NoSQL(MongoDB and DynamoDB) Overview.pptx

NoSQL presentation.

1. NoSQL Databases: An Overview July 6, 2024

2. Introduction to NoSQL Databases History and Background of NoSQL Databases Data Model Differences Storage Mechanisms Indexing Techniques Blob Storage Agenda Scalability Performance Flexibility Pricing Models Security Market Trends

3. Milestones The concept of NoSQL databases emerged in the late 2000s. It was driven by the need to handle large-scale data storage and processing beyond the capabilities of traditional SQL databases. Early Beginnings Key Developments Amazon's Dynamo paper (2007) and Google's Bigtable paper (2006) were pivotal. They introduced distributed storage systems that could scale horizontally, influencing many modern NoSQL databases. The term 'NoSQL' gained popularity around 2009. Major NoSQL databases like MongoDB (2009) and Apache Cassandra (2008) were released, providing alternatives to relational databases for handling big data. History and Background of NoSQL Databases Plus tip: Customize this slide by adding specific examples or anecdotes relevant to your audience's industry or interests.

4. Introduction to NoSQL Databases NoSQL databases are designed for large-scale data storage and for massively-parallel, high-performance data processing across a large number of com modity servers. They are schema-less and can handle unstructured data. Primarily used for big data and real-time web applications, NoSQL databases are ideal for applications requiring large-scale data storage and horizontal scalability, such as social media, ecommerce, and IoT. Overview of NoSQL Purpose and Use Cases Comparison with SQL Unlike SQL databases, which use structured query language and a fixed schema, NoSQL databases offer flexible schema design, allowing for dynamic data models. They provide high scalability, often at the cost of consistency. Plus tip: Customize this slide by including specific examples of NoSQL databases relevant to your audience, such as MongoDB for document storage or DynamoDB for key-value storage.

5. Data Model Differences Schema flexibility allows for dynamic and evolving data structures. Document model stores data in JSON/BSON format, e.g., MongoDB. Key-value model uses pairs for fast retrieval, e.g., DynamoDB. Column-family model groups related data, e.g., Cassandra. Graph model represents data as nodes and edges, e.g., Neo4j. NoSQL Data Models Schema requires predefined structure with tables and columns. Data is organized in relational tables with fixed schemas. Strong ACID compliance ensures reliable transactions. Supports complex joins and queries across multiple tables. Normalization reduces data redundancy and improves consistency. SQL Relational Model Plus tip: You can further customize the content by including specific use-case examples or visual diagrams to illustrate the differences in data models.

6. Comparison with MySQL MongoDB stores data in JSON-like documents, while DynamoDB uses a key-value and document data model. MySQL, on the other hand, stores data in structured tables with predefined schemas. MongoDB supports horizontal scaling through sharding, and DynamoDB offers seamless auto-scaling. MySQL primarily scales vertically, adding more power to a single server. Structure Scalability Use Cases MongoDB is ideal for flexible, semi-structured data. DynamoDB excels in high-traffic environments needing low-latency. MySQL is suited for complex queries and transactions in relational data. Plus tip: Customize the comparison points based on the specific aspects that are most relevant to your audience, such as performance or data consistency.

7. Storage Mechanisms SQL databases use a structured schema with tables, rows, and columns. Data is stored in predefined data types, ensuring consistency and integrity through ACID transactions. NoSQL databases store data in various formats like documents, key-value pairs, wide-columns, or graphs. This flexibility allows for handling unstructured or semi-structured data efficiently. SQL Storage NoSQL Storage Data Retrieval SQL databases use SQL queries to retrieve data, which involves complex joins and aggregations. NoSQL databases often use simpler query mechanisms optimized for fast retrieval based on the storage model. Plus tip: To customize this slide, you can add specific examples of SQL and NoSQL databases and illustrate their storage mechanisms with diagrams.

