Presto

Website

presto.com

$ Details

-

Categories

#Enterprise Software #Food And Beverage #Restaurants #SaaS #Software #POS

MongoDB

Website

mongodb.com

$ Details

Categories

#NoSQL Databases #Databases #Document Databases #Relational Databases

Presto features and specs

• Scalability
  Presto is highly scalable, allowing it to perform queries on large datasets across various data sources without data movement. It's designed to handle petabytes of data efficiently.
• High Performance
  Presto provides low latency in query execution, making it suitable for interactive analytics. It optimizes query processing with techniques like in-memory computation and distributed query execution.
• Flexibility
  It supports querying data from a wide variety of data sources including HDFS, S3, SQL databases like MySQL and PostgreSQL, and NoSQL databases like Cassandra, making it highly versatile.
• SQL Compliance
  Presto uses ANSI SQL, providing a smooth transition for users familiar with SQL, thus lowering the learning curve.
• Open Source
  Being open-source, Presto benefits from community contributions and support, allowing rapid development and innovation.

Possible disadvantages of Presto

• Complex Setup
  Setting up and configuring Presto can be complex, requiring a fair understanding of its architecture and integration points, particularly for large-scale deployments.
• Resource Intensive
  Presto can require significant resources, especially memory, due to its focus on in-memory processing, which can be a constraint on resource-limited environments.
• Maintenance Overhead
  Managing and maintaining Presto clusters involves overhead in terms of monitoring, tuning, and upgrading, which can be demanding compared to hosted solutions.
• Community Support Limitations
  While it has an active community, Presto lacks the level of enterprise support and comprehensive documentation available from proprietary solutions, which can be a barrier for some organizations.
• Limited User Management
  Presto has limited built-in user management and security features, necessitating additional tools or systems to handle these aspects effectively.

MongoDB features and specs

• Scalability
  MongoDB offers horizontal scaling through sharding, allowing it to handle large volumes of data and enabling distributed computing.
• Flexible Schema
  It allows for a flexible schema design using BSON (Binary JSON), making it easier to iterate and change application data models.
• High Performance
  MongoDB is optimized for read and write throughput, making it suitable for real-time applications.
• Rich Query Language
  Supports a rich and expressive query language that allows for efficient querying and analytics.
• Built-in Replication
  Provides robust replication mechanisms for high availability and redundancy.
• Geospatial Indexing
  Offers powerful geospatial indexing capabilities, useful for location-based applications.
• Aggregation Framework
  Enables complex data manipulations and transformations using the aggregation pipeline framework.
• Cross-Platform
  Works on multiple operating systems, enhancing its versatility and deployment options.

Possible disadvantages of MongoDB

• Memory Usage
  MongoDB can consume a large amount of memory due to its use of memory-mapped files, which may be a concern for some applications.
• Complex Transactions
  While MongoDB supports ACID transactions, they can be more complex to implement and less efficient compared to traditional relational databases.
• Data Redundancy
  The flexible schema design can lead to data redundancy and increased storage costs if not managed carefully.
• Limited Joins
  Joins are supported but can be less efficient and more limited compared to relational databases, affecting complex relational data querying.
• Indexing Overhead
  Extensive indexing can introduce overhead and impact performance, especially during write operations.
• Learning Curve
  Requires a different mindset and understanding compared to traditional relational databases, which can present a learning curve for new users.
• Lacks Mature Analytical Tools
  The ecosystem for analytical tools around MongoDB is not as mature as those for traditional relational databases, which might limit advanced analytics capabilities.
• Cost
  The cost of using MongoDB's cloud services (MongoDB Atlas) can be high, especially for large-scale deployments.

Nike Presto React 2019 Review & On Feet

More videos:

MySQL vs MongoDB

More videos: