Eventual Consistency

What is Eventual Consistency?

Eventual Consistency is a database system concept that allows for temporary inconsistencies between replicas during updates. In an eventually consistent system, all replicas of data will eventually achieve consistency without the need for rigid, immediate synchronization.

Functionality and Features

Eventual Consistency is primarily used in distributed systems where low latency and high availability are prioritized over perfect consistency. Features include:

  • Low latency: Since updates do not need to wait for synchronization, response times can be improved.
  • High availability: Data can be accessed even if some replicas are not updated.
  • Scalability: It supports a large number of nodes for write operations.

Benefits and Use Cases

Eventual Consistency is beneficial when high availability, partition tolerance and scalability are more important than consistency. It's frequently employed in large-scale web applications, such as social media platforms, where immediate consistency is not as crucial.

Challenges and Limitations

One major limitation is that it may lead to stale or conflicting reads. The system may also require complex conflict resolution strategies if different replicas receive updates in different orders.

Integration with Data Lakehouse

Eventual Consistency can complement a Data Lakehouse setup in situations where there is a need for high availability and low latency. However, for analytics and data science purposes where accurate, up-to-date data is critical, stronger consistency models like those provided by Dremio's technology may be more appropriate.


What is Eventual Consistency? It is a consistency model used in distributed systems which assures that, given enough time, all accesses to a particular data item will return the last updated value.

Where is Eventual Consistency commonly used? It is commonly used in large scale web applications like social media platforms, e-commerce sites etc.

What are the limitations of Eventual Consistency? It can lead to stale or conflicting reads and may require complex conflict resolution strategies.

How does Eventual Consistency fit into a data lakehouse setup? While it can complement a Data Lakehouse setup in situations requiring high availability and low latency, for analytics and data science purposes where accurate, up-to-date data is critical, stronger consistency models may be preferred.


Distributed Systems: A system where components located on different networked computers communicate and coordinate actions by passing messages. 

Data Replica: A copy of data that is stored separately to enhance accessibility and availability. 

Data Lakehouse: A new, open architecture that combines the best elements of data warehouses and data lakes. 

Consistency Model: Defines how and when changes made by an operation become visible to other threads. 

High Availability: A characteristic of a system that aims to ensure an agreed level of operational performance, usually uptime, for a higher than normal period.

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