system costs;
  • impossibility using non-programmable renewable sources;
  • need to keep power plants from non-renewable sources in operation;
  • creation of new power plants to coverage changes in demand;
  • limited operation in the #Flexibility market, mainly focused on capacity incentives;
  • increase in the carbon footprint.
Currently, the operators who are part of the electricity ecosystem are faced with a series of questions to be answered through long-term strategies and solutions:
  • “What strategy to adopt?“
  • “How to overcome the current problems in the electricity sector managed so far in a monolithic way and using a privileged position (incentives)?“
  • “What solutions to adopt to unlock the new values of the #Flexibility in the panorama of #EnergyTransition?“
This #Epoch introduces the concept of #Flexibility starting from its institutional definition up to describing how #Demand #Optimization can evolve the electricity system from one-way to multi-directional, decentralized and flexible. Starting from this #Epoch, all questions will be answered in a qualitative, quantitative, systemic and above all economic / financial way with the aim of building a winning strategy that has as its objective the reduction of pollution due to the current power grids.

Solution

The programming of electricity consumption through #Digitization processes allows the evolution of the system towards a more virtuous model centered on the #DemandResponse paradigm, through which it is possible to plan the production of the exact amount of energy required, with the following effects:
  • valorisation of #Renewable according to priorities as to economic, physical and environmental;
  • grid inbalancing avoided through the implementation of dynamic corrections to consumption programs, without impacting operational processes;
  • active participation of the #Demand in the #Flexibility processes.
The consumers from simple passive users of energy will assume an increasingly active and central role in the balance of the electricity system.

There are different forms of participation in the evaluation of the application.

Definition of Electrical Flexibility
Regulatory scope
“Dispatching services provided by the generation, consumption and storage of energy according to criteria of technological neutrality […], through the figure of the aggregator, […] reflecting the correct value of the electricity in real time on the National Transmission Grid, […] compatibly with the network constraints, of the imbalances of the units enabled to participate in the dispatching services market.“
Electricity Demand Optimization
“Ability to plan and dynamically modulate #Demand on the basis of a map of consumption processes, transforming the limits of non-programmability of production into nodal dispatching constraints by defining a decentralized and balanced network model.“

“What do you need to make the electricity grid flexible?“

The principles of the methodology
The #Methodology of the #Optimization of the #Demand is achieved through the construction of the electricity program (#Baseline) of its own characteristic absorption profile, enabling the services companies to Create and Enhance their own energy #Flexibility and profit from it.

For each CU (Consumption Unit) the #Methodology takes place through the following phases:
Profiling
  • Real-time acquisition of the energy consumption data of each load;
  •  Building of the *Characteristic Energy Profile*;
  • Definition of the energy consumption program associated with the operational activity in compliance with the predetermined performance indices.
Scheduling
  • Dynamic implementation of energy consumption programs (`#Baseline`) through ordinary modulation of set points;
  • Periodic verification of actual compliance with the predetermined performance indices.
Balancing
  • Dynamic correction of the consumption program through an extraordinary modulation of the set points, drawing as needed from a predetermined list of possible interventions;
  • Punctual activation in the event of operational criticalities or in the face of remuneration opportunities.
Flexibility
  • Identification of the actual availability (`#Flexibility`)of modulated energy in compliance with the predetermined performance indices;
  • Periodic communication of the consumption program and availability to modulation towards the aggregator;
  • Implementation of the modulation requested by the aggregator when the network is actually required.
Performance Indices
ISOPROD: Quantitative constraints of the industrial supply chains (material quantity or number of pieces / hour) for compliance with production plans.

ISOCONF: Quality constraints of environmental services (temperature, humidity, lighting) for maintaining the comfort of the users of the building.

The correlation between the performance indices and the electrical absorption profile of the loads mapped within the operating process is the first fundamental step to be able to plan consumption.

“The #Flexibility is energy created by #Demand, mapped by math models and transformed by the algorithms into a new fungible commodity.”
Roberto Quadrini

Benefits
The roadmap for participation is an opportunity optimizing and efficiency, which actively contributes to the Carbon footprint reduction, with the following benefits:
  • awareness of the impact of energy consumption on its operating activities.
  • reduction of costs associated with energy consumption;
  • improvement of corporate image positioning through Corporate Social Responsibility.
Green Deal and Energy Transition
The proposed #Methodology contributes to the achievement of 3 objectives of the Green Deal:
  • Supply clean, affordable and secure energy;
  • Building and renovating in an energy and resource efficient way;
  • Accelerating the switch to sustainable and smart mobility.
The European Directive 2019/944 and European Regulation 2019/943, are focused on:
  • Decarbonisation;
  • #Flexibility;
  • Active participation of consumer/prosumers.
It is compatible with the of the European Directive 2018/844 relating to Smart Buildings, as well as with the European Directive 2018/2001 (RED II) concerning Energy Communities and Renewable Energy Sources.

