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Are you tired of sifting through endless resources and questions to find the solutions you need for your database sharding needs? Look no further, because our Database Sharding in Software Development Knowledge Base has you covered.
Our comprehensive dataset consists of 1598 prioritized requirements, solutions, benefits, results, and real-life case studies/use cases for Database Sharding in Software Development.
We have carefully compiled the most important questions that are crucial for getting results with urgency and scope in mind.
But what sets our Database Sharding in Software Development dataset apart from competitors and alternatives? Our product is specifically designed for professionals like you, with a user-friendly interface and in-depth research on the subject matter.
The dataset provides a detailed overview of the product specifications and types, making it easy to compare with semi-related products.
We understand that staying within budget is important, which is why we offer an affordable, DIY alternative for Database Sharding in Software Development.
With our dataset, you have all the information and guidance you need at your fingertips to confidently handle the process yourself.
But don′t just take our word for it, our dataset has been tested and proven to deliver tangible benefits.
Say goodbye to wasting time and resources on trial and error, and hello to efficient and effective results with Database Sharding in Software Development.
Our Dataset is not just for individual use, businesses can also benefit greatly from it.
Cut down on costs and streamline your database sharding process with our dataset.
It′s a win-win situation for everyone involved.
We understand that making a decision to invest in a product can be daunting, which is why we are here to reassure you.
Our dataset is cost-effective and comes with a clear list of pros and cons to help you make an informed choice.
So, what does this Database Sharding in Software Development dataset do exactly? In simple terms, it provides you with all the necessary tools and information to properly implement database sharding in your software development.
It eliminates the guesswork and helps you achieve optimal results with minimum effort.
Don′t waste any more time trying to piece together scattered information.
Invest in our Database Sharding in Software Development Knowledge Base and see the difference it can make for yourself.
Take control of your database sharding process and elevate your software development game with our dataset.
Try it now and experience the convenience, efficiency, and benefits first-hand!
Which archive/repository/central database/ data center have you identified as a place to deposit data? What do you Do If the Network Connection Between the Replication Instance and Database Is Abnormal? What is the effect on the application of another client modifying the database as you try to read it?
Database Sharding
Database sharding is the process of dividing a large database into smaller, more manageable parts that are stored on different servers or data centers. This is done in order to improve performance and scalability.
1. Create a sharding strategy to distribute data efficiently among multiple databases.
2. Reduce database load and increase scalability.
3. Improved performance as data is spread across multiple servers.
4. Increased fault tolerance in case of server failure.
5. Automated data distribution for efficient resource utilization.
CONTROL QUESTION: Which archive/repository/central database/ data center have you identified as a place to deposit data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 10 years, our goal for Database Sharding is to have established a state-of-the-art, highly secure and reliable data center specifically designed for storing and managing large-scale sharded databases. This data center will serve as a central repository for all our clients′ data, providing seamless access and efficient management of their sharded databases.
We envision this data center to be equipped with advanced technologies such as data encryption, multi-factor authentication, and real-time monitoring to ensure the utmost security of our clients′ data. It will also have a highly scalable infrastructure to accommodate the exponential growth of data and handle massive amounts of concurrent requests.
Furthermore, this data center will be located in a strategic and geographically diverse location to ensure data redundancy and minimize the risk of disruptions. It will also have high-speed connectivity to enable fast and reliable data transfer between our clients′ systems and the data center.
We aim to make this data center a go-to destination for organizations looking for a reliable and efficient solution for managing their sharded databases. With our commitment to constantly innovate and improve our services, we are confident that this data center will become an integral part of the global data storage infrastructure, providing unparalleled efficiency and scalability for Database Sharding in the next 10 years.
"I`ve been searching for a dataset that provides reliable prioritized recommendations, and I finally found it. The accuracy and depth of insights have exceeded my expectations. A must-have for professionals!"
Case Study: Database Sharding for Depositing Data in a Centralized Repository
Client Situation:
Our client, a large multinational corporation in the technology industry, was facing significant challenges with managing their large and growing volumes of data. With multiple product lines and services, each generating vast amounts of data, the company needed a robust data management solution to streamline its operations and ensure scalability. The existing centralized database was struggling to handle the increasing data load, resulting in performance issues and delays in data retrieval. Additionally, the company needed a more secure and efficient way to consolidate and analyze data from various sources for strategic decision-making. As a result, the client approached our consulting firm to design and implement a solution that would address these issues and improve overall data management.
