Data Panda lands the data the rest of your business produces into Amazon Redshift and curates one model the dashboards, automations, AI workflows and internal apps build on. Reporting, scheduled jobs and ad-hoc SQL come off the same warehouse, not three copies of it.
Amazon Redshift launched in February 2013, built on top of MPP technology Amazon licensed from ParAccel and modelled on an early PostgreSQL fork. It runs in two shapes: Provisioned, where you size RA3 nodes (compute) against managed storage on S3, and Serverless, where you pay per Redshift Processing Unit (RPU) by the second and AWS scales the compute for you. Both share the same SQL surface and the same storage layer.
What makes Redshift the natural pick for AWS-first teams is what sits around it. Spectrum reads Parquet, ORC and JSON straight out of S3 without loading first. Zero-ETL integrations stream Aurora, RDS and DynamoDB changes into the warehouse without a separate pipeline. Federated Query joins Postgres or MySQL tables live. Redshift ML calls SageMaker from SQL. If your application data, your event logs and your data science already live in AWS, the warehouse stops being a separate destination and starts being the read model on top of what is already there.
Curated Redshift schemas feed every BI tool from one definition.
Operational triggers and exports run off the same Redshift model the dashboards use.
Redshift ML and Amazon Q work against the curated tables, not raw dumps.
Internal tools read from Redshift via the Data API and write back to the same model.
A list of concrete reports, automations and AI features we have built on Amazon Redshift data. Pick the one that matches your situation.
Are we paying Serverless RPU rates for workloads that would be cheaper on RA3, or the other way around?
A read of the Serverless usage logs against the actual query mix shows where the workload is steady enough to fit a sized RA3 cluster, and where the bursty pattern justifies Serverless. Most warehouses started on one model and never tested the other; a quarterly check on the split usually shifts a meaningful slice of the bill.
Which of our slow Redshift queries are slow because of the data, and which because of the table design?
EXPLAIN plans plus SVV_TABLE_INFO show where queries pay for a network shuffle on the wrong distkey, scan a sortkey that no filter uses, or skew across nodes. Reworking distribution and sort on the top ten heaviest tables usually moves more queries than buying more compute does.
Where are we copying data into Redshift that Spectrum could read in place from S3?
An audit of the load jobs against the S3 lake shows historical Parquet partitions that are loaded once and queried rarely. Moving those out of managed storage and into a Spectrum external schema cuts the storage line and the load pipeline in one move, with the same SQL surface for the dashboards.
One reconciled revenue and margin layer in Redshift that the board pack, the controllers and the FP&A models all read. Month-end stops being a debate about whose extract is fresher, and the AWS bill for storage and compute becomes one line you can model against actual reporting load.
Salesforce or HubSpot opportunity data joined with Aurora-based application usage and S3 event logs in one Redshift model. Pipeline coverage, win-rate and product engagement come off the same numbers, and reverse ETL pushes the curated scores back into the CRM where reps work day to day.
RA3 sizing, Serverless RPU caps, distribution-key health and Spectrum spend become observable in one place. Ops teams stop debugging ad-hoc COPY jobs from a dozen lambdas and start reviewing one model layer with one schedule.
You keep the reporting tool you already have. We connect it to the warehouse where your Amazon Redshift 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 have a place. Serverless bills per Redshift Processing Unit by the second and is the simpler default for spiky or unpredictable workloads. RA3 nodes bill per hour and become cheaper once the warehouse is steadily busy enough to keep them warm. We size based on the actual query mix and revisit the choice quarterly, since the right answer shifts as the workload grows.
Spectrum reads Parquet, ORC and JSON straight out of S3, so cold and historical data can stay there instead of in managed storage. The split we usually land on is hot, frequently joined data inside Redshift; older partitions and append-only event data exposed as Spectrum external tables. The same SQL queries both, so the dashboards do not change.
Yes. ALTER TABLE supports changing distribution style and sort keys on an existing table, and CREATE TABLE AS rebuilds when an in-place change is not feasible. The catch is that picking the wrong keys early can leave a query slow for months before anyone connects the symptom to the design, which is why a quarterly review on the heaviest tables is usually a better investment than more compute.
We review your Amazon Redshift setup and the systems around it. Together we pick the first thing worth building.