NoSQL databases don’t operate in isolation. They thrive in ecosystems where programming languages are the conduits for interactions. We’ll unveil the pivotal role of programming languages in NoSQL development and evaluate their compatibility and support in the context of e-commerce.

However, e-commerce doesn’t stop at the borders of internal databases. The digital marketplace extends to the broader web, where competitive intelligence, market trends, and user-generated content beckon. Web scraping becomes the bridge that connects your database with this external world of data. We’ll explore the synergy between web scraping and NoSQL databases, examining how they form a robust foundation for e-commerce data enrichment.

Understanding Non-Relational Databases:

Non-relational databases, often called NoSQL databases, represent a class of database management systems that depart from the traditional tabular, structured model of relational databases. They are designed to handle large volumes of unstructured or semi-structured data and offer several advantages over traditional relational databases.

Advantages of NoSQL Databases

1. Flexible Data Models: NoSQL databases allow for flexible data modeling, making it easier to adapt to changing data requirements. Unlike rigid tables in relational databases, NoSQL databases can store data in various formats, such as key-value pairs, documents, graphs, or wide-column stores.
2. Scalability: NoSQL databases are highly scalable, both vertically and horizontally. Vertical scaling involves increasing the capacity of a single server, while horizontal scaling involves adding more servers to a distributed system. This scalability is crucial for handling the immense volumes of data e-commerce platforms generate.
3. High Performance: NoSQL databases are often optimized for specific use cases, providing high performance for read and write operations. This makes them well-suited for scenarios where low-latency data access is essential, such as real-time analytics and user interactions on e-commerce websites.
4. Schema-less Design: Unlike relational databases requiring predefined schema, NoSQL databases are schema-less. This means you can insert data without defining its structure beforehand, making handling diverse and evolving data types easier.
5. Horizontal Partitioning: Many NoSQL databases support automatic data partitioning and distribution across multiple nodes, enabling efficient data storage and retrieval even as the dataset grows.

When to Prefer NoSQL over Relational Databases

NoSQL databases are preferred over traditional relational databases in several scenarios, including:

• Unstructured Data: When dealing with unstructured or semi-structured data like product descriptions, user-generated content, or JSON documents, NoSQL databases manage such flexible data formats.
• Scalability Demands: E-commerce platforms often experience fluctuating workloads and rapid growth. NoSQL databases offer horizontal scalability to handle spikes in traffic without significant performance degradation.
• Frequent Schema Changes: In e-commerce, product attributes, customer profiles, and catalog structures can change frequently. NoSQL databases accommodate these changes without requiring complex schema migrations.
• Real-time Analytics: E-commerce businesses rely on real-time data analysis for personalization and recommendations. NoSQL databases support quickly retrieving and analyzing data to provide timely insights.
• High Availability: NoSQL databases are designed for fault tolerance and high availability. E-commerce websites must be operational around the clock, and NoSQL databases help ensure uninterrupted service.

For a more detailed exploration of NoSQL database design principles, you can reference the article “How to design non-relational databases“.

Data Requirements in E-commerce

The e-commerce industry has distinct data requirements driven by the nature of online retail operations, customer interactions, and the need for effective decision-making. Understanding these requirements is crucial for designing a robust database system.

Variety of Data Types

1. Product Listings: E-commerce platforms manage extensive product catalogs. Each product listing can include diverse data such as product names, descriptions, prices, images, and attributes like size, color, and availability.
2. Customer Data: Customer information is a core component, including user profiles, contact details, purchase histories, and preferences. Personalization and targeted marketing rely on effective management of customer data.
3. Transaction Records: E-commerce generates a continuous stream of transaction data. This includes order details, payment information, shipping and delivery records, and returns. Accurate tracking of transactions is vital for order fulfillment and financial reporting.
4. User Behavior and Analytics: Understanding user behavior is essential. Tracking user interactions, clickstreams, session data, and conversion rates provides insights for optimizing the user experience and marketing strategies.
5. Reviews and Feedback: Customer reviews and feedback contribute to product ratings and reputation. Storing and managing this data is essential for maintaining transparency and trust.
6. Inventory and Stock Management: Inventory data is critical for maintaining accurate stock levels, preventing overselling, and ensuring timely restocking.
7. Logistics and Shipping: Managing shipping information, tracking numbers, and delivery statuses is essential for order tracking and customer satisfaction.