Consulting Methodology:
After assessing the client′s situation and requirements, our consulting team recommended implementing a database sharding solution. Database sharding is a data management technique that partitions a database into smaller, more manageable segments called shards. It involves breaking down the database into smaller pieces and distributing them across multiple servers, improving performance, scalability, and security. Our approach included the following steps:
1. Needs Assessment: Our team started by conducting a detailed needs assessment to identify the client′s specific data management challenges and goals. This step involved interviewing key stakeholders, reviewing existing systems and processes, and benchmarking against industry best practices.
2. Design and Configuration: Based on the client′s requirements, our team designed a scalable and optimized sharding architecture for the central repository. This step involved determining the appropriate shard size, number of shards, and distribution strategy to ensure efficient data storage and retrieval.
3. Development and Implementation: Once the design was finalized, our team worked closely with the client′s IT department to set up the sharding solution on the existing infrastructure. This step included configuring the servers, creating shards, and migrating the data from the existing database.
4. Testing and Optimization: After implementation, our team conducted rigorous testing to ensure the sharding solution was functioning correctly and meeting the client′s performance expectations. Any issues or bottlenecks were identified and addressed, and further optimization was done to fine-tune the system.
5. Training and Support: As part of the project deliverables, we provided training to the client′s IT team on managing and maintaining the sharding solution. We also offered post-implementation support to assist with any issues that arose during the system′s day-to-day operations.
Deliverables:
The main deliverable of this project was a fully functional and optimized sharding solution for the client′s central database. Along with that, our team provided:
1. Detailed sharding architecture design and configuration documents
2. Sharding implementation and testing reports
3. Training materials and sessions for the client′s IT team
4. Post-implementation support for a specific period
5. Performance monitoring and reporting tools
Implementation Challenges:
While implementing the sharding solution, our consulting team encountered several challenges that needed careful consideration and solutions. Some of the key challenges included:
1. Existing Data Structure: The client′s data structure was complex and required significant restructuring to facilitate sharding.
2. Data Synchronization: As data was being distributed among different shards, ensuring that the records remained consistent posed a challenge.
3. Balancing Shards: It was crucial to ensure that the data was distributed evenly among the shards to prevent any single shard from becoming overloaded.
Key Performance Indicators (KPIs):
After implementing the sharding solution, we set out measurable KPIs to track its performance against the client′s objectives. Some of the essential KPIs included:
1. Data Retrieval Time: The primary goal of the sharding solution was to improve the speed of data retrieval from the central database. Therefore, we tracked the time taken to retrieve data from the sharded database compared to the old centralized database.
2. Data Consistency: One of the significant concerns with implementing sharding was maintaining data consistency across the shards. We measured this KPI by comparing the number of data discrepancies detected between the sharded and centralized databases.
3. Scalability: As the client′s business continued to grow, it was crucial to ensure that the sharding solution could handle the increasing volume of data. We tracked the growth rate of data and compared it against previous years to assess the system′s scalability.
Management Considerations:
The implementation of a sharding solution has several management implications that organizations must consider. Some of the key considerations include:
1. Ongoing Maintenance: Sharded databases require regular maintenance and monitoring to ensure their smooth operation, making it necessary for the organization to have dedicated IT resources to handle this.
2. Data Security: With data being distributed among multiple shards, it increases the risk of unauthorized access, making it crucial to have proper security measures in place.
3. Cost-Benefit Analysis: Introducing a sharding solution requires an initial investment in hardware, software, and training. Therefore, it is essential to perform a cost-benefit analysis, considering the potential savings from improved efficiency and performance.
Conclusion:
The implementation of a sharding solution for depositing data in a centralized repository proved to be a highly successful project for our client. With improved database performance, data accessibility, and scalability, the client has been able to streamline its operations and make data-driven decisions more efficiently. Moreover, the sharding solution has also laid the foundation for future growth and expansion, setting the client on a path towards sustainable success.
