Amazon DynamoDB consulting for US app teams

Amazon DynamoDB is a serverless NoSQL database from AWS that stores items by key and returns them in single-digit milliseconds at almost any scale. Typical uses are shopping carts, orders, user sessions, game state, device data and the backend of serverless apps built on Lambda and API Gateway. It is a weak choice for ad-hoc reporting or joins across many tables.

MongoDB lets you query documents in many flexible ways and suits apps whose questions keep changing. DynamoDB asks you to design keys around known access patterns, and in return gives steady speed, no servers to run and pay-per-request pricing on AWS. If your team already runs MongoDB well and needs rich queries, staying can be right; if you want less to operate inside AWS, DynamoDB usually wins.

Choose a relational database on RDS or Aurora when your data has many relationships, you need joins and ad-hoc SQL, or reporting runs straight off the production database. Choose DynamoDB when reads and writes follow a known set of key lookups and traffic is large or spiky. Some apps use both, with DynamoDB for the hot path and SQL for reporting.

In US East (N. Virginia), on-demand mode is $0.625 per million write request units and $0.125 per million read request units. Provisioned mode is $0.00065 per write capacity unit-hour and $0.00013 per read capacity unit-hour. Storage is $0.25 per GB-month after the first 25 GB free, and point-in-time recovery adds $0.20 per GB-month.

Start new, unpredictable or spiky workloads on on-demand, which needs no capacity planning. Once traffic is steady and you can see the pattern in CloudWatch, provisioned capacity with auto scaling is often cheaper. You can switch a table between the two modes, so the first choice is not permanent.

Single-table design stores several entity types, such as customers, orders and invoices, in one table with generic key names, so related items can be fetched in one Query. It cuts round trips but makes the table harder to read for newcomers. We use it when patterns fetch related items together, and recommend separate tables when entities are read independently.

A hot partition happens when one key gets far more traffic than the rest, so requests to it get throttled even though the table has spare capacity. Each partition supports up to 3,000 read units and 1,000 write units per second. We avoid it with high-cardinality partition keys, write sharding for busy keys and caching for popular reads.

Yes, through code you own in AWS. Common patterns are Zoho CRM webhooks through API Gateway and Lambda into DynamoDB, DynamoDB Streams triggering Lambda to create invoices in Zoho Books, and DynamoDB as a cache with TTL to cut calls to the Zoho API. For simple hand-offs Zoho Flow can be enough.

Point-in-time recovery keeps continuous backups and lets you restore a table to any second within the recovery window, up to the last 35 days. On-demand backups are full copies you keep until you delete them. Both restore into a new table, so we plan and test how the app switches to it.

Talking through your tables on the first call is free. Each DynamoDB engagement after that, from a modeling workshop to a full move off MongoDB, gets one fixed price inside a signed BRD, along with dates for every phase. A big data move can call for a paid assessment, and we quote that before it begins. Your DynamoDB usage is billed by AWS straight to you.