This article is the first in a two-part series on rethinking the reporting platform. Here, we focus on why traditional reporting architectures are breaking down as reporting becomes more operational, distributed, and tightly governed.
Data doesn’t just serve the business anymore. Data is the business. Every transaction, every decision, every customer experience depends on having timely, trustworthy insight – not weekly, not daily, but continuously.
A reporting platform has become the connective tissue between data and decision: not a passive destination, but an active system that drives business intelligence forward. And it’s under strain. As data volumes explode, regulations grow more specific, and expectations shift from static dashboards to AI-powered interactivity, most reporting architectures are struggling to keep up.
This is not because of poor planning: It’s because the architecture that once made sense is no longer enough. This article explores how CockroachDB enables a fundamentally better foundation for modern reporting platforms – not by adding another system, but by treating reporting correctness, availability, and governance as traits of the operational database itself.
A Data architecture that made sense – until the requirements changed
What is a reporting platform? It’s a software system that collects, organizes, and presents data so it’s easy to analyze and share. Reporting platforms transform raw information into dashboards, charts, and reports. These powerful data visualizations help businesses to track performance metrics, spot trends, and make data-driven decisions.
Reporting platforms go way beyond the classic spreadsheet – they often integrate with multiple data sources and provide real-time or automated updates. Think of it as a personal data assistant that's always organizing your numbers, and presenting them in an actionable way.
For years, reporting platforms weren’t designed – they were assembled out of disparate systems that might include:
a transactional database for capturing business events
warehouse for aggregating metrics
cache to serve dashboards
a pipeline to move data between them
document store for flexibility
queue for ingest
a search index for exploration
Each system solved a specific problem. Each addition made sense at the moment. What these systems never encoded was intent: Which data was operational, which was analytical, which was historical, and which workloads should be isolated rather than arbitrated.
But collectively, they created a new kind of complexity: a Rube Goldberg machine of duct-taped components, fragile interfaces, duplicated logic, and ambiguous ownership. As the demands on reporting grew – more data, more regulation, more real-time requirements – these architectures began to show their age.
The symptoms are familiar:
Inconsistent definitions. Metrics calculated in the warehouse don’t match those in the dashboard. Definitions drift. Trust erodes.
Latency and lag. Pipelines introduce delay. Sync jobs fail silently. Users act on stale data without knowing it.
Change resistance. A simple schema update becomes a multi-day regression cycle across systems. Updates get deferred. Workarounds multiply.
Operational drag. Every component has its own deploy, monitor, and patch cycle. SLAs slip between the cracks. Outages become multi-team incidents.
Talent silos. SQL here, Spark there, NoSQL over there. Debugging becomes archaeology.
None of this happens because teams are careless. It happens because the underlying architecture – even if thoughtfully assembled – wasn’t built to behave like a single, coherent platform.
As Cockroach Labs CEO Spencer Kimball put it: “We routinely find customers who run 40 different databases in production...not one of them wants to add a 41st database! Understandably, they instead want a database platform which holds the promise of consolidation, and a future where there are far fewer databases in production.”
Put another way, what they’re really asking for isn’t fewer tools, but a platform that can absorb more responsibility without forcing teams to rebuild correctness elsewhere.
The role of reporting has changed. It’s no longer just a passive window into the past. It’s part of how products operate, how decisions are made, how regulators get satisfied, and how AI systems stay grounded. It’s operational, real-time, mission-critical – and no longer something that can be treated as downstream.
It’s time the underlying architecture caught up.
Related
O'Reilly's CockroachDB: The Definitive Guide
Learn how to design resilient, multi-region data architectures that serve both operational workloads and real-time reporting.
As reporting becomes more real-time and more tightly coupled to operational workflows, two architectural paths are emerging. One approach starts from analytics platforms and attempts to “operationalize” them – embedding transactional stores, metadata layers, or lightweight OLTP engines inside systems originally designed for batch processing, pipelines, and analytical isolation. In this model, correctness, freshness, and lifecycle semantics are reconstructed through ingestion guarantees, replication logic, and coordination across subsystems.
In practice, the difference is not about feature sets or deployment models;it’s about where responsibility lives. Operationalizing analytics platforms asks downstream systems to approximate operational truth. Treating reporting as an operational property ensures that truth is never reconstructed – because it never leaves the system of record database in the first place. New access patterns tend to attach themselves to that truth rather than replace it, increasing the cost of fragmentation when responsibility is split across systems.
How do you build resilient reporting without downtime?
Downtime used to be tolerated. Reporting systems ran behind the scenes, crunched overnight batches, and operated on the assumption that if something broke, the world could wait. That assumption no longer holds.
Modern reporting platforms sit on the critical path. They feed AI pipelines, surface compliance alerts, and shape high-stakes business decisions in real time. And that means resiliency isn’t a bonus – it’s a baseline requirement.
CockroachDB delivers resilience by design – and that resilience applies equally to transactional and reporting workloads because they operate on the same system, under the same guarantees. It’s a distributed SQL database where every node is a peer – there are no standby replicas, external failover mechanisms, or coordination services to manage. High availability and consistency are built into the system itself, powered by Raft consensus. If a node goes down, the cluster rebalances automatically. If a zone or region fails, data remains accessible and consistent – with no manual intervention, failover scripts, or service disruption. The system stays online, and the data stays correct.
And resiliency doesn’t stop at disaster scenarios. CockroachDB handles day-to-day evolution with the same continuous availability. Schema changes execute online, in the background, without locking tables or halting ingestion. Software upgrades roll forward node by node – no maintenance windows, no system freezes. Operations that typically require downtime in other databases simply don’t need it all with CockroachDB.
Even under failure, CockroachDB guarantees correctness. Its distributed consensus protocol ensures that every write is committed safely and applied one time. Its use of multi-version concurrency control (MVCC) lets long-running queries operate against a stable snapshot of the world – which is exactly what reporting workloads require. Whether a transaction executes during a failover, a schema change, or a quiet Tuesday morning, the result is the same: a globally consistent, serializable view of the data.
This level of built-in resiliency shifts the operating model for reporting teams. There's less duct tape, fewer fire drills, and more confidence: Engineers aren’t writing custom logic to protect dashboards from flaky replicas, stalled pipelines, or partially failed downstream systems. They’re not coordinating schema freezes across systems. They’re building, and trusting the database to handle the rest.
The result: Resilience is no longer something reporting platforms need to bolt on. With CockroachDB, it's already there – woven into the fabric of the system, always on, always correct.
How do you support global ingest and global reporting?
The dream of real-time global reporting often dies in the pipeline.
Data arrives in one geography, but it’s needed in another. Ingest happens close to the customer, but reports run elsewhere. Teams end up stitching together regional databases, data movement jobs, and cache layers – each piece solving a local problem, while multiplying global complexity.
CockroachDB addresses this at the foundation. It’s a single, distributed SQL database that spans regions natively – not as an afterthought, but as a core design principle. That means data can be written close to where it’s generated, and read from wherever it’s needed, under a single logical model – without sacrificing consistency, freshness, or performance.