8. Flexibility NoSQL databases support diverse data types like JSON, XML, and key-value pairs. Ideal for unstructured and semi-structured data, enabling more dynamic applications. SQL databases primarily handle structured data with predefined schemas. Handling Various Types of Data NoSQL databases offer schema-less design, allowing easy modifications and iterations. Developers can add new fields without impacting existing data or requiring downtime. SQL databases need predefined schemas, making structural changes complex and time-consuming. Changing Data Structures Plus tip: Customize this slide by adding specific examples from your organization where NoSQL databases provided flexibility in handling diverse data types or adapting to changing data requirements.

9. SQL databases typically use B-tree and hash indexes. These indexes are essential for optimizing query performance, reducing data access time, and improving overall efficiency. SQL Database Indexing Comparison NoSQL indexing is often more flexible, supporting a variety of data types and access patterns. SQL indexing is highly optimized for structured data and complex queries, offering robust performance improvements. MongoDB supports various index types including single field, compound, and geospatial indexes. Indexes improve query performance by allowing faster data retrieval. MongoDB Indexing DynamoDB Indexing DynamoDB uses primary keys and secondary indexes (both global and local). These indexes enhance query flexibility and performance, allowing efficient data access patterns. Indexing Techniques Plus tip: Customize this slide by adding specific examples of queries that benefit from each type of indexing technique.

10. MongoDB and DynamoDB MongoDB is a document-oriented NoSQL database known for its flexibility and scalability. It stores data in JSON-like documents, making it ideal for dynamic schemas and hierarchical data structures. DynamoDB is a fully managed NoSQL database service provided by AWS. It is designed for high availability and scalability, using a key-value and document data model to handle large amounts of traffic and data. MongoDB Overview DynamoDB Overview Primary Use Cases MongoDB excels in content management, real-time analytics, and mobile applications. DynamoDB is commonly used in gaming, IoT applications, and any scenario requiring low-latency data access at scale. Plus tip: Customize this slide by adding specific examples or case studies relevant to your audience's industry to make the content more relatable.

11. Rich query language supports a wide range of queries, including field, range, and regular expression searches, providing powerful data retrieval capabilities. Flexible schema enables dynamic, ad-hoc changes to data structures, allowing for easy scalability and evolution of applications. MongoDB: Key Features Document-oriented storage allows data to be stored in JSON-like documents, making it flexible and easy to map to objects in application code. Plus tip: Consider adding specific examples or case studies to illustrate how these features have benefited real-world applications.

12. Provides fine-grained access control with AWS Identity and Access Management (IAM) policies, ensuring secure data access. Offers high scalability with seamless scaling up or down to handle massive amounts of data and requests without downtime. Fully managed service that eliminates the need for manual administrative tasks such as hardware provisioning, setup, and configuration. DynamoDB: Key Features

13. In NoSQL databases, Blob Storage is efficiently handled using distributed storage systems, providing scalability and high availability. SQL databases also support Blob Storage, but they often require additional configurations and may not scale as efficiently for large volumes of unstructured data. Blob Storage stands for Binary Large Objects, used to store large amounts of unstructured data like images, videos, and documents. Blob Storage 1 2 3 Plus tip: Tailor the content to reflect specific use cases or examples from your organization to make the comparison more relevant.

14. Adding Binary Large Objects (BLOBs) Store BLOBs using GridFS, a file storage specification. Split files into chunks of 255KB and store in two collections. Use MongoDB drivers or libraries to upload and retrieve BLOBs. Adding BLOBs in MongoDB Store BLOBs directly in binary attributes. Recommended for small BLOBs due to 400KB item size limit. Use S3 for larger BLOBs and store references in DynamoDB. Adding BLOBs in DynamoDB Plus tip: Customize this slide by providing code examples or screenshots of the process for adding BLOBs in MongoDB and DynamoDB.

15. Scalability Designed for horizontal scaling across distributed systems Easily add more nodes to handle increased load Examples: MongoDB uses sharding to distribute data across servers Traditionally rely on vertical scaling by upgrading server hardware Complex and costly to scale out horizontally Examples: MySQL can use replication but lacks seamless horizontal scaling NoSQL Databases SQL Databases Plus tip: Customize this slide by adding specific examples or case studies relevant to your audience's industry to illustrate the scalability benefits of NoSQL databases over SQL databases.

