.
Attention all professionals and businesses!
Are you tired of scouring countless sources for Data Streaming in Data integration knowledge? Look no further.
Our Data Integration Knowledge Base is here to simplify your search and provide you with all the necessary information in one convenient dataset.
Our dataset contains the most important questions to ask, prioritized by urgency and scope, to get the best results for your business.
With 1583 carefully researched Data Streaming in Data integration requirements, solutions, and benefits, you will have all the tools you need to successfully integrate data streams into your operations.
What sets our Data Integration Knowledge Base apart from competitors and alternatives is its comprehensive coverage and user-friendly format.
We understand the complexity of data integration and have curated our dataset to make it easily accessible and understandable for professionals like you.
Not only does our dataset save you valuable time and effort, but it also helps you make informed decisions for your business.
Imagine having all the necessary information at your fingertips, allowing you to select the best Data Streaming in Data integration solutions for your specific needs.
But don′t just take our word for it, see for yourself the power of our Data Integration Knowledge Base through our example case studies and use cases.
Our dataset covers a wide range of industries and use cases, making it a versatile tool for any business.
In addition to its effectiveness, our Data Integration Knowledge Base is affordable and easy to use.
No matter your budget or expertise level, our dataset is designed to be accessible to everyone.
Say goodbye to expensive consulting services and complicated software, and hello to our DIY solution.
Our product detail and specification overview ensures that you have all the necessary information on hand before making a decision.
You can compare our product type to semi-related products, and see the clear benefits of choosing our Data Integration Knowledge Base.
With our dataset, you can stay ahead of the curve by keeping up with the latest research and developments in Data Streaming in Data integration.
Our dataset is regularly updated to provide you with the most current and relevant information.
Don′t let data integration hinder your business growth.
Invest in our Data Integration Knowledge Base and see the positive impact it has on your operations.
Our dataset is a game-changer for businesses of all sizes and industries.
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We understand the importance of efficiency and cost-saving measures for businesses, and our Data Integration Knowledge Base reflects that.
As with any product, there are pros and cons to consider.
But we are confident that the pros of our Data Integration Knowledge Base outweigh any cons.
With its comprehensive coverage, user-friendly format, and affordability, our dataset is a must-have for any business seeking to incorporate data streaming into their operations.
In summary, our Data Integration Knowledge Base is a one-stop-shop for all your data streaming needs.
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Is a user manual available to data management personnel for all software currently in use? Do statistical or machine learning processes need to be applied to data whilst its streaming? Are groups supplying data provided an opportunity to review data and correct erroneous entries?
Data Streaming
Yes, a comprehensive user manual is available for all software currently being used by data management personnel.
1. Use of unified data platform: Centralized data streaming tools provide a single platform to manage and integrate data from different sources.
2. Real-time data processing: Data streaming enables the processing of data in real-time, providing up-to-date and accurate insights.
3. ETL Tools: Extract, Transform, and Load (ETL) tools can be used to integrate data from various sources using a common data format.
4. Data Quality and Governance: Data streaming helps to maintain data quality and ensure governance by automatically filtering and validating data.
5. Cloud-based Solutions: Cloud-based data streaming solutions offer scalability, flexibility, and cost-efficiency for organizations of all sizes.
6. Automated Data Integration: Automated data integration allows for faster and more efficient data integration processes, saving time and resources.
7. Machine Learning: Data streaming with machine learning algorithms can detect patterns and anomalies in data, enhancing data integration accuracy.
8. API Integration: Application Programming Interface (API) integration allows for seamless connectivity between different applications, facilitating data exchange.
9. Real-Time Analytics: Data streaming enables real-time analytics, allowing organizations to make data-driven decisions quickly and efficiently.
10. Improved Business Insights: With data streaming, organizations can gain a comprehensive view of their data, leading to improved business insights and decision-making.
CONTROL QUESTION: Is a user manual available to data management personnel for all software currently in use?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
The big hairy audacious goal for Data Streaming for 10 years from now is to have a fully automated data management system in place that streamlines the processes of collecting, organizing, and analyzing large amounts of data in real-time. This system will be equipped with advanced machine learning and artificial intelligence capabilities to not only efficiently process data but also make intelligent recommendations and predictions for future data management strategies.
