Community Question
What are Elastic Tables in Microsoft Dataverse and when should they be used?
Share knowledge. Learn from experts. Build together.
Question
Elastic Tables in Microsoft Dataverse are designed for scenarios involving very high data volumes, high-throughput workloads, and flexible scaling requirements. They use a distributed data architecture that is different from standard Dataverse tables and are particularly useful when applications need to ingest or process large amounts of data quickly.
They can be considered for scenarios such as telemetry, transaction streams, large-scale operational data, and workloads where horizontal scalability is more important than some of the relational capabilities typically associated with standard Dataverse tables.
Answers
Elastic Tables in Microsoft Dataverse are designed for scenarios that require high-volume data ingestion, flexible scalability, and distributed processing. They are particularly useful when applications need to handle large quantities of records and high-throughput workloads that may not fit the typical transactional pattern of standard Dataverse tables.
Elastic Tables are well suited for scenarios such as telemetry, IoT data, application logs, high-volume transactions, and other workloads where data is continuously generated at scale.
A key architectural difference is that elastic tables are optimized for scale and distributed workloads, rather than complex relational transactions across many tables. Therefore, developers need to carefully consider partitioning, query patterns, consistency requirements, and transaction behavior.
In practice: Use elastic tables when high volume and scalability are primary requirements. Use standard Dataverse tables when the application depends heavily on relational data, complex business transactions, and traditional Dataverse capabilities.
Your Answer
Login required
Please login to participate in this discussion
and post your answer.