Scalability and Flexibility

E-commerce data requirements can grow exponentially, especially for successful businesses. Scalability is crucial to accommodate increasing data volumes without compromising performance. NoSQL databases excel in this regard, as they allow for horizontal scaling, making it feasible to add more servers or nodes to the system as data grows.

Flexibility in data modeling is also key. E-commerce databases must adapt to changing product attributes, customer preferences, and business strategies. NoSQL databases, with their schema-less design, enable flexibility in data structure, making it easier to incorporate new data types and accommodate changes in product offerings.

Real-time Data Processing

Real-time data processing is essential in e-commerce for various reasons:

• Inventory Management: Accurate stock levels must be maintained in real time to prevent overselling.
• Recommendation Engines: Personalized product recommendations require real-time analysis of user behavior.
• Fraud Detection: Real-time analysis of transactions can help detect and prevent fraudulent activities.
• Order Processing: Customers expect immediate order confirmations and shipping updates.

To achieve real-time data processing, databases must be capable of handling high volumes of concurrent requests and performing complex queries efficiently. This is where NoSQL databases, with their distributed and highly available nature, excel in e-commerce applications.

Understanding these data requirements is fundamental to designing a database system that meets the demands of the e-commerce industry. It sets the stage for effective data modeling and database architecture.

Choosing the Right Programming Language for NoSQL Development

When it comes to developing applications that interact with NoSQL databases, the choice of programming language plays a significant role in determining your software’s efficiency, maintainability, and performance. Here, we’ll explore the key aspects of selecting the right programming language for NoSQL development. Role of Programming Languages in NoSQL Development:

1. Integration and API Support: Programming languages serve as the bridge between your application and the NoSQL database. They handle database connections, execute queries, and process data. The choice of language should align with the database’s API and client libraries.
2. Data Modeling: Different programming languages offer varying levels of support for data modeling. Some languages are well-suited for mapping data structures to NoSQL database schemas, making it easier to work with complex data.
3. Performance: The performance of your application can be influenced by the programming language you choose. Some languages are more efficient at handling specific data processing tasks, which can impact the speed and responsiveness of your application.
4. Ecosystem and Libraries: The programming language’s ecosystem and the availability of third-party libraries can significantly impact development speed. Languages with robust ecosystems often have libraries for NoSQL database integration, simplifying development.

Compatibility and Support of Popular Programming Languages

The choice of programming language should align with your project’s requirements and the compatibility of the language with the NoSQL database you plan to use. For more information on programming languages used in web scraping, you can reference the article “Top Programming languages for Web Scraping“. Here’s a brief overview of the compatibility and support of popular programming languages for NoSQL development:

1. Python: Python is widely used for NoSQL development, thanks to its readability and extensive library support. It has mature drivers and libraries for popular NoSQL databases like MongoDB, Cassandra, and Redis.
2. JavaScript (Node.js): JavaScript, especially when used with Node.js, is well-suited for NoSQL development. It offers asynchronous capabilities that align with NoSQL databases’ non-blocking nature. Node.js has libraries for MongoDB, Couchbase, and more.
3. Java: Java is known for its performance and is a preferred choice for NoSQL databases like Cassandra and HBase. It has robust support for connecting to various NoSQL databases through libraries and drivers.
4. Ruby: Ruby can be used for NoSQL development with databases like MongoDB and Redis. It’s known for its simplicity and developer-friendly syntax.
5. Go (Golang): Go is gaining popularity in NoSQL due to its efficiency and strong support for concurrency. It has libraries and drivers for databases like MongoDB and Cassandra.
6. PHP: PHP is commonly used for web development and integrates well with NoSQL databases like MongoDB and Couchbase through available extensions.

Choosing the right programming language for your NoSQL development should be based on factors such as your team’s expertise, your project’s specific requirements, and the language’s compatibility with the chosen NoSQL database.

Web Scraping for E-commerce Data

In e-commerce, web scraping is a powerful technique for acquiring data from external sources, supplementing your internal databases powered by NoSQL systems. This section will delve into the importance of web scraping, especially in conjunction with NoSQL databases, while maintaining continuity with the previous sections.