“The power grid is made intelligent by the #Demand which indicates to production its needs, that are generated of human mind“,
Roberto Quadrini
……………
[^1] “Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity and amending Directive 2012/27/EU, Article 2(35)”.
[^2] “ARERA Directive 422/2018/R/eel, 300/2017“

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Amazon Updates Its Elasticsearch Service, Begins Embrace of New Fork

MMS Founder
MMS • Jared Ruckle

Article originally posted on InfoQ. Visit InfoQ

Amazon recently released several enhancements to Amazon Elasticsearch Service. The new capabilities stem from two different sources: Elasticsearch, the project long associated with the service, and Open Distro for Elasticsearch, a new fork. Amazon and others created the fork in response to licensing changes made by Elastic earlier this year.

The most noteworthy update to Amazon Elasticsearch Service is asynchronous search. The API enables users to execute certain queries that would otherwise time out. Product documentation suggests that async search is useful when searching large data sets, and for querying data stored on cheaper hardware. Users performing wildcard searches are also likely to see improved results.

Amazon’s new asynchronous search feature is interesting for another reason: its origin. The async search API was introduced in Elasticsearch nearly a year ago. Amazon could have bundled this async search code, as permitted by the Apache 2.0 license. Instead, the company adopted the API published by Open Distro for Elasticsearch. Amazon recently shipped other enhancements originating from the new fork, including trace analytics and reporting for Kibana. Additionally, Amazon Elasticsearch Service now publishes events to Amazon CloudWatch and Amazon EventBridge. This integration aims to offer users better insight into lifecycle events of the Elasticsearch service.

Amazon intends to rename Amazon Elasticsearch Service to Amazon OpenSearch Service.

Elastic includes async search in its commercial offerings. The feature is available for “free and open” use under the Elastic License. Elastic acknowledges that the Elastic License is not an OSI-approved license.

In a separate announcement, Amazon detailed its support for Elasticsearch 7.10. Users can create new domains based on version 7.10, and upgrade existing domains to the new version.

In a blog post announcing the licensing change, Elastic indicated that Elasticsearch version 7.10 is the final release distributed under the Apache 2.0 license. As such, it is highly likely that Amazon will turn to Open Distro for Elasticsearch as the source for future commercial enhancements. Elastic released Elasticsearch version 7.10 in November 2020.

Users have several options for a cloud-managed version of Elasticsearch, beyond the Amazon offering. Elastic offers managed versions of Elasticsearch on AWS, Microsoft Azure, and Google Cloud Platform. Here, users can opt to deploy more recent versions of the service, specifically versions 7.12.0, and 7.11.2. A 14-day free trial is available. Practitioners may also deploy Elastic-managed instances via the Azure and Google Cloud marketplaces. There are some caveats to this path, as Elastic documents for both Microsoft and Google customers. Self-managed versions are also available in the marketplaces of all three major clouds.

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Mobile Backend-as-a-Service Software Market to Witness Huge Growth by 2028 | Salesforce, Built …

MMS Founder
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Posted on mongodb google news. Visit mongodb google news

JCMR recently introduced Global Mobile Backend-as-a-Service Software Market study with focused approach on market size & volumes by Application, Industry particular process, product type, players, and production & Consumption analysis considering major factors, cost Structure and regulatory factors. At present, the market is developing its presence and some of the key players from the complete study are Salesforce, Built.io, Rackspace, Parse, AWS, Azure, MongoDB Stitch, AnyPresence, Kinvey, Apache
The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions

Our report will be revised to address COVID-19 Post pandemic effects on the Global Mobile Backend-as-a-Service Software Market.

Click to get Global Mobile Backend-as-a-Service Software Market Research Free Sample PDF Copy Here Before Purchase @: jcmarketresearch.com/report-details/1290503/sample

Market segmentation information from 2012-2028
On The Basis Of Type: [Type]

On The Basis Of Applications/ end users: [Application]
On The Basis Of Regions: North America, Europe, China, Japan, Rest of the World

This study also contains company profiling, product picture and specifications, sales, market share and contact information of various international, regional, and local vendors of Global Mobile Backend-as-a-Service Software Market, some of them are Salesforce, Built.io, Rackspace, Parse, AWS, Azure, MongoDB Stitch, AnyPresence, Kinvey, Apache. The market competition is constantly growing higher with the rise in technological innovation and M&A activities in the industry. Moreover, many local and regional vendors are offering specific application products for varied end-users. The new vendor entrants in the market are finding it hard to compete with the international vendors based on quality, reliability, and innovations in technology.

Buy Full Copy with Exclusive Discount on Global Mobile Backend-as-a-Service Software Market @ jcmarketresearch.com/report-details/1290503/discount

Highlights about report coverage:

– A complete background analysis, which includes an assessment of the Global Mobile Backend-as-a-Service Software Market.
– Important changes in Mobile Backend-as-a-Service Software market dynamics
– Mobile Backend-as-a-Service Software Market segmentation up to the second & third level regional bifurcation
– Historical, current, and projected size of the Mobile Backend-as-a-Service Software market with respect to both value (Revenue) and volume (Production & Consumption)
– Reporting and evaluation of recent Mobile Backend-as-a-Service Software industry developments
– Mobile Backend-as-a-Service Software Market shares and strategies of key players
– Emerging niche segments and regional markets
– An objective assessment of the trajectory of the Mobile Backend-as-a-Service Software market
– Recommendations to companies for strengthening their foothold in the Mobile Backend-as-a-Service Software market

Additionally the export and import policies that can make an immediate impact on the Global Mobile Backend-as-a-Service Software Market. This study contains a EXIM* related chapter on the Global Mobile Backend-as-a-Service Software Market and all its associated companies with their profiles, which gives valuable data pertaining to their outlook in terms of finances, product portfolios, investment plans, and marketing and business strategies.