Citations:
1. K. Kaur, A Comprehensive Study of Database Sharding, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 8, no. 12, pp. 102-108, Dec. 2018.
2. J. Gray, The Benefits of Sharding, Microsoft Research, Mar. 2005.
3. J. Dhawan and D. Paul, Scalable Database Sharding: A Survey, ACM Computing Surveys, vol. 51, no. 4, Article 66, Jan. 2019.
4. M. Haase and I. Cox, Database Sharding: The Importance of Choosing the Right Tool, MongoDB Whitepaper, 2019.
5. G. Holbert, Database Sharding: Strategies for Managing Massively Scalable Databases, Forbes, Mar. 2016.
Are you tired of sifting through endless resources and questions to find the solutions you need for your database sharding needs? Look no further, because our Database Sharding in Software Development Knowledge Base has you covered.
Our comprehensive dataset consists of 1598 prioritized requirements, solutions, benefits, results, and real-life case studies/use cases for Database Sharding in Software Development.
We have carefully compiled the most important questions that are crucial for getting results with urgency and scope in mind.
But what sets our Database Sharding in Software Development dataset apart from competitors and alternatives? Our product is specifically designed for professionals like you, with a user-friendly interface and in-depth research on the subject matter.
The dataset provides a detailed overview of the product specifications and types, making it easy to compare with semi-related products.
We understand that staying within budget is important, which is why we offer an affordable, DIY alternative for Database Sharding in Software Development.
With our dataset, you have all the information and guidance you need at your fingertips to confidently handle the process yourself.
But don′t just take our word for it, our dataset has been tested and proven to deliver tangible benefits.
Say goodbye to wasting time and resources on trial and error, and hello to efficient and effective results with Database Sharding in Software Development.
Our Dataset is not just for individual use, businesses can also benefit greatly from it.
Cut down on costs and streamline your database sharding process with our dataset.
It′s a win-win situation for everyone involved.
We understand that making a decision to invest in a product can be daunting, which is why we are here to reassure you.
Our dataset is cost-effective and comes with a clear list of pros and cons to help you make an informed choice.
So, what does this Database Sharding in Software Development dataset do exactly? In simple terms, it provides you with all the necessary tools and information to properly implement database sharding in your software development.
It eliminates the guesswork and helps you achieve optimal results with minimum effort.
Don′t waste any more time trying to piece together scattered information.
Invest in our Database Sharding in Software Development Knowledge Base and see the difference it can make for yourself.
Take control of your database sharding process and elevate your software development game with our dataset.
Try it now and experience the convenience, efficiency, and benefits first-hand!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1598 prioritized Database Sharding requirements. - Extensive coverage of 349 Database Sharding topic scopes.
- In-depth analysis of 349 Database Sharding step-by-step solutions, benefits, BHAGs.
- Detailed examination of 349 Database Sharding 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.
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- Covering: Agile Software Development Quality Assurance, Exception Handling, Individual And Team Development, Order Tracking, Compliance Maturity Model, Customer Experience Metrics, Lessons Learned, Sprint Planning, Quality Assurance Standards, Agile Team Roles, Software Testing Frameworks, Backend Development, Identity Management, Software Contracts, Database Query Optimization, Service Discovery, Code Optimization, System Testing, Machine Learning Algorithms, Model-Based Testing, Big Data Platforms, Data Analytics Tools, Org Chart, Software retirement, Continuous Deployment, Cloud Cost Management, Software Security, Infrastructure Development, Machine Learning, Data Warehousing, AI Certification, Organizational Structure, Team Empowerment, Cost Optimization Strategies, Container Orchestration, Waterfall Methodology, Problem Investigation, Billing Analysis, Mobile App Development, Integration Challenges, Strategy Development, Cost Analysis, User Experience Design, Project Scope Management, Data Visualization Tools, CMMi Level 3, Code Reviews, Big Data Analytics, CMS Development, Market Share Growth, Agile Thinking, Commerce Development, Data Replication, Smart Devices, Kanban Practices, Shopping Cart Integration, API Design, Availability Management, Process Maturity Assessment, Code Quality, Software Project Estimation, Augmented Reality Applications, User Interface Prototyping, Web Services, Functional Programming, Native App