As part of this system, there will be a centralized database that houses all the software currently in use within the organization. This database will be regularly updated and easily accessible to data management personnel who can refer to it for any queries related to software usage. The ultimate goal is to create a user manual that contains comprehensive information on all software used for data streaming in the organization.
The user manual will include detailed descriptions of the software′s functionality, instructions on how to use it, troubleshooting tips, and best practices for maximizing its potential. It will also have a section dedicated to data security protocols and compliance requirements for each software. This manual will be constantly updated and accessible online for easy access and reference by data management personnel.
By having a user manual available for all the software used in data streaming, the organization will ensure that its data management processes are streamlined, efficient, and compliant. It will also pave the way for continuous improvement and innovation in data streaming techniques, leading to better decision-making and business outcomes.
"I can`t speak highly enough of this dataset. The prioritized recommendations have transformed the way I approach projects, making it easier to identify key actions. A must-have for data enthusiasts!"
Client Situation:
A multinational organization in the financial services industry is facing challenges with data management and governance across its various business units. The company has a large and complex IT infrastructure including multiple data centers, cloud-based applications, and legacy systems. Due to the ever-increasing volume and variety of data coming in from different sources, the company struggled to effectively manage and utilize the data to make informed business decisions.
The company identified the need to implement a data streaming solution to process and analyze real-time data, as well as to efficiently handle large volumes of data in motion. The goal was to improve overall data management capabilities and support data-driven decision-making.
Consulting Methodology:
To address the client′s challenges, the consulting team adopted a structured approach that involved defining the client′s requirements, selecting the appropriate data streaming platform, and implementing the solution. The methodology consisted of the following steps:
1. Understanding the client′s current data management processes and challenges: The consulting team conducted interviews and workshops with key stakeholders to gain a comprehensive understanding of the client′s existing workflows, pain points, and future goals.
2. Defining requirements for the data streaming solution: Based on the information gathered, the team identified the client′s specific data streaming needs, such as real-time data processing, scalability, fault tolerance, and integration capabilities.
3. Selecting the right data streaming platform: The team analyzed various data streaming platforms available in the market, considering factors such as cost, functionality, and scalability. They recommended a comprehensive streaming platform that met the client′s requirements.
4. Implementing the solution: The team worked closely with the client′s IT team to implement the data streaming solution, integrating it with existing systems and processes. They also provided training and support to ensure a smooth adoption of the new technology.
5. Continuous monitoring and optimization: The consulting team assisted the client in monitoring the performance of the data streaming solution and provided recommendations for optimization to ensure efficient data management.
Deliverables:
1. Detailed analysis of client′s current data management processes and challenges
2. Requirements document for the data streaming solution
3. Detailed comparison of data streaming platforms and recommendations
4. Implementation plan for the selected data streaming solution
5. Training materials and support for IT team and end-users
6. Monitoring and optimization reports.
Implementation Challenges:
The implementation of the data streaming solution posed several challenges, including:
1. Resistance to change: Since the company had been using traditional batch processing methods for a long time, there was initial resistance to adopt a real-time data streaming solution.
2. Integration with legacy systems: The client′s legacy systems were not designed to handle real-time data processing, making it challenging to integrate them with the new streaming platform.
3. Data security concerns: As a financial services company, the client had strict data security requirements, and the new data streaming solution had to ensure compliance with these regulations.
Key Performance Indicators (KPIs):
The success of the data streaming project was evaluated based on a set of predefined KPIs, which included:
1. Improved data processing speed: The data streaming solution was expected to process large volumes of data in real-time, reducing the time taken for data analysis and decision-making.
2. Increased data accuracy and quality: The new solution was expected to improve data accuracy and provide better data quality, enabling more reliable insights.
3. Cost savings: The client anticipated cost savings by replacing manual data processing methods with automated data streaming.
4. Improved data governance: The data streaming solution was expected to streamline data management processes and improve data governance across the organization.
Management Considerations:
Effective data management is crucial for any organization, and the implementation of a data streaming solution can bring many benefits. However, before embarking on such a project, it is essential to consider some management considerations, including:
1. Investment in technology: Implementing a data streaming solution requires a significant investment in technology, including hardware, software, and training.