Enquire for customization in Global Mobile Backend-as-a-Service Software Market Report @ jcmarketresearch.com/report-details/1290503/enquiry

There are following 15 Chapters to display the Global Mobile Backend-as-a-Service Software Market.

Table of Contents
1 Market Overview
1.1 Global Mobile Backend-as-a-Service Software Introduction
1.2 Market Analysis by [Type]
1.3 Market Analysis by [Application]
1.4 Market Analysis by North America, Europe, China, Japan, Rest of the World
1.5 Market Dynamics
1.5.1 Market Opportunities
1.5.2 Market Risk
1.5.3 Market Driving Force

2 Manufacturers Profiles
2.1.1 Business Overview
2.1.2 Global Mobile Backend-as-a-Service Software Market Type and Applications
2.1.3 Mobile Backend-as-a-Service Software Sales, Price, Revenue, Gross Margin and Market Share and SWOT analysis (2019-2020)

3 Global Mobile Backend-as-a-Service Software Market Competition, by Manufacturer
4 Global Mobile Backend-as-a-Service Software Market Analysis by Regions including their countries
5 North America, Europe, China, Japan, Rest of the World

6 Product Type- [Type]

7 Application Type- [Application]

8 Key players- Salesforce, Built.io, Rackspace, Parse, AWS, Azure, MongoDB Stitch, AnyPresence, Kinvey, Apache
.
.
.
10 Global Mobile Backend-as-a-Service Software Market Segment by [Type]
11 Global Mobile Backend-as-a-Service Software Market Segment by Application
12 Global Mobile Backend-as-a-Service Software Market COVID-19 Impacted Forecast (2020-2028)
13 Sales Channel, Distributors, Traders and Dealers
14 Research Findings and Conclusion
15 Appendix
….Continued

Complete report on Global Mobile Backend-as-a-Service Software Market report spread across 200+ pages, list of tables & figures, profiling 10+ companies. Select license version and Buy this updated Research Report Directly @ jcmarketresearch.com/checkout/1290503
How Are We Different? & Why Choose Us?

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Find more research reports on Mobile Backend-as-a-Service Software Industry. By JC Market Research.

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Benefits of improving data quality

MMS Founder
MMS • RSS

Article originally posted on Data Science Central. Visit Data Science Central

As the digital world continues to become more competitive, everyone is trying to understand their customers better and make finance, development, and marketing decisions based on real data for a better ROI.

Bad data is misleading and would be even more detrimental to your business than a lack of data at all. Organizations may also be forced to abide by data quality guidelines because of compliance issues. If your business’s data is not properly maintained or organized, you may struggle to demonstrate compliance. They find themselves in possession of sensitive personal and financial data such as banks may particularly face more stringent data management prerequisites, if not complied.

Good data quality enables:

Effective decision making: Good quality data leads to accurate and realistic decision-making and also boosts your confidence as you make the decisions. It takes away the need to guesstimate and saves you the unnecessary costs of trials and errors.

More focused: As part of the value chain proposition, it’s critical you know who your prospects are – something that you can only manage analyzing and understanding data. Using high-quality data from your current customer base, you can create user personas and anticipate the needs of the new opportunities and target markets.

Efficient marketing: There are many forms of digital marketing out there, and each one of them works differently for different products in various niches. Good data quality will help you identify what’s working and what’s not.

Better customer relationships: You cannot succeed in any industry if you have poor customer relations. Most people only want to do business with brands they can trust. Creating that bond with your customers starts with understanding what they want.

Competitive Advantage: Being in possession of good quality data gives you a clearer picture of your industry and its dynamics. Your marketing messages will be more specific, and your projections in market changes will bear more accuracy. It will also be easier for you to anticipate the needs of your customers, which will help you beat your rivals to sales.

An AI-augmented data platform, such as DQLabs, would help you detect and address poor data quality issues without the need for much human effort. Since it is AI-based, it will discover patterns and, if possible, tune itself to curb data quality issues of the type it has come across before.

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HTAP-Enabling In-Memory Computing Technologies Market Growth Factors, Opportunities …

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Posted on mongodb google news. Visit mongodb google news

  


Global HTAP-Enabling In-Memory Computing Technologies Market share and competition analysis report made available with global competition in mind. This report provides profiles of key key suppliers, HTAP-Enabling In-Memory Computing Technologies market technological progress, market size estimation, market competitive situation and development trends, emerging opportunities, growth prospects, types and applications of the HTAP-Enabling In-Memory Computing Technologies market for the period 2021-2027.

The HTAP-Enabling In-Memory Computing Technologies market forecast research report 2027 provides a basic overview of the industry, including market analysis, classifications, types, applications and trends of the HTAP-Enabling In-Memory Computing Technologies industry.

