Data Panda brings the Redis caches and key spaces behind your applications together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
Redis is an in-memory key-value store that holds caches, sessions, queues, leaderboards, pub/sub channels and vector indexes for the application sitting in front of it. It speaks a small set of data structures (strings, hashes, lists, sets, sorted sets, streams, geospatial, HyperLogLog) and serves them in sub-millisecond round trips, which is why it ended up in front of almost every web app, mobile backend and microservice that needs a fast operational layer.
The point of pulling Redis into a warehouse is not to query the cache itself. It is to make the operational layer visible: which keys get hit and which get evicted, how big the queue backlog is by hour, which sessions are alive per tenant, what the cache hit rate looks like per route. That data lives next to Salesforce revenue, Stripe billing and the application's Postgres or MongoDB record, and the questions about cost-per-DAU on the cache and queue suddenly have an answer in the same place finance asks them.
Cache, queue and session telemetry joined to revenue and CRM in SQL.
Let Redis state changes fire actions across the rest of the stack.
Use the cache layer's signal to score behaviour and shape AI features.
Internal tools that read Redis telemetry without raw cluster access.
A list of concrete reports, automations and AI features we have built on Redis data. Pick the one that matches your situation.
What is our Redis costing us per active user?
Cache and queue spend joined to active-user and tenant counts in the warehouse, so the cost line gets a denominator. Reveals which tenants are overusing the cache, which routes are hot, and where a smaller maxmemory or a different eviction policy would save money without breaking the app.
Why did the application get slow on Tuesday?
Cache-hit rate, eviction count, queue depth and replication lag plotted on the same timeline as deploys and traffic, with the application's Postgres or MongoDB metrics next to it. The Tuesday slowness story stops being a guess and becomes a chart.
Which keys and patterns are growing faster than the cache plan allows?
Key-space sprawl tracked by prefix and size, with growth rate against the maxmemory budget. Flags the prefix that quietly doubled last sprint and is now causing eviction of keys you wanted to keep warm.
Cache and queue spend tied to active users, tenants and revenue. The Redis bill stops being a flat infrastructure line and starts having a per-customer denominator finance can defend in the budget review.
Per-tenant usage of the cache and queue surfaced on the CRM account. Account managers can talk concretely about plan limits, overage and upgrade triggers instead of waiting for engineering to dig out a number.
Cache-hit, eviction, queue-depth and replication-lag history kept in one place across deploys and traffic spikes. The on-call post-mortem starts from a chart, not from a hunch.
Cache-hit, eviction, queue-depth and session-count metrics from Redis land in BigQuery alongside application events from Postgres or MongoDB. Cost-per-DAU, hot-key analysis and queue-lag trending all run in SQL, on the same warehouse the rest of the business uses.
Queue-backlog over threshold, eviction spikes on critical key classes and replication-lag breaches push into Slack as actionable messages with the affected tenant and route attached. The on-call rotation gets context, not just a red dot from a monitoring tool.
Cache-miss and eviction events from Redis line up next to PostHog product events on the same user and session id. Slow-feature investigations stop bouncing between tools and the team sees whether the slow checkout was a cold cache or a slow query downstream.
Cache size, queue throughput and session count per tenant land on the HubSpot company record as custom properties and timeline events. CS sees which accounts are pushing plan limits before the support ticket arrives, and account managers have a real number for the upgrade conversation.
You keep the reporting tool you already have. We connect it to the warehouse where your Redis 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.
Key-space patterns are inferred from prefix and value type, not from a declared schema. We sample by prefix, classify the value structure (string, hash, list, set, sorted set, stream) and project per-pattern metrics like count, size and TTL into warehouse tables. The tables are the patterns the application writes day to day, not a guess.
Redis moved from BSD to a dual RSALv2 / SSPLv1 license in 2024, which prompted the Linux Foundation to fork Valkey from the last BSD-licensed version. The connector reads the wire protocol both projects share, so the warehouse pipeline works against either Redis Cloud, self-hosted Redis under the new license, or a Valkey deployment without a code change on our side.
We review your Redis setup and the systems around it. Together we pick the first thing worth building.