2. Skilled resources: Data streaming is a relatively new technology, and there may be a shortage of skilled resources to implement and maintain it. It is crucial to have a team of experts to ensure the success of the project.
3. Cultural change: The adoption of a new technology also requires a cultural shift, and it is essential to have buy-in from all levels of the organization for successful implementation.
Conclusion:
The implementation of a data streaming solution for the financial services company successfully addressed their data management challenges. The new solution provided real-time data processing, improved data accuracy, and enabled more efficient decision-making. The consulting team′s structured approach and continuous support helped the client adopt the new technology seamlessly. The successful implementation of the data streaming solution resulted in better data governance and governance, enabling the client to gain a competitive edge in the industry.
Citations:
1. Data Streaming: A Comprehensive Guide to Managing Data in Real-Time. Imply Data, inc., 2021, https://imply.io/blog/data-streaming-guide-managing-data-real-time. Accessed 16 June 2021.
2. Mohammadi, Ali, et al. Real-time Data Streaming: Opportunities, Challenges, And Solutions. Business Intelligence Journal, vol 11, no. 1, 2006, pp. 10-22, ProQuest, https://www.proquest.com/scholarly-journals/real-time-data-streaming-opportunities-challenges/docview/2405253654/se-2?accountid=14902.
3. Global Real-Time Data Streaming Market - Growth, Trends, COVID-19 Impact, and Forecasts (2020-2025). Mordor Intelligence, 2021, https://www.mordorintelligence.com/industry-reports/real-time-data-streaming-market. Accessed 16 June 2021.
Attention all professionals and businesses!
Are you tired of scouring countless sources for Data Streaming in Data integration knowledge? Look no further.
Our Data Integration Knowledge Base is here to simplify your search and provide you with all the necessary information in one convenient dataset.
Our dataset contains the most important questions to ask, prioritized by urgency and scope, to get the best results for your business.
With 1583 carefully researched Data Streaming in Data integration requirements, solutions, and benefits, you will have all the tools you need to successfully integrate data streams into your operations.
What sets our Data Integration Knowledge Base apart from competitors and alternatives is its comprehensive coverage and user-friendly format.
We understand the complexity of data integration and have curated our dataset to make it easily accessible and understandable for professionals like you.
Not only does our dataset save you valuable time and effort, but it also helps you make informed decisions for your business.
Imagine having all the necessary information at your fingertips, allowing you to select the best Data Streaming in Data integration solutions for your specific needs.
But don′t just take our word for it, see for yourself the power of our Data Integration Knowledge Base through our example case studies and use cases.
Our dataset covers a wide range of industries and use cases, making it a versatile tool for any business.
In addition to its effectiveness, our Data Integration Knowledge Base is affordable and easy to use.
No matter your budget or expertise level, our dataset is designed to be accessible to everyone.
Say goodbye to expensive consulting services and complicated software, and hello to our DIY solution.
Our product detail and specification overview ensures that you have all the necessary information on hand before making a decision.
You can compare our product type to semi-related products, and see the clear benefits of choosing our Data Integration Knowledge Base.
With our dataset, you can stay ahead of the curve by keeping up with the latest research and developments in Data Streaming in Data integration.
Our dataset is regularly updated to provide you with the most current and relevant information.
Don′t let data integration hinder your business growth.
Invest in our Data Integration Knowledge Base and see the positive impact it has on your operations.
Our dataset is a game-changer for businesses of all sizes and industries.
But that′s not all, our dataset is also cost-effective, ensuring that you get the best value for your money.
We understand the importance of efficiency and cost-saving measures for businesses, and our Data Integration Knowledge Base reflects that.
As with any product, there are pros and cons to consider.
But we are confident that the pros of our Data Integration Knowledge Base outweigh any cons.
With its comprehensive coverage, user-friendly format, and affordability, our dataset is a must-have for any business seeking to incorporate data streaming into their operations.
In summary, our Data Integration Knowledge Base is a one-stop-shop for all your data streaming needs.
It provides you with the necessary information and tools to make informed decisions and stay ahead of the competition.
Don′t miss out on this valuable resource, purchase our Data Integration Knowledge Base today and take your business to new heights.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1583 prioritized Data Streaming requirements. - Extensive coverage of 238 Data Streaming topic scopes.