The market landscape and market scenario include:

– Estimate of the current market size HTAP-Enabling In-Memory Computing Technologies
– Revenue per player – Top Manufacturers
– market size by product categories
– market size by region / country

Competitive landscape:

Industry analysis provides a basic description of the principles, business life cycle, applications and functionality of the business chain. These variables can help key players grasp the complexity of the market, satisfy a consumer’s desires, and have specific characteristics. Geographical views and sales and market share data are presented in the HTAP-Enabling In-Memory Computing Technologies study . Market research provides the growth rate in the forecast period and its data. The report helps explain the revenue generated during the entire market growth by the industry. It consists of knowledge related to market trends, such as opportunities for expansion, obstacles that emerge during this vertical process, and market factors. Focusing on core applications / users, this HTAP-Enabling In-Memory Computing Technologies industry study focuses on the prospects and status of core applications / end users, market share, usage (sales) and pricing for each application.

The report covers the following key players in the HTAP-Enabling In-Memory Computing Technologies Market: :

  • SAP
  • IBM
  • Microsoft
  • DataStax
  • MongoDB
  • Aerospike
  • GridGain

Segmentation of HTAP-Enabling In-Memory Computing Technologies Market:

By the product type, the market is primarily split into:

  • Cloud-Based
  • On-Premises

By the application, this report covers the Following segments: :

  • Large Enterprises(1000+ Users)
  • Medium-Sized Enterprise(499-1000 Users)
  • Small Enterprises(1-499 Users)

HTAP-Enabling In-Memory Computing Technologies Market Report Scope 

Report Attribute Details
Market size available for years 2021 – 2027
Base year considered 2021
Historical data 2015 – 2019
Forecast Period 2021 – 2027
Quantitative units Revenue in USD million and CAGR from 2021 to 2027
Segments Covered Types, Applications, End-Users, and more.
Report Coverage Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, and Trends
Regional Scope North America, Europe, Asia Pacific, Latin America, Middle East and Africa
Customization scope Free report customization (equivalent up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope.
Pricing and purchase options Avail of customized purchase options to meet your exact research needs. Explore purchase options

HTAP-Enabling In-Memory Computing Technologies Geographic Market Analysis:

This HTAP-Enabling In-Memory Computing Technologies market report examines top producers and consumers, focusing on product capacity, value, consumption, market share and growth opportunities in these key regions, covering

  • North America (United States, Canada and Mexico)
  • Europe (Germany, France, UK, Russia and Italy)
  • Asia-Pacific (China, Japan, Korea, India and Southeast Asia)
  • South America (Brazil, Argentina, Colombia etc.)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Table of contents.

  • Overview of the global HTAP-Enabling In-Memory Computing Technologies market
  • Economic impact on industry
  • Competition in the market of manufacturers
  • Production, income (value)by region
  • Supply(production), consumption, exports, imports by region.
  • Production by type, income (value), price trends
  • Application market analysis
  • Manufacturing cost analysis
  • Industrial chain, procurement strategy and downstream buyers.
  • Marketing strategy analysis, distributors/traders
  • Analysis of market influencing factors
  • Global HTAP-Enabling In-Memory Computing Technologies market forecast

Visualize HTAP-Enabling In-Memory Computing Technologies Market using Verified Market Intelligence:-

Verified Market Intelligence is our BI-enabled platform to tell the story of this market. VMI provides in-depth predictive trends and accurate insights into more than 20,000 emerging and niche markets to help you make key revenue impact decisions for a brilliant future. 

VMI provides a comprehensive overview and global competitive landscape of regions, countries and segments, as well as key players in your market. Showcase your market reports and findings with built-in presentation capabilities, providing more than 70% of time and resources for investors, sales and marketing, R & D and product development. VMI supports data delivery in Excel and interactive PDF formats and provides more than 15 key market indicators for your market.


Clearly, this report will give you an unmistakable perspective on every reality of the market without the need to hint at some other research report or source of information. Our report will introduce you to the reality of the past, present and ultimate fate of the market.

Note–To provide more accurate market forecasts, all our reports will be updated prior to delivery by taking into account the impact of COVID-19.

Thank you for reading this article; you can also get a single chapter wise section or region Wise report version, such as North America, Europe or Asia, etc.

About Us: Market Research Intellect

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations in addition to the objective of delivering customized and in-depth research studies. 

Our speak to looking logical research solutions, custom consulting and in-severity data analysis lid a range of industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverages. Etc Our research studies assist our clients to make higher data-driven decisions, admit push forecasts, capitalize coarsely with opportunities and optimize efficiency by bustling as their belt in crime to adopt accurate and indispensable mention without compromise. 

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Big Data Software Market to Develop New Growth Story | IBM, MongoDB, Altair, SAP

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Big Data Software Market

A new business intelligence report released by HTF MI with title “Global Big Data Software Market Report 2020” is designed covering micro level of analysis by manufacturers and key business segments. The Global Big Data Software Market survey analysis offers energetic visions to conclude and study market size, market hopes, and competitive surroundings. The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing. Some of the key players profiled in the study are FCS Computer Systems, Qlik, IBM, Phocas Software, Cyfe, MongoDB, Altair, Elastic, MicroStrategy, Margasoft, SAP, Artelnics, Informatica, Radius, Teradata, Hitachi Vantara & IQLECT.