Connecting E-commerce Data with Web Scraping

As we’ve discussed earlier, e-commerce databases must manage a wide array of data, including product listings, customer data, and transaction records. However, not all data can be directly ingested into your database through conventional means. This is where web scraping comes into play.

Web scraping allows you to extract data from product pages, competitor websites, customer reviews, and other online sources, enriching your database with valuable insights. This data can be used for various purposes:

1. Product Catalog Enrichment: Web scraping lets you update your catalog with real-time pricing, availability, and product descriptions from supplier websites.
2. Competitive Analysis: By scraping competitor websites, you can gather competitive pricing information, product features, and customer reviews, helping you stay competitive.
3. User-generated Content: Collecting user-generated content, such as product reviews and ratings, can enhance customer trust and provide valuable feedback for product improvements.
4. Market Trends: Web scraping can monitor market trends, pricing fluctuations, and customer sentiment, assisting in strategic decision-making.

Shopify Python API and Web Scraping

For e-commerce businesses using the Shopify platform, the Shopify Python API can be a valuable tool for integrating web scraping capabilities. This API allows you to interact with your Shopify store programmatically, enabling you to automate tasks like inventory management, order processing, and data extraction.

By combining the Shopify Python API with web scraping libraries such as Scrape-it.cloud, you can:

• Retrieve product information, pricing, and inventory data directly from your Shopify store.
• Scrape competitor websites to gather market insights and pricing data.
• Automate the process of updating your product catalog with real-time data.
• Integrate user-generated content from various sources to enhance your product listings.

Real-time Data Processing and NoSQL Databases

One of the challenges in web scraping for e-commerce is processing real-time data efficiently. NoSQL databases, with their ability to handle high volumes of data and support for horizontal scalability, are well-suited for managing the influx of scraped data in real time.

With the proper data pipeline, you can scrape data from the web, transform it, and seamlessly store it in your NoSQL database for immediate access and analysis. This ensures that your e-commerce operations are always based on the most up-to-date information.

Conclusion

In the world of e-commerce, where data is the lifeblood of business operations, the design of non-relational databases and the choice of programming language play pivotal roles. Understanding the nuances of NoSQL databases and their compatibility with programming languages sets the foundation for efficient and scalable e-commerce data management.

Web scraping, coupled with the power of NoSQL databases, emerges as a valuable tool for e-commerce businesses. Using web scraping techniques to gather data from online sources, you can enrich your database with real-time information, stay competitive, and make data-driven decisions. Integrating the Shopify Python API and web scraping libraries like scrape-it.cloud allows you to seamlessly connect your e-commerce data with external sources.

In conclusion, successful e-commerce operations depend on a well-designed NoSQL database, the right programming language, and the ability to harness web scraping for data enrichment. By leveraging these technologies and principles, e-commerce businesses can thrive in a data-driven landscape, delivering exceptional customer experiences and staying ahead in the competitive market.

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From understanding data models to evaluating consistency models and considering scalability, we provide you with practical insights that bridge the gap between theory and real-world application. Whether you’re a seasoned developer seeking to fine-tune your database selection skills or a project manager looking to make informed choices, this article is your roadmap to success. Join us on this enlightening journey to discover the essential considerations that will shape your project’s data management strategy and set the stage for its triumph.

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In the ever-evolving landscape of database management, NoSQL databases have emerged as a valuable alternative to traditional relational databases. These non-relational databases are designed to handle vast amounts of unstructured or semi-structured data efficiently. However, with a variety of NoSQL database types available, selecting the right one for your project can be a daunting task. In this article, we will explore the key factors to consider when choosing a NoSQL database, helping you make an informed decision that aligns with your project’s requirements and goals.

1. Data Model

The first and most fundamental consideration is the data model. NoSQL databases are classified into four main types based on their data models:

• Document: Stores data in flexible, semi-structured documents (e.g., MongoDB).
• Key-Value: Stores data as key-value pairs, suitable for high-speed data retrieval (e.g., Redis).
• Column-Family: Organizes data into column families, optimized for read-heavy workloads (e.g., Apache Cassandra).
• Graph: Focuses on relationships between data points, ideal for complex data structures (e.g., Neo4j).
• Understanding your project’s data structure and how it will evolve over time is crucial in choosing the appropriate data model.