- In-depth analysis of 238 Data Streaming step-by-step solutions, benefits, BHAGs.
- Detailed examination of 238 Data Streaming case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Scope Changes, Key Capabilities, Big Data, POS Integrations, Customer Insights, Data Redundancy, Data Duplication, Data Independence, Ensuring Access, Integration Layer, Control System Integration, Data Stewardship Tools, Data Backup, Transparency Culture, Data Archiving, IPO Market, ESG Integration, Data Cleansing, Data Security Testing, Data Management Techniques, Task Implementation, Lead Forms, Data Blending, Data Aggregation, Data Integration Platform, Data generation, Performance Attainment, Functional Areas, Database Marketing, Data Protection, Heat Integration, Sustainability Integration, Data Orchestration, Competitor Strategy, Data Governance Tools, Data Integration Testing, Data Governance Framework, Service Integration, User Incentives, Email Integration, Paid Leave, Data Lineage, Data Integration Monitoring, Data Warehouse Automation, Data Analytics Tool Integration, Code Integration, platform subscription, Business Rules Decision Making, Big Data Integration, Data Migration Testing, Technology Strategies, Service Asset Management, Smart Data Management, Data Management Strategy, Systems Integration, Responsible Investing, Data Integration Architecture, Cloud Integration, Data Modeling Tools, Data Ingestion Tools, To Touch, Data Integration Optimization, Data Management, Data Fields, Efficiency Gains, Value Creation, Data Lineage Tracking, Data Standardization, Utilization Management, Data Lake Analytics, Data Integration Best Practices, Process Integration, Change Integration, Data Exchange, Audit Management, Data Sharding, Enterprise Data, Data Enrichment, Data Catalog, Data Transformation, Social Integration, Data Virtualization Tools, Customer Convenience, Software Upgrade, Data Monitoring, Data Visualization, Emergency Resources, Edge Computing Integration, Data Integrations, Centralized Data Management, Data Ownership, Expense Integrations, Streamlined Data, Asset Classification, Data Accuracy Integrity, Emerging Technologies, Lessons Implementation, Data Management System Implementation, Career Progression, Asset Integration, Data Reconciling, Data Tracing, Software Implementation, Data Validation, Data Movement, Lead Distribution, Data Mapping, Managing Capacity, Data Integration Services, Integration Strategies, Compliance Cost, Data Cataloging, System Malfunction, Leveraging Information, Data Data Governance Implementation Plan, Flexible Capacity, Talent Development, Customer Preferences Analysis, IoT Integration, Bulk Collect, Integration Complexity, Real Time Integration, Metadata Management, MDM Metadata, Challenge Assumptions, Custom Workflows, Data Governance Audit, External Data Integration, Data Ingestion, Data Profiling, Data Management Systems, Common Focus, Vendor Accountability, Artificial Intelligence Integration, Data Management Implementation Plan, Data Matching, Data Monetization, Value Integration, MDM Data Integration, Recruiting Data, Compliance Integration, Data Integration Challenges, Customer satisfaction analysis, Data Quality Assessment Tools, Data Governance, Integration Of Hardware And Software, API Integration, Data Quality Tools, Data Consistency, Investment Decisions, Data Synchronization, Data Virtualization, Performance Upgrade, Data Streaming, Data Federation, Data Virtualization Solutions, Data Preparation, Data Flow, Master Data, Data Sharing, data-driven approaches, Data Merging, Data Integration Metrics, Data Ingestion Framework, Lead Sources, Mobile Device Integration, Data Legislation, Data Integration Framework, Data Masking, Data Extraction, Data Integration Layer, Data Consolidation, State Maintenance, Data Migration Data Integration, Data Inventory, Data Profiling Tools, ESG Factors, Data Compression, Data Cleaning, Integration Challenges, Data Replication Tools, Data Quality, Edge Analytics, Data Architecture, Data Integration Automation, Scalability Challenges, Integration Flexibility, Data Cleansing Tools, ETL Integration, Rule Granularity, Media Platforms, Data Migration Process, Data Integration Strategy, ESG Reporting, EA Integration Patterns, Data Integration Patterns, Data Ecosystem, Sensor integration, Physical Assets, Data Mashups, Engagement Strategy, Collections Software Integration, Data Management Platform, Efficient Distribution, Environmental Design, Data Security, Data Curation, Data Transformation Tools, Social Media Integration, Application Integration, Machine Learning Integration, Operational Efficiency, Marketing Initiatives, Cost Variance, Data Integration Data Manipulation, Multiple Data Sources, Valuation Model, ERP Requirements Provide, Data Warehouse, Data Storage, Impact Focused, Data Replication, Data Harmonization, Master Data Management, AI Integration, Data integration, Data Warehousing, Talent Analytics, Data Migration Planning, Data Lake Management, Data Privacy, Data Integration Solutions, Data Quality Assessment, Data Hubs, Cultural Integration, ETL Tools, Integration with Legacy Systems, Data Security Standards
Data Streaming Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Data Streaming
Yes, a comprehensive user manual is available for all software currently being used by data management personnel.