Development, Change Evaluation, Memory Management, Product Experiment Results, Project Budgeting, File Naming Conventions, Stakeholder Trust, Authorization Techniques, Code Collaboration Tools, Root Cause Analysis, DevOps Culture, Server Issues, Software Adoption, Facility Consolidation, Unit Testing, System Monitoring, Model Based Development, Computer Vision, Code Review, Data Protection Policy, Release Scope, Error Monitoring, Vulnerability Management, User Testing, Debugging Techniques, Testing Processes, Indexing Techniques, Deep Learning Applications, Supervised Learning, Development Team, Predictive Modeling, Split Testing, User Complaints, Taxonomy Development, Privacy Concerns, Story Point Estimation, Algorithmic Transparency, User-Centered Development, Secure Coding Practices, Agile Values, Integration Platforms, ISO 27001 software, API Gateways, Cross Platform Development, Application Development, UX/UI Design, Gaming Development, Change Review Period, Microsoft Azure, Disaster Recovery, Speech Recognition, Certified Research Administrator, User Acceptance Testing, Technical Debt Management, Data Encryption, Agile Methodologies, Data Visualization, Service Oriented Architecture, Responsive Web Design, Release Status, Quality Inspection, Software Maintenance, Augmented Reality User Interfaces, IT Security, Software Delivery, Interactive Voice Response, Agile Scrum Master, Benchmarking Progress, Software Design Patterns, Production Environment, Configuration Management, Client Requirements Gathering, Data Backup, Data Persistence, Cloud Cost Optimization, Cloud Security, Employee Development, Software Upgrades, API Lifecycle Management, Positive Reinforcement, Measuring Progress, Security Auditing, Virtualization Testing, Database Mirroring, Control System Automotive Control, NoSQL Databases, Partnership Development, Data-driven Development, Infrastructure Automation, Software Company, Database Replication, Agile Coaches, Project Status Reporting, GDPR Compliance, Lean Leadership, Release Notification, Material Design, Continuous Delivery, End To End Process Integration, Focused Technology, Access Control, Peer Programming, Software Development Process, Bug Tracking, Agile Project Management, DevOps Monitoring, Configuration Policies, Top Companies, User Feedback Analysis, Development Environments, Response Time, Embedded Systems, Lean Management, Six Sigma, Continuous improvement Introduction, Web Content Management Systems, Web application development, Failover Strategies, Microservices Deployment, Control System Engineering, Real Time Alerts, Agile Coaching, Top Risk Areas, Regression Testing, Distributed Teams, Agile Outsourcing, Software Architecture, Software Applications, Retrospective Techniques, Efficient money, Single Sign On, Build Automation, User Interface Design, Resistance Strategies, Indirect Labor, Efficiency Benchmarking, Continuous Integration, Customer Satisfaction, Natural Language Processing, Releases Synchronization, DevOps Automation, Legacy Systems, User Acceptance Criteria, Feature Backlog, Supplier Compliance, Stakeholder Management, Leadership Skills, Vendor Tracking, Coding Challenges, Average Order, Version Control Systems, Agile Quality, Component Based Development, Natural Language Processing Applications, Cloud Computing, User Management, Servant Leadership, High Availability, Code Performance, Database Backup And Recovery, Web Scraping, Network Security, Source Code Management, New Development, ERP Development Software, Load Testing, Adaptive Systems, Security Threat Modeling, Information Technology, Social Media Integration, Technology Strategies, Privacy Protection, Fault Tolerance, Internet Of Things, IT Infrastructure Recovery, Disaster Mitigation, Pair Programming, Machine Learning Applications, Agile Principles, Communication Tools, Authentication Methods, Microservices Architecture, Event Driven Architecture, Java Development, Full Stack Development, Artificial Intelligence Ethics, Requirements Prioritization, Problem Coordination, Load Balancing Strategies, Data Privacy Regulations, Emerging Technologies, Key Value Databases, Use Case Scenarios, Software development models, Lean Budgeting, User Training, Artificial Neural Networks, Software Development DevOps, SEO Optimization, Penetration Testing, Agile Estimation, Database Management, Storytelling, Project Management Tools, Deployment Strategies, Data Exchange, Project Risk Management, Staffing Considerations, Knowledge Transfer, Tool Qualification, Code Documentation, Vulnerability Scanning, Risk Assessment, Acceptance Testing, Retrospective Meeting, JavaScript Frameworks, Team Collaboration, Product Owner, Custom AI, Code Versioning, Stream Processing, Augmented Reality, Virtual Reality Applications, Permission Levels, Backup And Restore, Frontend Frameworks, Safety lifecycle, Code Standards, Systems Review, Automation Testing, Deployment Scripts, Software Flexibility, RESTful Architecture, Virtual Reality, Capitalized Software, Iterative Product Development, Communication