2. Scalability

Scalability is often a paramount concern in modern applications. NoSQL databases offer two primary types of scalability:

• Horizontal Scaling: Involves adding more machines or nodes to distribute the data and workload (common in document and key-value stores).
• Vertical Scaling: Involves upgrading the existing hardware resources to handle increased data and traffic (common in column-family and graph databases).

Consider the growth trajectory of your project and choose a NoSQL database that can scale accordingly without compromising performance.

3. Consistency Model

NoSQL databases offer varying levels of data consistency, typically falling into one of three categories:

• Strong Consistency: Guarantees that all read operations return the most recent write, but this can affect performance.
• Eventual Consistency: Allows for some lag in consistency but can offer improved performance.
• CA, CP, or AP: Databases often adhere to the CAP theorem, prioritizing consistency (CA), partition tolerance (CP), or availability (AP) differently.

Determine the level of consistency your project requires and choose a NoSQL database with an appropriate consistency model.

4. Query Language

Consider the query language supported by the NoSQL database. Some databases offer SQL-like query languages, while others provide their query languages or APIs. Choose a database with a query language that aligns with your project’s requirements and your team’s expertise.

5. Performance and Latency

Evaluate the performance characteristics of the NoSQL database, including read and write latency. Some databases excel in high-speed data retrieval, while others prioritize write-intensive workloads. The performance profile should match your project’s needs.

6. Community and Support

The strength of the community and the availability of support can significantly impact your project’s success. Opt for a NoSQL database with an active user community, extensive documentation, and reliable support channels.

7. Ecosystem and Integrations

Consider the broader ecosystem of the NoSQL database. Does it integrate well with the programming languages, frameworks, and tools your project relies on? Compatibility with existing technology stacks can streamline development and maintenance.

8. Security and Compliance

Security is a critical concern for any project. Evaluate the security features offered by the NoSQL database, including encryption, access control, and compliance with industry standards and regulations.

9. Cost and Licensing

Determine the cost implications of your chosen NoSQL database, including licensing fees, infrastructure costs, and ongoing maintenance expenses. Be mindful of your project’s budget constraints.

10. Data Backup and Recovery

Finally, assess the database’s data backup and recovery mechanisms. Ensure that it provides robust backup options and a clear recovery process to safeguard your data against unexpected incidents.

Conclusion

Choosing the right NoSQL database for your project is a crucial decision that can impact its performance, scalability, and overall success. By carefully considering factors such as data model, scalability, consistency, query language, performance, community support, ecosystem compatibility, security, cost, and data backup, you can make an informed choice that aligns with your project’s unique requirements and objectives. It’s a decision that warrants careful consideration to ensure your project’s long-term success and efficiency in handling your data needs.

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Here are the four most common types of NoSQL databases:

Key-value
In key-value type stores, key and value pairs are generated using a hash table. Key-value type databases are best suited for when the key is known but the associated value is not.

Document databases
Document databases are an extended version of key-value databases in which entire documents are organized into groups called collections. They support nested key-value pairs and queries on any attributes of documents.

Column-based databases
Columnar, wide column, or column family based databases provide efficient data storage and querying of rows of sparse data. They are also convenient to use for querying specific columns of a database.

Graph
Graph databases use a model based on nodes and edges that represent related data (e.g., relationships between people in a social network). They also simplify storage and navigation of complex relationships.

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• Data Consistency (Consistency). That is, the data must be complete and consistent (including across all nodes in the cluster).
• Availability. Roughly speaking, this is the speed of server response to our request (write or read).
• Partition tolerance. This means that if the system is divided into several parts, each of them, if available, should be able to work autonomously, giving the correct response and providing its data. A broken link in the cluster should not affect the final performance.

The CAP theorem tells us that we can only get two of these three components. Or in the same 2:1 proportion, balance between these components: improved performance in one of the properties entails degradation in some other property. To better understand the power of this theorem, imagine a distributed storage where you are trying to provide 100% consistent data (read and write) without performance brakes.

In a single-server architecture, if the server is running, it is available. And the database on it, if the designer’s hands grow from the shoulders, is consistent. There is no need to worry about node partitioning, since the system is physically indivisible. It is under such conditions that classical relational systems emerged. And this is why they are partition-unstable: it makes no sense to design a separable structure in an indivisible environment.