1. Use of unified data platform: Centralized data streaming tools provide a single platform to manage and integrate data from different sources.
2. Real-time data processing: Data streaming enables the processing of data in real-time, providing up-to-date and accurate insights.
3. ETL Tools: Extract, Transform, and Load (ETL) tools can be used to integrate data from various sources using a common data format.
4. Data Quality and Governance: Data streaming helps to maintain data quality and ensure governance by automatically filtering and validating data.
5. Cloud-based Solutions: Cloud-based data streaming solutions offer scalability, flexibility, and cost-efficiency for organizations of all sizes.
6. Automated Data Integration: Automated data integration allows for faster and more efficient data integration processes, saving time and resources.
7. Machine Learning: Data streaming with machine learning algorithms can detect patterns and anomalies in data, enhancing data integration accuracy.
8. API Integration: Application Programming Interface (API) integration allows for seamless connectivity between different applications, facilitating data exchange.
9. Real-Time Analytics: Data streaming enables real-time analytics, allowing organizations to make data-driven decisions quickly and efficiently.
10. Improved Business Insights: With data streaming, organizations can gain a comprehensive view of their data, leading to improved business insights and decision-making.
CONTROL QUESTION: Is a user manual available to data management personnel for all software currently in use?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
The big hairy audacious goal for Data Streaming for 10 years from now is to have a fully automated data management system in place that streamlines the processes of collecting, organizing, and analyzing large amounts of data in real-time. This system will be equipped with advanced machine learning and artificial intelligence capabilities to not only efficiently process data but also make intelligent recommendations and predictions for future data management strategies.
As part of this system, there will be a centralized database that houses all the software currently in use within the organization. This database will be regularly updated and easily accessible to data management personnel who can refer to it for any queries related to software usage. The ultimate goal is to create a user manual that contains comprehensive information on all software used for data streaming in the organization.
The user manual will include detailed descriptions of the software′s functionality, instructions on how to use it, troubleshooting tips, and best practices for maximizing its potential. It will also have a section dedicated to data security protocols and compliance requirements for each software. This manual will be constantly updated and accessible online for easy access and reference by data management personnel.
By having a user manual available for all the software used in data streaming, the organization will ensure that its data management processes are streamlined, efficient, and compliant. It will also pave the way for continuous improvement and innovation in data streaming techniques, leading to better decision-making and business outcomes.
Customer Testimonials:
"I can`t speak highly enough of this dataset. The prioritized recommendations have transformed the way I approach projects, making it easier to identify key actions. A must-have for data enthusiasts!"
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Data Streaming Case Study/Use Case example - How to use:
Client Situation:
A multinational organization in the financial services industry is facing challenges with data management and governance across its various business units. The company has a large and complex IT infrastructure including multiple data centers, cloud-based applications, and legacy systems. Due to the ever-increasing volume and variety of data coming in from different sources, the company struggled to effectively manage and utilize the data to make informed business decisions.
The company identified the need to implement a data streaming solution to process and analyze real-time data, as well as to efficiently handle large volumes of data in motion. The goal was to improve overall data management capabilities and support data-driven decision-making.