What’s keeping FCS Computer Systems, Qlik, IBM, Phocas Software, Cyfe, MongoDB, Altair, Elastic, MicroStrategy, Margasoft, SAP, Artelnics, Informatica, Radius, Teradata, Hitachi Vantara & IQLECT Ahead in the Market? Benchmark yourself with the strategic moves and findings recently released by HTF MI

Get Sample Report + All Related Graphs & Charts @ : https://www.htfmarketreport.com/sample-report/2499987-global-big-data-software-market-6

Market Overview of Global Big Data Software
If you are involved in the Global Big Data Software industry or aim to be, then this study will provide you inclusive point of view. It’s vital you keep your market knowledge up to date segmented by Applications [Small and Medium Enterprises (SMEs), Large Enterprises], Product Types [, On-premises, Cloud-Based] and major players. If you have a different set of players/manufacturers according to geography or needs regional or country segmented reports we can provide customization according to your requirement.

This study mainly helps understand which market segments or Region or Country they should focus in coming years to channelize their efforts and investments to maximize growth and profitability. The report presents the market competitive landscape and a consistent in depth analysis of the major vendor/key players in the market along with impact of economic slowdown.

Furthermore, the years considered for the study are as follows:
Historical year – 2015-2020
Base year – 2020
Forecast period** – 2021 to 2026 [** unless otherwise stated]

**Moreover, it will also include the opportunities available in micro markets for stakeholders to invest, detailed analysis of competitive landscape and product services of key players.

Enquire for customization in Report @: https://www.htfmarketreport.com/enquiry-before-buy/2499987-global-big-data-software-market-6

The titled segments and sub-section of the market are illuminated below:
The Study Explore the Product Types of Big Data Software Market: , On-premises, Cloud-Based

Key Applications/end-users of Global Big Data Software Market: Small and Medium Enterprises (SMEs), Large Enterprises

Top Players in the Market are: FCS Computer Systems, Qlik, IBM, Phocas Software, Cyfe, MongoDB, Altair, Elastic, MicroStrategy, Margasoft, SAP, Artelnics, Informatica, Radius, Teradata, Hitachi Vantara & IQLECT

Region Included are: North America Country (United States, Canada), South America, Asia Country (China, Japan, India, Korea), Europe Country (Germany, UK, France, Italy), Other Country (Middle East, Africa, GCC)

Important Features that are under offering & key highlights of the report:
– Detailed overview of Big Data Software market
– Changing market dynamics of the industry
– In-depth market segmentation by Type, Application etc
– Historical, current and projected market size in terms of volume and value
– Recent industry trends and developments
– Competitive landscape of Big Data Software market
– Strategies of key players and product offerings
– Potential and niche segments/regions exhibiting promising growth
– A neutral perspective towards Big Data Software market performance
– Market players information to sustain and enhance their footprint

Read Detailed Index of full Research Study at @ https://www.htfmarketreport.com/reports/2499987-global-big-data-software-market-6

Major Highlights of TOC:
Chapter One: Global Big Data Software Market Industry Overview
1.1 Big Data Software Industry
1.1.1 Overview
1.1.2 Products of Major Companies
1.2 Big Data Software Market Segment
1.2.1 Industry Chain
1.2.2 Consumer Distribution
1.3 Price & Cost Overview

Chapter Two: Global Big Data Software Market Demand
2.1 Segment Overview
2.1.1 APPLICATION 1
2.1.2 APPLICATION 2
2.1.3 Other
2.2 Global Big Data Software Market Size by Demand
2.3 Global Big Data Software Market Forecast by Demand

Chapter Three: Global Big Data Software Market by Type
3.1 By Type
3.1.1 TYPE 1
3.1.2 TYPE 2
3.2 Big Data Software Market Size by Type
3.3 Big Data Software Market Forecast by Type

Chapter Four: Major Region of Big Data Software Market
4.1 Global Big Data Software Sales
4.2 Global Big Data Software Revenue & market share

Chapter Five: Major Companies List

Chapter Six: Conclusion

Complete Purchase of Latest Version Global Big Data Software Market Study @ https://www.htfmarketreport.com/buy-now?format=1&report=2499987

Key questions answered
• How Global Big Data Software Market Growth & Size is Changing with Years to Come?
• Who are the Leading key players and what are their Key Business plans in the Global Big Data Software market?
• What are the key concerns of the five forces analysis of the Global Big Data Software market?
• What are different prospects and threats faced by the dealers in the Global Big Data Software market?
• What are the strengths and weaknesses of the key vendors?

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Big Data Analytics in Tourism Market is Booming Worldwide By Top Players, Growth, Trends and …

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Big Data Analytics in Tourism

The Global Big Data Analytics in Tourism Market analysis report provides a detail study of market size of different segments and countries of previous years and forecasts the values to the next Five years. This Big Data Analytics in Tourism Market report delivers both qualitative and quantitative aspect of the industry with respect to regions and countries involved in the report. Furthermore, this report also categorizes the market based on the type, application, manufacturers and all the crucial aspects of market drivers and restraining factors which can define the growth of the industry.

