Single service comparison

DynamoDB exposes a proprietary AWS SDK API with SDK-level constructs (partitions, GSIs/LSIs, conditional writes, auto scaling) and a serverless pay-per-request billing model. Huawei GeminiDB is an Ustore/Cassandra-compatible distributed NoSQL engine offered in multiple API flavors (Cassandra, Redis, MongoDB, InfluxDB, openCypher) on shared cloud-native infrastructure, and DDS is a separately managed MongoDB-compatibility cluster. Neither offers DynamoDB's exact API, so applications must rewrite data-access code rather than re-point a driver.

Scaling and HA differ structurally. DynamoDB auto-partitions and auto-scales transparently with no node sizing, offering single-table global active-active via Global Tables. GeminiDB and DDS are cluster-based services where you select node flavors, shard/replica counts, and instance topology; scaling is storage-expandable but compute scaling often requires node changes or restarts. Consistency also differs: DynamoDB defaults to eventual with optional strong per-item reads, while GeminiDB/DDS follow their respective engine's tunable consistency (MongoDB read preference, Cassandra tunable consistency).

Integration and operational responsibility diverge. DynamoDB couples natively with AWS Lambda, IAM, Kinesis, Streams, Backup, and CloudWatch with the cloud handling patching, partitioning, and durability. On Huawei you retain more operational choice: DDS/GeminiDB integrate with IAM/CTS/CES and Data Ingestion Service but you manage index design, instance class selection, parameter templates, and cross-region DR patterns. DRS offers heterogeneous DB migration but does not provide a DynamoDB-specific connector; DynamoDB Streams/Global Tables have no direct Huawei parity and must be re-architected.

Start with a workload profile assessment: access patterns (key-value vs document), item size, read/write ratio, consistency needs, GSIs/LSIs, TTL, streams, and global-table requirements. Pick the target by data model: GeminiDB for Cassandra/Redis-style key-value/wide-column workloads or DDS for MongoDB document workloads—neither replicates DynamoDB's API directly, so a translation/adapter layer or code rewrite is required. There is no documented one-click DynamoDB-to-Huawei path; treat re-platforming, not lift-and-shift, as the baseline.

Re-model the data onto the chosen engine: convert DynamoDB tables, partition keys, sort keys, GSIs/LSIs, and condition expressions to the target's schema/index model (Cassandra primary/clustering keys or MongoDB indexes/shard keys). Rewrite application access code against the target SDK and adapt transactions, atomic counters, and streams to DDS change streams or GeminiDB CDC/options. Use Huawei DRS for any homogeneous relational/MongoDB staging sources where a connector is documented, and DLI/DIS for streaming integration; validate connector support for your specific source before relying on it.

Migrate data via export/transform/load: export DynamoDB to S3 (or AWS Data Pipeline/DMS export to S3 for MongoDB-shaped targets), transform to the target's load format, and ingest with the engine's bulk loader (e.g., mongoimport for DDS, CQL loader/Cassandra SSTable load for GeminiDB). Replay recently captured changes via DynamoDB Streams/replication to a staging cluster during a defined cutover window. Validate row counts, secondary indexes, read latency under peak RCU-equivalent load, and conditional-write semantics before declaring parity.

Re-architect cross-cutting features that lack direct parity: DynamoDB Streams, Global Tables multi-region active-active, auto-scaling, and on-demand capacity. Replicate streams with DDS change streams or GeminiDB CDC feeding DIS/DLI, and build DR with cross-region backups/replicas rather than active-active. Expect a cost-model shift from DynamoDB RCU/WCU + storage + backup to DDS/GeminiDB node/flavor billing plus storage, backup, and DRS task fees; re-model TCO against peak request volume, item size, retention, and cross-region traffic rather than assuming savings.