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.
Under the hood, CockroachDB uses geo-partitioning and quorum-based consensus to ensure that writes are acknowledged quickly – often within the local region – while still maintaining global consistency. Reads are served from nearby replicas when possible, minimizing latency, and always reflect a consistent view of the data. This architecture eliminates the need for ETL pipelines to shuttle data across regions, avoids fragmented query logic, and removes the traditional tradeoff between geographic performance and transactional correctness.
The result is a platform where global ingestion and global consumption are not two separate systems joined by pipelines – they’re two sides of the same architecture. For example, when an application writes to CockroachDB in Sydney, a dashboard in Frankfurt can query that data in real time and trust that it’s current, complete, and correct. When an ML model in Virginia consumes customer behavior logs from Tokyo, it’s not reading from a shadow replica or a lagging cache, it’s reading the real thing.
This architecture doesn't just improve performance. It changes how reporting systems are built, removes whole classes of coordination logic, and eliminates the need for “eventual” anything. It also gives teams a consistent, low-latency interface to work with, regardless of geography.
For organizations operating at global scale, the question is no longer, “How do we move the data?” Instead, it can be, “Why are we moving it at all?” In most global reporting stacks, data movement exists to compensate for architectural boundaries, not business requirements. CockroachDB makes it possible – and practical – to leave the data where it belongs, and still see the full picture.
How do you enable real-time, always-correct reporting?
The expectation for reporting has changed. Once considered a passive reflection of the past, reporting platforms are now expected to operate in real time – reacting to live transactions, informing decisions as they happen, and delivering immediately usable, consistent insight.
But most data architectures weren’t built for the demands of reporting. They batch, buffer, lag, and when you need an alert to trigger, a model to update, or a decision to land on the right number the “eventual consistency” that results isn’t just a technical compromise, it’s a business risk.
CockroachDB removes the need to choose between correctness and availability for reporting workloads. It’s a distributed SQL database that delivers strong consistency by default, even across regions. Every transaction – whether it’s a write from an ingest pipeline or a read from a reporting tool – operates on a fully consistent snapshot of the system. There’s no replication lag. No read-after-write gaps. No stale cache to invalidate.
This matters not just for dashboards, but for everything they feed. Risk engines, customer notifications, and automated workflows all depend on knowing that what they’re seeing reflects reality right now. With CockroachDB, a transaction committed in one region is immediately visible to consistent reads across the cluster, without polling, syncing, or retry logic. Reporting is no longer a downstream consumer of truth: It’s operating on the same substrate as the system of record.
Performance, meanwhile, comes built in. CockroachDB’s distributed architecture allows data to be written and read close to where it’s produced or consumed, which reduces cross-region latency without sacrificing consistency. Queries can run against operational data without competing with transactional throughput, because execution and access paths are explicitly separated. There’s no need to export data to make it usable, since the answer is already there.
The end result is a reporting system that doesn’t trail the business – it keeps pace with it. Real-time isn’t a feature. It’s the baseline.
Storage That Serves Both Sides
Every reporting system faces the same balancing act: it needs to support fast ingest and frequent updates while also powering high-volume reads, aggregations, and exploratory queries. Most architectures respond by splitting responsibility across systems: a transactional database captures operational writes, while a separate reporting or aggregation layer serves read-heavy workloads, with pipelines continuously moving and reshaping data between them.
CockroachDB takes a different approach to data storage. It’s designed to support both patterns against the same data, without requiring a separate analytical system or replicated copy, thanks in large part to its storage architecture.
Underneath the SQL layer, CockroachDB stores data in a row-oriented layout, tuned for high-throughput transactional workloads. But it’s not a basic key-value store – it’s smarter than that. A few storage-level design choices make this possible:
The database groups related columns into column families, minimizing disk I/O for read-heavy queries and reducing the cost of scanning large datasets. The same system that handles a spike in concurrent inserts can also serve complex queries without reaching for a separate analytical tier.
Indexing plays a big role here, too. With CockroachDB’s STORING clause, secondary indexes don’t just help with lookups – they can satisfy entire queries. That means fewer table scans, faster response times, and less pressure on primary storage, even as query volumes climb.
