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World Congress 2025 • Aug 20, 2025 • Session details

Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases

Wei Hu

Are fragmented data stores bottlenecking your global scaling efforts? Discover how a distributed SQL platform unifies relational and AI workloads, ensuring massive scale and fault tolerance without vendor lock-in.

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#1 about 1 min

Defining the core mechanics of distributed databases

How distributed databases map data subsets across multiple locations while hiding topology from edge applications.

#2 about 2 min

Evolution from key-value stores to distributed SQL

Why organizations move away from basic key-value engines to structured distributed relational systems for complex processing.

#3 about 3 min

Architectural benefits of single logical database topologies

How federated requests and parallel processing enable global transaction distribution without dictating application architectural changes.

#4 about 2 min

Meeting compliance requirements with data sovereignty controls

Splitting information across boundaries satisfies local regulatory compliance policies while sustaining global transaction logic.

#5 about 2 min

Scaling operations and surviving single region failures

Architecting active-active data centers enables massive transaction volume processing paired with rapid automated recovery.

#6 about 3 min

Comparing native SQL clustering to NoSQL distribution layers

Layering relational SQL queries over baseline NoSQL frameworks creates unavoidable application performance bottlenecks.

#7 about 2 min

Defeating network latency in distributed system deployments

Utilizing data locality minimizes the physical tax associated with wide area network node traversal.

#8 about 2 min

Exploring value-based, system-managed, and composite sharding

Consistent hashing and value sharding map data topologies strictly to unique application usage requirements.

#9 about 2 min

Handling uneven workloads with user-defined placement strategies

Resolving data skew requires applying custom user-defined placement strategies over rigid hashing methodologies.

#10 about 2 min

Optimizing read limits with duplicated reference tables

Replicating global dimension tables onto every node guarantees local access while reducing cross-network queries.

#11 about 2 min

Ensuring survivability using raft-based synchronous replication

Synchronous raft-based replication ensures zero data loss during severe infrastructure outages using quorum consensus.

#12 about 2 min

Stabilizing performance despite unpredictable network jitter

Adaptive synchronous configurations automatically switch transmission behaviors in response to unpredictable connection latencies.

#13 about 3 min

Avoiding lock-in across multi-cloud and autonomous setups

Multi-cloud deployments prevent infrastructure vendor lock-in while machine learning autonomous routines lower maintenance complexity.

#14 about 4 min

Simplifying architectures through converged database platforms

Consolidating JSON, graphs, and AI vectors into a converged database simplifies complex application development.

#15 about 2 min

Design principles for optimizing distributed application structures

Minimizing secondary indexes and isolating disparate table requests optimizes core distributed transaction performance.

#16 about 2 min

Evaluating criteria for distributed SQL system adoption

Examining standard SQL completeness and flexible deployment topologies ensures productive modern architecture adoption.

Matching moments

1:14 min

Evolution of distributed SQL database architectures

Wei Hu Wei Hu · WWC 2024

1:52 min

Evolution of distributed SQL database management systems

Akmal Chaudhri Akmal Chaudhri · LIVE

2:25 min

Distributed SQL properties for agentic AI applications

Daniël van Eeden Daniël van Eeden +1 · WWC Europe 2026

3:40 min

Clustered databases versus native distributed databases

Wei Hu Wei Hu · WWC 2024

4:30 min

Introducing data management and the shift to streaming

Mary Grygleski Mary Grygleski · LIVE

3:23 min

Distributing data across multiple database servers with sharding

Tobias Petry Tobias Petry · WWC 2024

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