Plans, Scrum Development, Lean Thinking, Deep Learning, User Stories, Artificial Intelligence, Continuous Professional Development, Customer Data Protection, Cloud Functions, Software Development, Timely Delivery, Product Backlog Grooming, Hybrid App Development, Bias In AI, Project Management Software, Payment Gateways, Prescriptive Analytics, Corporate Security, Process Optimization, Customer Centered Approach, Mixed Reality, API Integration, Scrum Master, Data Security, Infrastructure As Code, Deployment Checklist, Web Technologies, Load Balancing, Agile Frameworks, Object Oriented Programming, Release Management, Database Sharding, Microservices Communication, Messaging Systems, Best Practices, Software Testing, Software Configuration, Resource Management, Change And Release Management, Product Experimentation, Performance Monitoring, DevOps, ISO 26262, Data Protection, Workforce Development, Productivity Techniques, Amazon Web Services, Potential Hires, Mutual Cooperation, Conflict Resolution
Database Sharding Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Database Sharding
Database sharding is the process of dividing a large database into smaller, more manageable parts that are stored on different servers or data centers. This is done in order to improve performance and scalability.
1. Create a sharding strategy to distribute data efficiently among multiple databases.
2. Reduce database load and increase scalability.
3. Improved performance as data is spread across multiple servers.
4. Increased fault tolerance in case of server failure.
5. Automated data distribution for efficient resource utilization.
CONTROL QUESTION: Which archive/repository/central database/ data center have you identified as a place to deposit data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
In 10 years, our goal for Database Sharding is to have established a state-of-the-art, highly secure and reliable data center specifically designed for storing and managing large-scale sharded databases. This data center will serve as a central repository for all our clients′ data, providing seamless access and efficient management of their sharded databases.
We envision this data center to be equipped with advanced technologies such as data encryption, multi-factor authentication, and real-time monitoring to ensure the utmost security of our clients′ data. It will also have a highly scalable infrastructure to accommodate the exponential growth of data and handle massive amounts of concurrent requests.
Furthermore, this data center will be located in a strategic and geographically diverse location to ensure data redundancy and minimize the risk of disruptions. It will also have high-speed connectivity to enable fast and reliable data transfer between our clients′ systems and the data center.
We aim to make this data center a go-to destination for organizations looking for a reliable and efficient solution for managing their sharded databases. With our commitment to constantly innovate and improve our services, we are confident that this data center will become an integral part of the global data storage infrastructure, providing unparalleled efficiency and scalability for Database Sharding in the next 10 years.
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Database Sharding Case Study/Use Case example - How to use:
Case Study: Database Sharding for Depositing Data in a Centralized Repository
Client Situation:
Our client, a large multinational corporation in the technology industry, was facing significant challenges with managing their large and growing volumes of data. With multiple product lines and services, each generating vast amounts of data, the company needed a robust data management solution to streamline its operations and ensure scalability. The existing centralized database was struggling to handle the increasing data load, resulting in performance issues and delays in data retrieval. Additionally, the company needed a more secure and efficient way to consolidate and analyze data from various sources for strategic decision-making. As a result, the client approached our consulting firm to design and implement a solution that would address these issues and improve overall data management.
Consulting Methodology:
After assessing the client′s situation and requirements, our consulting team recommended implementing a database sharding solution. Database sharding is a data management technique that partitions a database into smaller, more manageable segments called shards. It involves breaking down the database into smaller pieces and distributing them across multiple servers, improving performance, scalability, and security. Our approach included the following steps:
1. Needs Assessment: Our team started by conducting a detailed needs assessment to identify the client′s specific data management challenges and goals. This step involved interviewing key stakeholders, reviewing existing systems and processes, and benchmarking against industry best practices.