The execution engine builds on that foundation. CockroachDB uses vectorized processing to operate on batches of rows in memory, accelerating aggregations, joins, and transformations. That efficiency scales with concurrency, making it well-suited for reporting workloads that need to run alongside transactional ingest without competing for correctness or availability.
Beyond performance, the storage engine is flexible. CockroachDB supports rich data types that extend the reach of reporting platforms into previously siloed workloads. These include JSON for semi-structured records, geospatial types for location-aware analytics, user-defined types for domain-specific models, and vectors for powering semantic search and ML-driven applications.
The result is a system where developers don’t need to choose between transactional integrity and analytical responsiveness. Schema design can follow real-world modeling needs, not the quirks of a dual-database stack. Reports can run on operational data – without requiring exports, replicas, or cache layers – and without disrupting ingestion. Dashboards don’t need their own cache. And as data grows, performance stays predictable.
CockroachDB’s storage engine doesn’t just store data – it elevates it, making it usable across the full spectrum of reporting demands without fragmenting the system.
How do materialized views improve reporting performance?
In traditional reporting architectures, performance comes at a price. To serve complex queries quickly, teams add caching layers, roll up tables, or pre-aggregate metrics in separate pipelines. Each workaround introduces more systems to monitor, more logic to maintain, and more distance between raw data and the answers the business actually needs.
CockroachDB takes a simpler route: With native support for materialized views, reporting teams can define complex queries once and let the database manage their lifecycle – no orchestration required, no cache to warm up, no logic to reimplement in downstream tools.
Materialized views in CockroachDB behave like derived, managed structures. They store the results of expensive joins, filters, and aggregations, and can be refreshed either manually or on a defined schedule. This gives teams control over the tradeoff between freshness and cost, whether you need hourly KPI snapshots or near-real-time rollups for dashboards.
But materialized views in CockroachDB aren’t just a shortcut – they are deliberate architectural surfaces that are fully integrated into the system’s indexing and query planning. Views can have their own primary and secondary indexes, which means performance can be tuned to match usage patterns, so reporting-specific access patterns can be optimized without affecting transactional ingest or lifecycle operations. This makes it possible to optimize for both write-heavy ingest and read-heavy analytics – in the same system, on the same data.
The impact is twofold: reports get faster, and reporting semantics stay anchored to the same source of truth – without introducing new systems to reconcile. There’s no need to offload heavy queries to another database. No nightly ETL to transform raw logs into usable metrics. No cache invalidation cycle to troubleshoot when numbers don’t match.
Materialized views let you meet performance goals without compromising correctness, consistency, or operational simplicity. And because they’re built into the same infrastructure that guarantees consistency and resilience, they behave like the rest of CockroachDB – reliable, predictable, and operationally safe.
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How can you evolve schemas without breaking reporting?
Reporting platforms live in a constant state of evolution. Business teams introduce new KPIs, while compliance adds new regulatory dimensions. Product launches redefine how data needs to be tracked and sliced.
In most architectures, these changes ripple painfully. A schema update upstream means migration scripts, regression testing, deployment freezes, and downstream rework. Every layer – ingest, ETL, warehouse, BI – has to coordinate. Teams slow down or defer changes entirely, not because they lack ambition, but because their systems resist change.
CockroachDB was built for environments where schema evolution is constant – and must be safe.
Schema changes in CockroachDB are fully online and non-blocking. You can add columns, drop indexes, or update defaults without halting ingestion or locking tables. These changes run in the background while the system stays live, without invalidating reporting queries or breaking downstream access patterns.
DDL is transactional, too. Changes are applied atomically and can be rolled back just like data. If something goes wrong mid-deployment, there’s no half-baked schema or broken downstream job. It either works, or it doesn’t.
Everything is expressed in SQL. No proprietary migration framework or ad hoc tooling. Just standard DDL that integrates cleanly with CI/CD pipelines and change control workflows.
The result: platform teams can ship schema updates during working hours. Data engineers can evolve data flows without coordinating brittle downstream processes, while analysts get the new dimensions they need without waiting for a sprint to finish. The system as a whole gets safer, because change becomes routine rather than disruptive.