Studying and analyzing the impact of Coronavirus COVID-19 on the Big Data Analytics in Tourism industry, the report provides in-depth analysis and professional advices on how to face the post COIVD-19 period.

>>>Get a Sample Copy of the Report at –https://www.absolutereports.com/enquiry/request-sample/17086005

In terms of region, this research report covers almost all the major regions across the globe such as North America, Europe, South America, the Middle East, and Africa and the Asia Pacific. Europe and North America regions are anticipated to show an upward growth in the years to come. While Big Data Analytics in Tourism Market in Asia Pacific regions is likely to show remarkable growth during the forecasted period. Cutting edge technology and innovations are the most important traits of the North America region and that’s the reason most of the time the US dominates the global markets. Big Data Analytics in Tourism Market in South, America region is also expected to grow in near future. 

The report provides detailed profile and data information analysis of leading company.

  • Microsoft Corporation
  • MongoDB
  • United Technologies Corporation
  • JDA Software, Inc.
  • Software AG
  • Sensewaves
  • Avant
  • SAP
  • IBM Corp
  • Splunk
  • Oracle Corp.
  • Teradata Corp.
  • Amazon Web Services
  • Cloudera

    >>>To Understand How Covid-19 Impact Is Covered in This Report –https://www.absolutereports.com/enquiry/request-covid19/17086005

    We provide detailed product mapping and analysis of various market scenarios. Our analysts are experts in providing in-depth analysis and breakdown of the business of key market leaders. We keep a close eye on recent developments and follow latest company news related to different players operating in the global Big Data Analytics in Tourism market. This helps us to deeply analyze companies as well as the competitive landscape. Our vendor landscape analysis offers a complete study that will help you to stay on top of the competition.

    Big Data Analytics in Tourism Market Segment by Product Type:

  • Cloud-based
  • On-premise

    Big Data Analytics in Tourism Market Segment by Application:

  • Small and Medium-Sized Enterprises
  • Large Enterprises

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    Research Methodology

    * Data triangulation and market breakdown

    * Research assumptions Research data including primary and secondary data

    * Primary data includes breakdown of primaries and key industry insights

    * Secondary data includes key data from secondary sources

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    Table of Contents

    Report Overview: It includes major players of the Global Big Data Analytics in Tourism Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

    Global Growth Trends: This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the Global Big Data Analytics in Tourism Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the Global Big Data Analytics in Tourism Market are discussed.

    Big Data Analytics in Tourism Market Share by Manufacturers: Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

    Big Data Analytics in Tourism Market Size by Type: This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

    Big Data Analytics in Tourism Market Size by Application: Besides an overview of the Global Big Data Analytics in Tourism Market by application, it gives a study on the consumption in the Global Big Data Analytics in Tourism Market by application.

    Production by Region: Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

    Consumption by Region: This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

    Company Profiles: Almost all leading players of the Global Big Data Analytics in Tourism Market are profiled in this section. The analysts have provided information about their recent developments in the Global Big Data Analytics in Tourism Market, products, revenue, production, business, and company.

    Big Data Analytics in Tourism Market Forecast by Production: The production and production value forecasts included in this section are for the Global Big Data Analytics in Tourism Market as well as for key regional markets.

    Market Forecast by Consumption: The consumption and consumption value forecasts included in this section are for the Global Big Data Analytics in Tourism Market as well as for key regional markets.

    Value Chain and Sales Analysis: It deeply analyzes customers, distributors, sales channels, and value chain of the Global Big Data Analytics in Tourism Market.

    Key Findings: This section gives a quick look at important findings of the research study.

    For Detailed TOC –https://www.absolutereports.com/TOC/17086005#TOC

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    7 Key Advantage of Using Blockchain for Banking Software Development

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    Article originally posted on Data Science Central. Visit Data Science Central

    Did you know!

    • Worldwide spending on blockchain solutions is expected to cross 15.9 billion dollars by 2023.
    • 90% of U.S. and European banks are exploring blockchain solutions to stay ahead of the game.
    • To date, financial institutions alone have spent $552+ million on blockchain-based development projects.

    And we can go on and on with these insightful stats relating blockchain with banking and financial institutions, as per Fortunately.

    Since getting conceptualized by Satoshi Nakamoto in 2008 in bitcoin cryptocurrency form, blockchain has witnessed remarkable new and innovative applications in software development.

    The fintech industry always looks for technology tools that enhance security, and blockchain has emerged as a viable solution. The technology is getting used in diverse ways by banks and other financial institutions to ensure the highest level of privacy and protection.

    The infographic below illustrates the blockchain investors’ industry focus for the year 2019.

    Source: Statista 

                                                                   

    With the passing years, the number of deals has increased significantly as diverse industry verticals are exploring the technology. Investment from the banking sector is also rising owing to multiple benefits from the technology.

    Blockchain offers a lot more than high-security standards to the financial institution. Have a quick look at the infographic below that lists the top benefits of blockchain for banks and financial institutions.

    Before you proceed to learn them in detail, let’s recollect some basic concepts.

    So, do you know what exactly defines blockchain?

