Arc vs. Conventional Databases

Today, the world of databases offers a multitude of options. Relational Databases have reached maturity and excel in handling complex SQL queries, making them a powerful tool. However, a common pitfall is attempting to use them for all types of data, which might not always be the best approach.

On the other hand, NoSQL databases have evolved significantly and now offer cost-effective solutions for storing massive volumes of data. They come in various flavors, each tailored to address specific challenges, particularly in fields like IoT and Healthcare. These diverse NoSQL databases are designed to cater to the unique requirements of these industries.

The Search revolution began with Google, and when it comes to search functionality, using search indexes rather than traditional databases is the optimal choice. While distributed caching effectively addresses the challenge of scaling read operations, it's important to note that caching serves as a temporary read-only space, not a permanent data store.

Database	Advantages	Disadvantages
Relational Databases	Data Integrity Advanced SQL Capabilities ACID Transactions Maturity Structured Schema	Complex and expensive to scale Low performance for high-velocity, high-volume data Complex to design and manage - especially for large and evolving datasets Inflexible schema which hinders agility High licensing and maintenance costs
NoSQL Databases	Excellent Horizontal scalability, suitable for high-velocity, high-volume data Agility because of schema-less or schema flexible nature High performance for read and write operations Simple to setup and maintain Supports a variety of data models including document, key-value, column-family, and graph databases	Prioritizing performance over data integrity, leads to inconsistencies Limited SQL capabilities Lack of ACID transactions Low Maturity compared to Relational databases May require thoughtful schema design to optimize performance in some scenarios, which can be complex
Distributed Caching	High Performance read and writes Horizontal scalability to accomodate increased traffic and data volumes Data Consistency and High Availability via replication and clustering Variety of Data Types supported Low-latency data retrieval for enhanced application performance	Data is Volatile leading to increased load on database when there is data loss Data Size Limitations Complex Configuration Infrastructure and Operational Costs to expand and maintain Data Eviction from cache eviction policies can impact performance
Search Systems	Efficient Searching Scalability Advanced features for ranking search results based on relevance Support for Structured and Unstructured Data Integration with AI and Machine Learning	Complex Configuration Expensive Licensing and Infrastructure costs Learning Curve Data Synchronization with primary data source is challenging Substantial resource requirements for larger datasets and complex search scenarios
Arc	All the advantages of Relational + NoSQL + Search + Distributed Caching without having to integrate them yourself.

In the evolving landscape of database solutions, many emerging companies attempt to create databases that promise to be all-encompassing. However, we firmly believe that such an approach often leads to solutions that struggle to excel in any particular area and end up performing all tasks suboptimally.

Our approach is an integrated one, designed to harness the strengths of each technology while cleverly avoiding their individual weaknesses. We act as the cohesive force that binds all the components together to create a fully-integrated architecture, where the whole is greater than the sum of its parts.