In fact, almost all NoSQL technologies were born to solve the problem of partitioning resistance, that is, to run efficiently on clusters. The relational model is not up to the task because it was created for other purposes and in other environments. You will not be able to “just saw off a couple or three tables or quietly partition them into a neighboring cluster” and then go for coffee and tea. Welcome to Hell.

NoSQL stores by their very nature can be easily partitioned into a cluster because of the specific storage structure.

It is in a distributed system environment that the true essence of the CAP theorem becomes apparent. Obviously, creating a cluster that is unstable to partitioning is devoid of any practical use. That is, the cluster must be created a priori as partition-resistant. Understanding this fact allows us to see the CAP theorem in a new light: one can choose only one of consistency and availability – or use a reasonable compromise between these two items (not three, as one might think from the original definition).

The second task that NoSQL technology ideologists try to solve is to increase availability, i.e. to get a fast server response.

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A database is used for dynamic websites with large volumes (online store, portal, corporate website).

What you need to know

A database is a certain set of data that is interconnected by a common feature or property and is organized, for example, alphabetically.

Combining a large amount of data into a single database allows you to create many variations of grouping information – personal data of a customer, order history, product catalog, and anything else.

The main advantage of the database is the speed of entering and using the necessary information. Thanks to special algorithms used for databases, you can easily find the necessary data in just a few seconds. Also, there is a certain interconnection of information in the database: a change in one row can cause changes in other rows – this helps to work with information easier and faster.

Purpose

Databases for websites allow you to store information that looks like linked tables. It is in the database that all the necessary and useful information for the website functioning is stored (customer data, price list, list of goods).

When programming websites, different database management systems are used. The main DBMSs include:

• Oracle Database, an object-relational database management system;
• free database management system PostgreSQL;
• Microsoft SQL Server database management system;
• free MySQL database management system;

Such management systems are characterized by centralized query processing, ensure reliability, availability, and security of the database.

The most popular management system is MySQL; it provides convenient access to database management and supports a large number of tables of various types.

Distributed databases

Distributed databases (DBMS) are a set of logically interconnected databases that are distributed over a computer network.

Advantages of RDBMS:

• This model displays information in the simplest form for the user;
• it is based on a developed mathematical apparatus that allows to describe the basic operations with data quite concisely;
• allows creating data manipulation languages of non-procedural type;
• manipulation of data at the level of the source database and the possibility of changes.

Disadvantages of RDBMS:

• the slowest access to data;
• laboriousness of development.
• Database management systems

DBMSs are software tools that act as an intermediary between the database and its users. Thanks to a set of language and software tools, DBMSs facilitate the creation, maintenance and sharing of databases by different users.

A modern DBMS program consists of a kernel, a database language processor, a runtime support subsystem, and service programs that provide additional capabilities for maintaining information systems.

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One of the main differences from other tree structures, such as AVL, is that B-Tree allows for a variable number of children, which means less tree balancing but more space wastage. B+Tree is one of the most popular variants of B-trees. This improvement (unlike B-Tree) requires all keys to be in a leaf.

The T-Trees data structure was developed by combining the features of AVL Trees and B-Trees. AVL trees are a type of self-balancing binary search trees, while B-Trees are unbalanced, and each node can have a different number of children.

The structure of a T-tree is very similar to an AVL-tree and a B-tree. Each node stores a tuple {key-value, pointer}. In addition, binary search is used in combination with multiple nodes and tuples to provide better memory and performance.

A T-tree has three types of nodes: a node with right and left children, a terminal node with no children, and a half-leaf node with only one child. T-Trees are considered to have better overall performance.

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Native native types are a new methodology for developing non-relational NoSQL databases implemented by large companies to meet corporate needs, such as Google’s BigTable, which is considered the first NoSQL system, and Amazon DynamoDB. The success of these systems gave rise to the development of a number of similar open source and proprietary database systems, the most popular of which are Hypertable, Cassandra, MongoDB, DynamoDB.