16. Security Features MongoDB offers encryption at rest through the WiredTiger storage engine, while DynamoDB provides encryption at rest using AWS Key Management Service (KMS). Both also support TLS for data in transit. MongoDB supports various authentication mechanisms including SCRAM, LDAP, and Kerberos. DynamoDB relies on AWS Identity and Access Management (IAM) for user authentication and permissions. Encryption Authentication Access Control MongoDB uses role-based access control (RBAC) to manage permissions. DynamoDB employs IAM policies to control access at a granular level, including fine-grained access control for items and attributes. Plus tip: Customize the slide by adding your own examples or specific security policies your organization follows when using these databases.

17. Access control in NoSQL databases includes role-based access control (RBAC), which is also common in SQL databases. Compliance with regulations like GDPR and HIPAA is supported by many NoSQL databases, aligning with SQL databases. Security NoSQL databases offer data encryption at rest and in transit, similar to SQL databases. Both NoSQL and SQL databases offer features like IP whitelisting and Virtual Private Cloud (VPC) for network security. NoSQL databases often provide auditing features to track and log access and changes to data. SQL databases have a longer history of security features, but NoSQL databases have rapidly caught up in recent years. Plus tip: You can customize this slide by adding specific security features of the NoSQL databases you are using, such as MongoDB's field-level encryption or DynamoDB's fine-grained access control.

18. Charged based on storage and provisioned throughput Offers flexible pricing plans including pay-as-you-go and reserved instances Additional costs for backup, monitoring, and advanced security features MongoDB Pricing Model Charges for read/write capacity units and data storage On-demand and provisioned capacity modes available Additional costs for global tables, backups, and DynamoDB Streams DynamoDB Pricing Model Pricing Models Plus tip: Customize this slide by including specific pricing examples or cost calculators to help the audience understand potential expenses.

19. SQL databases often have licensing fees based on the number of users or cores, with options for enterprise and standard editions. DynamoDB uses on-demand and provisioned capacity pricing, with additional costs for backup, restore, and data transfer. MongoDB offers a flexible pricing model, including a free tier, pay-as-you-go cloud pricing, and enterprise licensing. Pricing Models Cloud-based SQL databases like Azure SQL and Amazon RDS offer pay-as-you-go pricing, with additional costs for storage and data transfer. Plus tip: You can customize this slide by adding specific examples of pricing from current market offerings or by including a comparison table for visual impact.

20. NoSQL databases are seeing increasing adoption rates, particularly in sectors requiring high scalability and flexibility, such as e-commerce, social media, and IoT. Adoption Rates Market Trends Industries like finance, healthcare, and retail are leveraging NoSQL for real-time analytics, customer insights, and handling large volumes of unstructured data. Industry Usage Emerging technologies such as AI and machine learning are integrating with NoSQL databases to provide advanced data processing and predictive analytics capabilities. Technological Advancements The NoSQL market is projected to grow at a CAGR of over 20% through 2025, driven by the increasing need for big data analytics and cloud computing. Future Projections Plus tip: Tailor the data in each section to reflect the latest statistics and specific examples from your industry or organization.

21. Performance Performance Comparison: NoSQL vs SQL Databases NoSQL databases generally offer faster read/write speeds for large datasets due to their flexible schemas and horizontal scaling. This chart is a placeholder. Customize it here . Plus tip: To customize this slide, consider including specific performance metrics from real-world use cases or benchmarks relevant to your industry.

22. Conclusion Plus tip: Consider personalizing this slide by including specific use cases or experiences your team has had with MongoDB and DynamoDB to provide practical insights. In conclusion, both MongoDB and DynamoDB offer powerful NoSQL solutions with distinct features. MongoDB excels with its flexible schema design, rich query capabilities, and developer-friendly environment. DynamoDB stands out for its fully managed service, seamless scalability, and fine-grained access control. While MongoDB is ideal for applications needing complex queries and schema flexibility, DynamoDB is perfect for high-throughput and low-latency requirements. Both databases provide robust security features and have different pricing models to suit various needs. Choosing the right one depends on your specific use case and requirements.