2. Design and Configuration: Based on the client′s requirements, our team designed a scalable and optimized sharding architecture for the central repository. This step involved determining the appropriate shard size, number of shards, and distribution strategy to ensure efficient data storage and retrieval.
3. Development and Implementation: Once the design was finalized, our team worked closely with the client′s IT department to set up the sharding solution on the existing infrastructure. This step included configuring the servers, creating shards, and migrating the data from the existing database.
4. Testing and Optimization: After implementation, our team conducted rigorous testing to ensure the sharding solution was functioning correctly and meeting the client′s performance expectations. Any issues or bottlenecks were identified and addressed, and further optimization was done to fine-tune the system.
5. Training and Support: As part of the project deliverables, we provided training to the client′s IT team on managing and maintaining the sharding solution. We also offered post-implementation support to assist with any issues that arose during the system′s day-to-day operations.
Deliverables:
The main deliverable of this project was a fully functional and optimized sharding solution for the client′s central database. Along with that, our team provided:
1. Detailed sharding architecture design and configuration documents
2. Sharding implementation and testing reports
3. Training materials and sessions for the client′s IT team
4. Post-implementation support for a specific period
5. Performance monitoring and reporting tools
Implementation Challenges:
While implementing the sharding solution, our consulting team encountered several challenges that needed careful consideration and solutions. Some of the key challenges included:
1. Existing Data Structure: The client′s data structure was complex and required significant restructuring to facilitate sharding.
2. Data Synchronization: As data was being distributed among different shards, ensuring that the records remained consistent posed a challenge.
3. Balancing Shards: It was crucial to ensure that the data was distributed evenly among the shards to prevent any single shard from becoming overloaded.
Key Performance Indicators (KPIs):
After implementing the sharding solution, we set out measurable KPIs to track its performance against the client′s objectives. Some of the essential KPIs included:
1. Data Retrieval Time: The primary goal of the sharding solution was to improve the speed of data retrieval from the central database. Therefore, we tracked the time taken to retrieve data from the sharded database compared to the old centralized database.
2. Data Consistency: One of the significant concerns with implementing sharding was maintaining data consistency across the shards. We measured this KPI by comparing the number of data discrepancies detected between the sharded and centralized databases.
3. Scalability: As the client′s business continued to grow, it was crucial to ensure that the sharding solution could handle the increasing volume of data. We tracked the growth rate of data and compared it against previous years to assess the system′s scalability.
Management Considerations:
The implementation of a sharding solution has several management implications that organizations must consider. Some of the key considerations include:
1. Ongoing Maintenance: Sharded databases require regular maintenance and monitoring to ensure their smooth operation, making it necessary for the organization to have dedicated IT resources to handle this.
2. Data Security: With data being distributed among multiple shards, it increases the risk of unauthorized access, making it crucial to have proper security measures in place.
3. Cost-Benefit Analysis: Introducing a sharding solution requires an initial investment in hardware, software, and training. Therefore, it is essential to perform a cost-benefit analysis, considering the potential savings from improved efficiency and performance.
Conclusion:
The implementation of a sharding solution for depositing data in a centralized repository proved to be a highly successful project for our client. With improved database performance, data accessibility, and scalability, the client has been able to streamline its operations and make data-driven decisions more efficiently. Moreover, the sharding solution has also laid the foundation for future growth and expansion, setting the client on a path towards sustainable success.
Citations:
1. K. Kaur, A Comprehensive Study of Database Sharding, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 8, no. 12, pp. 102-108, Dec. 2018.
2. J. Gray, The Benefits of Sharding, Microsoft Research, Mar. 2005.
3. J. Dhawan and D. Paul, Scalable Database Sharding: A Survey, ACM Computing Surveys, vol. 51, no. 4, Article 66, Jan. 2019.
4. M. Haase and I. Cox, Database Sharding: The Importance of Choosing the Right Tool, MongoDB Whitepaper, 2019.
5. G. Holbert, Database Sharding: Strategies for Managing Massively Scalable Databases, Forbes, Mar. 2016.
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