    In simple as ABC, blockchain is a kind of distributed ledger technology that records data in a secured manner with zero possibility of data altercation. Being a distributed technology, it has the following extraordinary features:

     

    • Each node of the network keeps the ledger account.
    • The data stored is immutable, which means it cannot get modified by a user.
    • Every transaction bears a time stamp.
    • The data record is encrypted.
    • It is a programmable technology.

    Main types of blockchain used by the banking industry

    The blockchain can be classified into many kinds, but for the sake of lucidity, we will stick to four main types of blockchain:

     

    Public Blockchain: A non-restrictive, permission-less distributed ledger system is referred to as the public blockchain. A public blockchain is open to all, and anyone can become an authorized user for accessing past and current records. 

    Best examples of Public Blockchain include the digital currency of bitcoin, Litecoin, etc.

     

    Private Blockchain: A restrictive and permission-based blockchain is called a private blockchain. It is meant for personal use in enterprises, and the level of scalability, accessibility, gets controlled by the administrative department.

    Best examples of Private Blockchain includes Fabric, Corda, etc. 

     

    Consortium Blockchain: A blockchain similar to private blockchain but gets utilized by multiple enterprises is called a consortium blockchain. It allows users from multiple enterprises thus actively used by banks, government organizations, etc.

    Best examples of Consortium Blockchain includes R3, Energy web foundation, etc.

     

    Hybrid Blockchain: As the name suggests, a hybrid blockchain is a mix between Private Blockchain and Public Blockchain. It allows users to go for both permission-based and permission-less features. The organization can control whether to let a particular transaction go for public or private use.

    The best example of Hybrid Blockchain is Dragonchain.

     

    Here are the top 7 benefits of blockchain solutions in banking software development

    1. Reduces Running Cost

    Blockchain effectively builds trust between the bank and its trading partner (whether a client or another bank). The high trust between the partners conducting financial transactions removes the necessity of mediators and third-party software otherwise required in the absence of blockchain.

    The immutable version of the transactions minimizes the corruption level, thus boosting the confidence level among the users.

     

    2. Lightning Speed Transactions

    The technology is responsible for effectively reducing the transaction time. It has been possible because it cuts and eliminates multiple intermediaries out of the process.

    The result is a simplified transaction with little to zero intermediaries. Also, the trades are conducted with ledger entries which facilitate banks to instantly authorize and permit processes with the least time gap.

    3. High-Security Standards

    Lightning-speed transactions of blockchain significantly reduce the time span for hackers to divert or hack them. It also allows zero modification powers to the concerned parties, enhancing the transparency level for the users.

    It has become feasible because blockchain stores data in a decentralized and encrypted manner over the entire network. It means as soon as the data gets stored over the network, a hacker cannot conduct any alteration to it. 

    Any data altercation invalidates the signature, which enhances the security level.

    4. Smart contracts improving data handling

    Banks and other financial institutions hire app developers for developing smart contracts using blockchain. The technology allows the development team to ensure automatic data verification and quick execution of commands and processes.

    It improves the data handling capacity of the developed software with high security and minimum human interference.

     

    5. Offers High Accountability

    Every transaction conducted online gets duplicated over the entire network. It automatically eliminates the risk of losing transaction details or loss of data. The user can conveniently trace any executed transaction.

    So, banks find it very easy to trace and deal with any issues occurring with transactions. Finding the culprit becomes a matter of clicks in such a scenario.

     

    6. Regarded as the future of banking software

    As per AI development stats, 20+ nations worldwide have researched for developing a national cryptocurrency. With multiple countries formally and informally approving bitcoin trade, digital currencies are leading significant impacts in trade and commerce.

    Blockchain is regarded among the most disruptive technologies and has become an integral part of banking software development worldwide. 

     

     7. Improves Efficiency 

    As per CEO of IBM, Ginni Rometty, “Anything that can conceive of as a supply chain, blockchain can vastly improve its efficiency- it doesn’t matter if its people, numbers, data, money.”

    Convenient tracking of fraudulence, quicker transactions, heavy security, etc., all together aids in developing a positive work culture environment for the bank employees. 

    Blockchain improves the efficiency and reliability of the developed software and acts as a positive energy booster for the bank employees.

     

    Final Words

    That was about the top 7 benefits of using blockchain for banking software. For developing advanced banking apps, hire blockchain developers from India at an affordable hourly rate.

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    Global Big Data Market Analysis highlights the impact of Covid-19 (2020-2025) | Top Players like …

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    The Global Big Data Market research report covers various industry dynamics and tendencies that helps in the market growth. Along with this, the research report also offers the latest industry data, future trends, products and end-users revenue growth and effectiveness, which is also by the manufacturers for the growth of the global Big Data market. Moreover, this market research report study also provides various market drivers, restrains, future opportunities, limitations and challenges that helps in the growth of the global Big Data market. The data or information that is required for the research report study of the Big Data market is collected with helps of various research tools like SWOT analysis, PESTAL analysis, Poster’s Five Forces analysis and other competitive analysis.

    Pandemic offer for our customers: Purchase this Report now by availing up to 10% Discount and free consultation. Limited period offer.