Real-World Example
Imagine that a compliance team needs to begin reporting on a new transaction tag for AML audits. The tag doesn’t yet exist in the reporting surface.
With CockroachDB, a developer:
Adds a column in SQL
Backfills historical data via an async job
Updates the materialized view logic
Pushes changes through CI
All without triggering downtime, QA panic, or a freeze on other work.
In legacy environments, this would have been a multi-week, highly coordinated effort. With CockroachDB, it becomes a routine, low-risk change.
The difference isn’t developer speed – it’s that reporting correctness is preserved throughout the change because it never leaves the operational database
How can you simplify the reporting Stack without losing capabilities?
The modern reporting stack has grown into a patchwork of specialized systems: transactional databases for ingest, warehouses for analytics, caches for performance, orchestration tools for coordination, and security layers bolted on at every step.
Each component adds operational weight with more tools to deploy, more credentials to manage, and more dashboards to monitor. Each integration adds risk: What happens when the cache lags behind the source of truth, the warehouse misses a batch window, or the ETL job fails silently overnight?
CockroachDB allows reporting platforms to operate as a single, distributed system by handling ingestion, querying, compliance, and resilience within the same operational boundary – without requiring external replication jobs, cache layers, or scheduling frameworks to preserve correctness. That simplification reduces not only the number of systems in play, but also the number of teams required to maintain them.
It also reduces cognitive overhead. CockroachDB speaks standard SQL, which means analysts, data engineers, backend developers, and platform teams can work against the same interface. There’s no need to translate between dialects, offload queries to BI tools, or wrap logic in workarounds. Everyone sees the same data, the same structure, and the same behavior.
That consistency reduces coordination cost. Teams no longer need to synchronize changes across multiple systems, or reason about how updates propagate through a chain of tools. New features don’t require full-stack architectural reviews, and onboarding becomes simpler because the platform behaves predictably.
It also broadens the hiring pool. With fewer specialized systems and a single SQL-based operational model, teams rely less on niche expertise tied to specific warehouses, pipelines, or caching layers. New engineers ramp faster, institutional knowledge can be de-emphasized, and staffing the platform becomes simpler. The platform stays leaner, more stable, and more predictable.
By collapsing multiple layers into one and unifying access patterns across roles, CockroachDB makes reporting architecture not just more powerful, but radically simpler.
Breaking Down Team Silos
Traditional reporting stacks don’t just fragment systems – they fragment teams.
Analysts write SQL against BI caches. Engineers debug pipeline logic in Spark or Python. Platform teams juggle schema migrations across multiple databases. Governance teams audit logs in isolation. Each group works in its own silo, using different tools to interpret different representations of what is supposed to be the same data.
CockroachDB brings these functions back together. With a unified SQL interface, consistent access controls, and a single operational model, it enables analysts, engineers, and operators to collaborate on the same platform – reducing friction, eliminating rework, and accelerating delivery because reporting, correctness, and lifecycle are handled in one place, rather than coordinated across teams.
Compliance, Auditability, and Data Sovereignty by Design
As reporting platforms become more distributed and integrated into operational workflows, compliance expectations grow more intense. It's no longer enough to produce accurate results – platforms must also prove where data resides, who accessed it, and how long it was retained.
CockroachDB was designed with these requirements in mind. With native support for geo-partitioning, it enforces physical data placement at the row, table, or tenant level. This ensures that data lives where regulations require, whether that’s within a region, a country, or a specific infrastructure boundary.
Data residency is just one part of the compliance equation, however. CockroachDB also provides a consistent, system-wide model for access control, change tracking, and transactional history. Because it's a single, distributed system – not a collection of stitched-together components – you get one source of truth for auditing, logging, and policy enforcement.
Multi-Version Concurrency Control (MVCC) plays a key role here. With AS OF SYSTEM TIME, auditors and internal teams can reconstruct the exact state of the database at any point in time, within its configured retention window. This makes it possible to validate report correctness, trace historical anomalies, and demonstrate regulatory compliance without maintaining shadow tables or complex ETL logic.