The SQL scalability problem has been recognized by Web 2.0 companies with huge, growing data and infrastructure needs, such as Google, Amazon, and Facebook. They found their own solutions to the problem by implementing BigTable, DynamoDB, and Cassandra technologies. The growing interest has led to the emergence of a number of NoSQL database management systems (DBMS) with a focus on performance, reliability, and consistency. A number of existing indexing structures have been reused and improved in order to improve search and read performance.

The term was coined by Calor Strozzi back in 1998 and revived in 2009 by Rackspace employee Eric Evans to address the problems of web companies with a large volume of transactions and information.

One key difference between NoSQL databases and traditional relational databases is that the former is a form of unstructured storage.

Thus, NoSQLs do not have a fixed table structure, as in a relational system. This table provides a brief comparison of NoSQL and SQL capabilities.

It should be noted that the table shows a comparison at the database level, not the DBMSs that implement both models. These systems provide their own proprietary methods to overcome some of the problems and shortcomings of both systems, which greatly improves performance and reliability.

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How do you know if you need a non-relational database?

• The project has a large dataset. When you have more than 1 TB of data and its index does not fit in RAM, it is better to abandon the relational model.
• High update speed. If you need more than 100 KUps, choose NoSQL.
• Availability. A non-relational database can have a fixed latency of 2 ms and provide high read and ping speeds, as well as no locks. In SQL, this is possible only on small datasets.
• Stable access pattern. NoSQL is ideal for business applications where clear access patterns can be identified. These can be queries that do not change or change slowly. For example, a list of certain products in an online store.
• Accounting prevails over analysis. This is the OLTP approach mentioned above, which is associated with access patterns.

After choosing the right type of database, proceed to design. In the case of NoSQL, we are limited in the ways we can access data. Therefore, you need to customize the model for frequent and important queries. To do this, analyze the Use Cases and all product requirements in detail. This way, you will be able to correctly assess how to look at your data. This will form the basis of the future data structure.

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All you need to work with the items stored in the database is a key. The data is stored as a JSON string or BLOB (large binary object). One of the biggest disadvantages of this form is the lack of consistency at the database level. This can be added during the development of a NoSQL database by programmers with their own code, but it also requires more effort, due to the complexity of implementation and time. The most famous NoSQL database built on a key value store is Amazon DynamoDB.

Document stores are similar to key value stores in that they do not contain a schema and are based on a value model. So, both types have the same advantages and disadvantages. Both lack consistency at the database level, which does not allow applications to provide more reliable functions. Nevertheless, there are some key differences between them. In document stores, values (documents) provide the encoding for the stored data. These encodings can be XML, JSON, or BSON (binary JSON). The most popular database application that uses a document store is MongoDB.

In the Column Family database, data is stored in columns, not in rows, as is done in most relational database management systems. A column store consists of one or more column families that logically group certain columns in the database. A key is used to identify and specify the number of columns with a keyspace attribute that defines its scope. Each column contains tuples of names and values, ordered and separated by commas.

Column stores provide quick read/write access to the stored data. In it, the columns of a row correspond to a single column and are stored as a single disk record. This provides faster access during read/write operations. The most popular databases that use NoSQL database column storage are examples: Google BigTable, HBase, and Cassandra.

The NoSQL Graph Bd database uses the structure of an oriented graph to represent data. A graph consists of edges and nodes.

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• Horizontal scalability with the ability to increase its size, increase the storage space in the database without compromising performance.
• Cloud technology. Most NoSQL databases base their storage in the cloud to free up more space. Besides, they have nodes for information replication.
• Efficient use of resources. Companies are currently in the process of technological transition, so it is almost necessary that they have a database that allows them to implement new technological tools. NoSQL data works for this very purpose – a flexible model allows you to quickly adapt to new tools.
• A free scheme of functioning. NoSQLs do not have a rigid system, so programmers have the freedom to change data as needed. This means that if you need to change the definition of a field or data type, it is no problem, unlike SQL databases, where such changes are associated with great difficulties.
• Response speed. The speed in the database is measured by latency, which is the response time, NoSQL is concerned with minimizing the latency as much as possible.
• Use of indexes. SQL and NoSQL both need indexes because queries can be made across millions of records if an index has not been configured. In NoSQL, indexes are created in the form of a B-Tree, which means that the nodes are balanced, which means that the search speed increases.

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