DynamoDB development company
Krapton is a DynamoDB development company that models tables from your access patterns first, then builds the queries, Streams consumers and capacity settings around them, so read and write costs stay predictable as traffic grows on AWS.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
DynamoDB development services built on access patterns
Krapton works as a DynamoDB development company for teams building serverless backends, high-write event systems and multi-region products on AWS. DynamoDB rewards upfront design and punishes improvisation: a table with the wrong partition key throttles under load, and a missing access pattern can force a costly backfill or a new global secondary index. Our engineers start by listing every query the product needs, then choose between single-table and multi-table design, define composite sort keys, and decide which patterns belong on a GSI. From there we write the data layer with the AWS SDK for JavaScript v3 or boto3, add condition expressions for optimistic locking, and wire DynamoDB Streams into Lambda for search indexing, audit trails or change data capture. We also tell you when DynamoDB is the wrong fit: ad hoc analytics, complex joins and reporting usually belong in PostgreSQL, Athena or a warehouse fed by an export.
DynamoDB development services
Access pattern modeling
We map every read and write the product performs, then design partition keys, sort keys and GSIs so each request is a Query or GetItem rather than a Scan.
Serverless backend development
API Gateway or AppSync in front, Lambda functions in the middle and DynamoDB underneath, defined in AWS CDK, SAM or Terraform with least-privilege IAM policies scoped to individual tables and indexes.
Streams and event pipelines
DynamoDB Streams feed Lambda consumers or EventBridge Pipes for search indexing, notifications, aggregates and audit logs, with idempotent handlers, batch failure reporting and dead-letter queues for poison records.
Migration to DynamoDB
Moving tables from MongoDB, Cassandra or a relational database means redesigning keys, not copying rows. We script the transform, backfill through batch writes or S3 import, and dual-write during cutover.
Performance and cost tuning
We read CloudWatch and Contributor Insights to find hot partitions, choose between on-demand and provisioned capacity with auto scaling, trim item sizes, and add DAX where read-heavy traffic justifies a cache.
Multi-region with Global Tables
For products serving several continents we configure Global Tables, choose a consistency mode, design writes to avoid cross-region conflicts and rehearse failover by shifting traffic between regional endpoints.
Why teams choose Krapton for DynamoDB
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Design reviews before code
Every table design goes through a written access-pattern review with sample queries for each screen and job, so missing patterns surface on paper instead of after launch.
Honest fit assessment
If your workload needs ad hoc filtering, many-table joins or heavy reporting, we say so and propose PostgreSQL, Aurora or an analytics export alongside DynamoDB rather than forcing it.
Tested data access layers
Repository code runs against DynamoDB Local or a dedicated AWS test account in CI, with tests for condition failures, pagination with LastEvaluatedKey and transaction cancellation reasons.
Cost visibility from day one
We tag tables, set CloudWatch alarms on throttling and consumed capacity, and estimate read and write unit usage per feature so finance sees what each access pattern costs.
How a DynamoDB project runs with Krapton
01
Access pattern workshop
We list entities, relationships and every query with its expected volume, then agree which patterns need strong consistency, transactions or a secondary index.
02
Key and index design
Partition keys, sort key formats and GSI projections are drafted in NoSQL Workbench, checked against the pattern list and reviewed with your team before build.
03
Build and load test
Engineers implement the data layer and infrastructure as code, then run load tests that replay realistic key distributions to expose hot partitions and throttling early.
04
Launch and operate
We enable point-in-time recovery, deletion protection and alarms, document the table design, and hand over runbooks for backfills, index additions and capacity changes.
The DynamoDB stack we build with
DynamoDB development: questions answered
What does a DynamoDB development company actually deliver?
A DynamoDB development company delivers a working data layer, not just a table. That includes the access-pattern document, key and index design, the repository code that issues queries and transactions, infrastructure as code for tables, alarms and backups, and Streams consumers where the product needs events. Krapton also includes load-test results and a runbook explaining how to add a new access pattern without breaking existing ones.
When is DynamoDB the wrong choice for a new product?
DynamoDB is the wrong choice when you cannot predict your queries. Products still discovering their data model, back offices built around flexible filters, and anything needing multi-table joins or ad hoc SQL fit better in PostgreSQL. DynamoDB shines when access patterns are known and traffic is large or spiky: session stores, carts, IoT ingestion, leaderboards, user profiles and event logs. Many systems use both, with Streams feeding a relational or analytics store.
Should we use single-table design or one table per entity?
Use single-table design when related entities are fetched together and you want one Query to return them; use separate tables when entities have different lifecycles, access controls or capacity needs. Single-table design reduces round trips but makes the schema harder to read and evolve. We often settle on a middle path: one table per bounded context, with libraries such as ElectroDB keeping key formats consistent.
How do you handle hot partitions and throttling at scale?
Hot partitions are fixed by spreading writes across more partition key values, not by buying capacity. We find the offending keys with CloudWatch Contributor Insights, then apply write sharding with a key suffix, aggregate counter updates through a Streams consumer, or cache hot reads in DAX. Adaptive capacity helps with uneven load, but a single key still has per-partition throughput limits, so design changes matter more than settings.
What decides the cost of a DynamoDB development project?
Cost is decided by scope and data complexity, not by the database itself. The main drivers are the number of access patterns and entities, whether you are migrating existing data and need dual writes, multi-region requirements, the volume of Streams-driven integrations, and compliance needs such as encryption with customer-managed KMS keys and audit logging. AWS usage is billed to your account separately, and we estimate it per access pattern during design.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.