CockroachDB also supports full encryption at rest and in transit, integrates with external identity providers for authentication, and offers role-based access controls that can be scoped as narrowly as needed. The result is an infrastructure that doesn’t just meet security and compliance requirements, it simplifies them.
By collapsing data, access, and auditability into a single system, CockroachDB dramatically reduces compliance risk, ensuring that sensitive data stays where it should, behaves as it must, and can be proven correct at any point in time.
In environments where auditability, data sovereignty, and end-to-end traceability are non-negotiable, CockroachDB turns compliance from a manual, error-prone process into a built-in property of the system.
Where This Architecture Makes the Most Impact
The need for accurate, real-time, globally accessible reporting isn’t unique to one industry. It’s become foundational across every sector where decisions are time-sensitive, outcomes are high-stakes, and data must be trusted at the moment it’s consumed.
Financial Services
In trading, fraud detection, risk modeling, and compliance, reporting isn’t a background task, it’s an operational imperative. With strict regulatory controls and global teams, firms need real-time data that’s correct, consistent, and jurisdiction-aware. Latency or inconsistency isn’t just a bug in FinServ – it’s a governance failure.
Transportation and Logistics
From airline operations to supply chain visibility, distributed systems are the norm. Terminals, ports, warehouses, and endpoints all generate telemetry that must be reconciled and acted on against a coherent, up-to-date view of the system. CockroachDB enables localized ingest with global consistency, so decisions are based on the full picture, not a delayed snapshot.
Healthcare
Hospital networks, labs, and care providers operate under strict compliance regimes and require high availability. Clinical decisions depend on data that’s current, consistent, and governed, from audit logs to shared summaries. CockroachDB ensures HIPAA-ready access, local control, and always-on reporting.
Energy and Utilities
From smart grids to carbon tracking, this sector relies on telemetry and forecasting across broad, distributed footprints. Reporting platforms must remain operational through outages, balance performance with precision, and meet strict retention requirements – all areas where CockroachDB thrives.
AI-Powered SaaS
Products that offer analytics, recommendations, or user-facing reporting need real-time inputs and low-latency infrastructure. Whether powering ML models, anomaly detection, or customer dashboards, these systems demand fresh, correct data without relying on brittle batch jobs or stale caches.
Across industries, the common thread is architectural, not domain-specific. When reporting becomes operational, regulated, and globally distributed, the same traits matter: consistent reads, explicit data placement, predictable lifecycle, and a single source of truth.
CockroachDB: A Platform Purpose-Built for Reporting
Reporting is no longer a passive artifact of business activity. It has become an operational system — continuously available, decision-safe, and deeply embedded in how products run, how risks are managed, and how compliance is demonstrated. The platform behind it must reflect that reality.
CockroachDB doesn’t just support reporting workloads. It changes where reporting responsibility lives. Correctness, availability, data locality, and auditability are treated as traits of the operational database itself, rather than reconstructed downstream through pipelines, replicas, and caches.
Where legacy architectures rely on handoffs and coordination, this approach delivers architectural advantages:
A single, globally consistent source of truth for both operations and reporting
Reporting on operational data without replicas or synchronization pipelines
Schema evolution and lifecycle changes without downtime or coordination freezes
Built-in data placement, auditability, and compliance guarantees
This isn’t about incremental optimization. Rather, it represents a shift in how reporting platforms are designed – from downstream systems that approximate truth, to operational capabilities that preserve it by default.
Ready to learn more about making CockroachDB your global reporting platform? Visit here to speak with an expert.
FAQ
This FAQ includes foundational concepts and common questions related to global reporting platforms, expanding on the core topics covered here and aligning to common search intent around the topic.
What is a global reporting platform?
A global reporting platform is a system that delivers near-real-time reporting across regions from a single logical database with strong consistency guarantees. It allows data to be written close to where it’s generated and queried globally without external replication, ETL pipelines, or downstream data movement.
What is an operational reporting platform?
An operational reporting platform runs reporting workloads directly on live production data. Instead of relying on downstream replicas, warehouses, or ETL pipelines, reporting queries operate on the system of record with strong consistency guarantees.
