Data Panda lifts the items behind your Lambda functions and serverless apps out of DynamoDB and into a SQL warehouse, on a schedule the business can trust. Once the partition-keyed data sits next to your CRM and accounting tables, dashboards, AI workflows and internal apps can all query it without a developer writing a one-off Scan.
Amazon DynamoDB went generally available in January 2012, built on the ideas in the 2007 internal Dynamo paper from Amazon. It is a fully managed, key-value and document database run by AWS, with single-digit-millisecond read and write latency at any table size. Data is organised in tables of items, addressed by a partition key and an optional sort key, with Local Secondary Indexes and Global Secondary Indexes to support extra access patterns. Capacity comes in two modes: provisioned (you set read and write units) or on-demand (you pay per request and AWS handles the scaling).
DynamoDB is the default operational store for serverless apps on AWS. A Lambda function reads and writes items in milliseconds, DynamoDB Streams emit a change feed for downstream consumers, and Global Tables replicate writes across AWS regions for active-active multi-region apps. PartiQL adds a SQL-like query surface on top of the same tables. The trade-off the AWS docs themselves call out: DynamoDB is built for known access patterns at scale, not for ad-hoc analytics. That is why most teams pair it with a warehouse: the operational items live in DynamoDB, and an export (via S3 export, Streams, or scheduled sync) makes the same data joinable in SQL alongside the rest of the business.
DynamoDB items flattened into warehouse tables, joined to CRM and finance.
Let DynamoDB Streams trigger work across the rest of the stack.
Score serverless app data with models without exporting items by hand.
Admin tools on DynamoDB data without exposing AWS keys to the team.
A list of concrete reports, automations and AI features we have built on DynamoDB (AWS) data. Pick the one that matches your situation.
How do we report on DynamoDB without firing a full Scan in production?
We do not Scan the live tables for analytics. The default path is DynamoDB Streams or scheduled S3 exports landing into the warehouse, where business users query in SQL. Production read units stay free for the app.
Our team uses single-table design. Can the warehouse still make sense of that?
Yes. Items in a single-table-design model are split by entity type and partition prefix into SQL tables that look the way analytics expects. The original table stays untouched, the warehouse layer carries the relational shape.
How do we handle multi-region Global Tables in one report?
Writes from each AWS region are unioned into one warehouse view, with the source region kept as a column. Reporting sees a single global table without losing the audit trail of where each write originated.
App-generated revenue and usage from DynamoDB joined to the accounting ledger, without a developer running ad-hoc exports each month-end. Subscription and transactional revenue end up on the same customer record.
Product usage and feature adoption from the serverless app on every CRM account. Reps see signals from DynamoDB on their pipeline view in SQL, not in a JSON dump.
DynamoDB capacity, throttling, GSI usage and stream lag tracked alongside business KPIs. Cost per feature and tenant becomes a number finance can read, not a CloudWatch graph.
You keep the reporting tool you already have. We connect it to the warehouse where your DynamoDB (AWS) data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Both are supported. DynamoDB Streams give a near-real-time change feed and are the default for tables where freshness matters. Scheduled S3 exports are the pragmatic choice for large historical tables where a daily snapshot is enough. The two paths land in the same warehouse model.
Items are flattened per attribute, with map and list attributes modelled as related tables. New attributes appear as columns on the next sync, type changes are versioned, and renamed attributes are mapped to their history so existing dashboards keep running.
We review your DynamoDB (AWS) setup and the systems around it. Together we pick the first thing worth building.