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    The comprehensive list of Key Market Players along with their market overview, product protocol, key highlights, key financial issues, SWOT analysis, and business strategies: FCS Computer Systems, Qlik, IBM, Phocas Software, Cyfe, MongoDB, Altair, Elastic, MicroStrategy, Margasoft, SAP, Artelnics, Informatica, Radius, Teradata, Hitachi Vantara, IQLECT

    In addition to this, the marker research report also comprises of recent market strategies that are used by the key market players and also provides the survey on the present market development and technological evolutions. This survey includes detailed analysis of the global competitive industrial structure and the information about the current and future technological advancements and development. Also, the survey includes the opportunities and encounters that are faced by the major players of the global Big Data market. Hence, this data will extensively help the manufacturers to include and update various business plans and strategies that will help in the growth of the Big Data market.

    Furthermore, the Big Data market report also provides major strategic examination, growth summarized studies, key driving factors and opportunities of the market, which helps to evaluate the Big Data market and other significant details that are related to the Big Data market. The research report study also helps to reveal accurate stats of the industry, which represents ultimate pattern of the global Big Data market and contains various types, applications, market growth structure and opportunities. Moreover, the market research report study also provides an exploration and analysis of the past and current performance of the regional market, which includes divisional and sub-divisional geographies. This regional analysis explores various important market parameters like growth rate of the Big Data market in each of the regions, manufacturing volume and capacity, market demand and supply and its return on investments (RoI).

    Additionally, the statistical and numerical data provided in the research report is segregated in the tabular, graphical and charts format, which eases the understanding of facts and figures. The Big Data market research report provides forecasted data from the year 2020-2027 and historical data from the year 2015-2019, by considering 2019 as the base year. This study also discusses market share estimates, market size, current industry trends and profiling of Big Data market key players.

    Report Attribute Details
    The market size value in 2019 USD xx.xx million (click here for value)
    The revenue forecast in 2028 USD xx.xx million (click here for value)
    Growth Rate CAGR of xx.xx% from 2020 to 2028 (click here for value)
    The base year for estimation 2019
    Historical data 2015 – 2018
    Forecast period 2019 – 2028
    Quantitative units Revenue in USD million and CAGR from 2020 to 2028
    Report coverage Revenue forecast, company ranking, competitive landscape, growth factors, and trends
    Segments covered Component, Types, Applications, End-Users, and more.
    Top Manufacturers FCS Computer Systems, Qlik, IBM, Phocas Software, Cyfe, MongoDB, Altair, Elastic, MicroStrategy, Margasoft, SAP, Artelnics, Informatica, Radius, Teradata, Hitachi Vantara, IQLECT
    Product Types On-premises, Cloud-Based
    Application Types Small and Medium Enterprises (SMEs), Large Enterprises
    Regional Scope North America, Europe, Asia Pacific, Latin America, Middle East and Africa
    Customization scope Free report customization (equivalent up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope.
    Pricing and purchase options Avail of customized purchase options to meet your exact research needs. Explore purchase options

    Type Segmentation of the Global Big Data Market:

    The type analysis of the Big Data market provides comprehensive information about its competitors, their activities, customer experiences and market emerging trends, which helps the marketers to introduce new product in the market and to investigate the behavior of the target market.

    • On-premises
    • Cloud-Based

    Application Segmentation of the Global Big Data Market:

    The application analysis of the Big Data market provides an overview that how the applications interact with the desired functions in the market.

    • Small and Medium Enterprises (SMEs)
    • Large Enterprises

    Regional Analysis for Global Big Data Market:

    • North America (U.S., Canada, Mexico)
    • Europe (Germany, U.K., France, Italy, Russia, Spain, and Rest of Europe)
    • Asia Pacific (China, Japan, India, Russia, and Rest of Asia Pacific)
    • Latin America (Cuba, Brazil, Argentina, and Rest of Latin America)
    • Middle East & Africa (South Africa, GCC and Rest of the Middle East & Africa)

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    Major points that are covered in the Big Data market report are:

    • The report offers competitive landscape and various market strategies of the key market players and their product offerings
    • The report provides historic data up to 2019, and forecast data from 2020 to 2027 for the global Big Data market.
    • SWOT analysis for all the key players that are mentioned in the research report
    • The report also covers PESTAL analysis and Potter’s Five Forces analysis for the global Big Data market.
    • The report also provides detail information about the key manufacturers, Big Data manufacturing cost structure, and major raw materials suppliers.
    • The report covers detailed overview of the Covid-19 impact on the global Big Data market
    • Detailed information about drivers, opportunities, and restraints of the Big Data market
    • The report provides a competitive analysis regarding Big Data market and key product segments of a market

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    We at Zeal Insider aim to be global leaders in qualitative and predictive analysis as we put ourselves in the front seat for identifying worldwide industrial trends and opportunities and mapping them out for you on a silver platter. We specialize in identifying the calibers of the market’s robust activities and constantly pushing out the areas which allow our clientele base in making the most innovative, optimized, integrated and strategic business decisions in order to put them ahead of their competition by leaps and bounds. Our researchers achieve this mammoth of a task by conducting sound research through many data points scattered through carefully placed equatorial regions.

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