How is operational reporting different from analytical reporting?
Operational reporting focuses on real-time visibility into live transactional data, while analytical reporting typically relies on batch-loaded or aggregated datasets. The key difference is freshness: operational reporting reflects current system state, not delayed snapshots.
Can you run reporting workloads without ETL pipelines?
Operational reporting focuses on near-real-time visibility into live transactional data, while analytical reporting typically relies on batch-loaded or aggregated datasets. The key difference is freshness: operational reporting reflects the current system state, not delayed snapshots.
Can CockroachDB replace a data warehouse for reporting?
Whether CockroachDB can or should replace a data warehouse depends on the workload. CockroachDB is well-suited to operational reporting, where reports run directly on the system of record and reflect recent transactional state without the complexity of ETL pipelines, external replication, or downstream synchronization.
A dedicated data warehouse remains appropriate for deep analytics workloads, such as large-scale offline analysis, long-running exploratory queries, or historical analysis over cold data, where heavy computation and workload isolation are the primary requirements.
How does CockroachDB support reporting with zero downtime?
CockroachDB runs reporting workloads on a distributed, fault-tolerant database designed for continuous availability. Because resilience and failover are built into the core architecture, reporting queries continue to run on live, strongly consistent data during node failures, infrastructure changes, schema updates, and rolling upgrades – without interruption.
How does CockroachDB support multi-region reporting?
CockroachDB supports multi-region reporting by controlling where data is placed and where it is accessed within a distributed SQL cluster. Geo-partitioning allows data to be located in specific regions to meet latency or regulatory requirements, while locality-aware reads ensure reporting queries are served close to the point of consumption.
This enables both writes and reads to be localized, without introducing external replication systems or compromising strong consistency.
Is CockroachDB suitable for compliant and audit-ready reporting?
Yes. CockroachDB supports compliant and audit-ready reporting through built-in access controls, encryption, historical reads, and data residency enforcement. This allows reporting to meet regulatory requirements as a property of the operational database, rather than through downstream controls.
Feature References and Documentation Links
AS OF SYSTEM TIME & MVCC
Used for historical reads and auditing:
Vectorized Execution Engine
How CockroachDB accelerates query processing:
JSONB Support
Handling semi-structured data:
Geo-Partitioning & Multi-Region Capabilities
Table Partitioning (Geo-Partitioning)
Allows you to set row-level data placement to optimize latency or comply with regulations:
Anchor text: Table partitioning / geo-partitioning
Link: https://www.cockroachlabs.com/docs/stable/table-partitioning Cockroach Labs+15Cockroach Labs+15Cockroach Labs+15
Multi-Region Capabilities Overview
Explains how to configure a CockroachDB cluster to work across geographic regions:
Anchor text: Multi-region Capabilities Overview
Link: https://www.cockroachlabs.com/docs/stable/multiregion-overview Cockroach LabsCockroach Labs+5Cockroach Labs+5Cockroach Labs+5
Table Localities (REGIONAL / GLOBAL)
Details table-level configuration for optimizing local vs. global access patterns:
Anchor text: table localities (REGIONAL BY TABLE / ROW)
Link: https://www.cockroachlabs.com/docs/stable/table-localities Cockroach Labs+15Cockroach Labs+15Cockroach Labs+15
Materialized Views
In-database precomputation for performance:
Column Families
Used to optimize I/O for related fields:
Online Schema Changes
Non-blocking schema evolution:
https://www.cockroachlabs.com/docs/stable/online-schema-changes
https://www.cockroachlabs.com/blog/how-online-schema-changes-are-possible-in-cockroachdb
Role-Based Access Control (RBAC) & Identity
Compliance-aligned access management:
Encryption at Rest and in Transit
Data protection built-in:
Alex Seriy is a Senior Staff Sales Engineer at Cockroach Labs, where he designs and builds reference architectures that explore how distributed systems behave under real operational constraints. His work centers on questions of correctness, data lifecycle, workload isolation, and governance in globally distributed SQL systems. By grounding theory in observable system behavior, he helps teams reason about architectural responsibility rather than assembling downstream compensations.