Consulting Methodology:
To address the client′s challenges, the consulting team adopted a structured approach that involved defining the client′s requirements, selecting the appropriate data streaming platform, and implementing the solution. The methodology consisted of the following steps:
1. Understanding the client′s current data management processes and challenges: The consulting team conducted interviews and workshops with key stakeholders to gain a comprehensive understanding of the client′s existing workflows, pain points, and future goals.
2. Defining requirements for the data streaming solution: Based on the information gathered, the team identified the client′s specific data streaming needs, such as real-time data processing, scalability, fault tolerance, and integration capabilities.
3. Selecting the right data streaming platform: The team analyzed various data streaming platforms available in the market, considering factors such as cost, functionality, and scalability. They recommended a comprehensive streaming platform that met the client′s requirements.
4. Implementing the solution: The team worked closely with the client′s IT team to implement the data streaming solution, integrating it with existing systems and processes. They also provided training and support to ensure a smooth adoption of the new technology.
5. Continuous monitoring and optimization: The consulting team assisted the client in monitoring the performance of the data streaming solution and provided recommendations for optimization to ensure efficient data management.
Deliverables:
1. Detailed analysis of client′s current data management processes and challenges
2. Requirements document for the data streaming solution
3. Detailed comparison of data streaming platforms and recommendations
4. Implementation plan for the selected data streaming solution
5. Training materials and support for IT team and end-users
6. Monitoring and optimization reports.
Implementation Challenges:
The implementation of the data streaming solution posed several challenges, including:
1. Resistance to change: Since the company had been using traditional batch processing methods for a long time, there was initial resistance to adopt a real-time data streaming solution.
2. Integration with legacy systems: The client′s legacy systems were not designed to handle real-time data processing, making it challenging to integrate them with the new streaming platform.
3. Data security concerns: As a financial services company, the client had strict data security requirements, and the new data streaming solution had to ensure compliance with these regulations.
Key Performance Indicators (KPIs):
The success of the data streaming project was evaluated based on a set of predefined KPIs, which included:
1. Improved data processing speed: The data streaming solution was expected to process large volumes of data in real-time, reducing the time taken for data analysis and decision-making.
2. Increased data accuracy and quality: The new solution was expected to improve data accuracy and provide better data quality, enabling more reliable insights.
3. Cost savings: The client anticipated cost savings by replacing manual data processing methods with automated data streaming.
4. Improved data governance: The data streaming solution was expected to streamline data management processes and improve data governance across the organization.
Management Considerations:
Effective data management is crucial for any organization, and the implementation of a data streaming solution can bring many benefits. However, before embarking on such a project, it is essential to consider some management considerations, including:
1. Investment in technology: Implementing a data streaming solution requires a significant investment in technology, including hardware, software, and training.
2. Skilled resources: Data streaming is a relatively new technology, and there may be a shortage of skilled resources to implement and maintain it. It is crucial to have a team of experts to ensure the success of the project.
3. Cultural change: The adoption of a new technology also requires a cultural shift, and it is essential to have buy-in from all levels of the organization for successful implementation.
Conclusion:
The implementation of a data streaming solution for the financial services company successfully addressed their data management challenges. The new solution provided real-time data processing, improved data accuracy, and enabled more efficient decision-making. The consulting team′s structured approach and continuous support helped the client adopt the new technology seamlessly. The successful implementation of the data streaming solution resulted in better data governance and governance, enabling the client to gain a competitive edge in the industry.
Citations:
1. Data Streaming: A Comprehensive Guide to Managing Data in Real-Time. Imply Data, inc., 2021, https://imply.io/blog/data-streaming-guide-managing-data-real-time. Accessed 16 June 2021.
2. Mohammadi, Ali, et al. Real-time Data Streaming: Opportunities, Challenges, And Solutions. Business Intelligence Journal, vol 11, no. 1, 2006, pp. 10-22, ProQuest, https://www.proquest.com/scholarly-journals/real-time-data-streaming-opportunities-challenges/docview/2405253654/se-2?accountid=14902.
3. Global Real-Time Data Streaming Market - Growth, Trends, COVID-19 Impact, and Forecasts (2020-2025). Mordor Intelligence, 2021, https://www.mordorintelligence.com/industry-reports/real-time-data-streaming-market. Accessed 16 June 2021.
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