DynamoDB

Website

aws.amazon.com

Categories

#Databases #NoSQL Databases #Relational Databases #Cloud Hosting

Datahike

Website

github.com

Categories

#Databases #NoSQL Databases #Relational Databases #Network & Admin

DynamoDB features and specs

• Scalability
  DynamoDB automatically scales up and down to handle your application's needs, with no intervention required. This allows for easy handling of traffic spikes and growth over time.
• Performance
  With its fast, predictable performance at any scale, DynamoDB ensures low-latency responses, even with large volumes of data.
• Fully Managed
  As a fully managed service, DynamoDB handles hardware provisioning, setup, configuration, replication, software patching, and backups, letting you focus on your application.
• Flexible Data Model
  DynamoDB supports both document and key-value store models, providing flexibility in how you structure your data.
• Security
  DynamoDB integrates with AWS Identity and Access Management (IAM) to provide fine-grained access control and encrypts data at rest and in transit.
• Global Tables
  You can create multi-region, fully replicated tables for high availability and globally distributed apps with low latency reads and writes.
• Event-Driven Architecture
  DynamoDB integrates with AWS Lambda for automatic triggering and the creation of event-driven architectures.

Possible disadvantages of DynamoDB

• Pricing Complexity
  DynamoDB's pricing model, which charges based on read and write capacity units, storage, and data transfer, can be complex and difficult to predict.
• Limited Query Capabilities
  DynamoDB does not support complex queries as well as traditional SQL databases. Querying capabilities are limited primarily to primary key attributes.
• Secondary Indexes
  While DynamoDB supports secondary indexes, their use can be limited and complex to manage effectively compared to relational databases.
• Consistency
  DynamoDB offers eventual consistency by default. While strongly consistent reads are available, they can be more expensive and slower.
• Data Size Limitations
  Each item in a DynamoDB table must be 400KB or less, limiting the amount of data you can store in a single item.
• Vendor Lock-In
  Using DynamoDB heavily ties your application to AWS, which can be a downside if you want to maintain flexibility in your cloud infrastructure choices.

Datahike features and specs

• Persistence
  Datahike is a persistent database, which means that it retains data across sessions and can be relied upon for storage that survives application restarts.
• Datalog queries
  Datahike supports Datalog queries, a powerful and expressive query language that is similar to Prolog, allowing for complex querying of data relationships.
• Schema flexibility
  Datahike provides schema flexibility that allows developers to define and evolve their data models without needing to perform migrations. This can significantly speed up development.
• Immutable data structures
  By utilizing immutable data structures, Datahike allows safe concurrent reads and writes, reducing the risk of data corruption and improving application stability.
• Transactional support
  Datahike offers ACID-compliant transactions, ensuring data integrity and consistent state even in the face of concurrent operations.
• Integration with Datomic API
  Datahike is designed to be compatible with the Datomic API, making it easier for developers familiar with Datomic to transition and leverage their knowledge.
• Off-the-shelf scalability
  The architecture of Datahike is conducive to scaling horizontally, providing flexibility to handle growing amounts of data and user load.

Possible disadvantages of Datahike

• Relatively new ecosystem
  Being a lesser-known and newer alternative compared to databases like Datomic, Datahike may have a smaller community and fewer resources like documentation and third-party integrations.
• Performance limitations
  While Datahike is designed to be lightweight and flexible, it may not match the performance of more mature databases, especially in very high-load or high-volume scenarios.
• Limited features
  Datahike may lack some advanced features present in other databases, such as sophisticated indexing or native support for certain types of analytics, which could be necessary for specific applications.
• Java Virtual Machine (JVM) requirement
  As it runs on the JVM, Datahike requires a Java runtime environment, which might not be ideal or convenient for projects seeking to minimize dependencies or employ lightweight deployment strategies.

#13 - Amazon DynamoDB Basics In Under 5 Minutes [Tutorial For Beginners]

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