In-Memory Computing - The Big Picture

What if your database is the real bottleneck? See how a database-less architecture can be 1000x faster and cut cloud costs by up to 99%.

#1about 3 minutes

The critical need for performance in modern applications

Latency is a significant cost for businesses, making high-performance, in-memory computing essential for modern applications.

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Understanding the fundamental speed of in-memory operations

In-memory operations are orders of magnitude faster, measured in microseconds, compared to database access which is measured in milliseconds.

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The core problem of object-relational impedance mismatch

Object-oriented programming languages are inherently incompatible with relational database models, leading to complex and slow data mapping.

#4about 3 minutes

Why NoSQL and mapping layers don't solve the bottleneck

Even with NoSQL databases, the need for data conversion and mapping layers like ORMs persists, creating a significant performance bottleneck.

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Using distributed caches to reduce database load

A distributed cache cluster sits between the application and the database to store frequently accessed data in memory, reducing database load.

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Differentiating in-memory data grids from distributed caches

In-memory data grids extend distributed caches by adding computational capabilities, allowing for distributed processing across the cluster.

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The architecture and limitations of in-memory databases

In-memory databases run the DBMS in memory but often on a separate cluster, which still introduces network latency and requires data mapping.

#8about 4 minutes

A new paradigm: Database-less processing and system prevalence

The system prevalence architecture keeps the entire application state as an object graph in memory, leveraging native language APIs for ultra-fast queries.

#9about 3 minutes

Simplifying architecture and costs with Eclipse Store

Eclipse Store provides a persistence engine that stores the in-memory object graph directly to cloud blob storage, eliminating database clusters and reducing costs.