What is cloud analytics?

Your analyst needs to run a heavy query, but the data warehouse is already maxed out serving dashboards. In the old world, you'd either wait for off-hours or start a procurement process for more hardware. With cloud analytics, you can spin up a dedicated compute cluster in seconds, run the query, and shut it down when you're done.

Cloud analytics separates storage from compute, so scaling analysis doesn't mean fighting for shared infrastructure. This guide explains how cloud analytics works, why the architecture matters, and what to look for when choosing a platform.

What is cloud analytics?

Cloud analytics is a service model where data storage and analysis happen on vendor-managed cloud infrastructure rather than servers you maintain yourself. Instead of tuning indexes, managing storage arrays, or configuring cluster parameters, you let the cloud provider handle optimization while you focus on the actual analysis.

Consider how this plays out in practice. Your analyst needs to answer a capacity planning question. In a traditional setup, they write SQL against an on-premise database that's also handling production traffic. The query times out because someone else is running a heavy report. They try again at 6 pm when the load is lighter, get results, export the data to CSV, build projections in Excel, and copy charts into Slides. Three days later, someone asks about a different region. And they have to start over.

With cloud analytics, your analyst runs the query against a dedicated compute cluster. Production traffic isn't affected, so the results come back in minutes. She iterates through follow-up questions in the same session because she's not competing for shared resources.

The architecture that makes this possible separates storage from compute. Your data lives in cheap cloud object storage while compute resources spin up and down based on query demand. This separation creates the elastic scaling and cost efficiency that define cloud analytics.

Why data teams should use cloud analytics

Cloud analytics removes the two costs that normally throttle analysis: waiting on infrastructure, and paying for capacity that sits idle.

Speed that improves workflows

Query performance directly impacts how you work. With cloud analytics, the analyst who used to wait for off-peak hours to run heavy queries now runs them whenever they need to, without slowing down the dashboards everyone else depends on.

AllTrails saw this firsthand. Their analysts used to cobble together CSVs, SQL files, and Jupyter notebooks using a workflow that made even routine analysis slow and painful. After moving to Hex's hosted notebooks with direct BigQuery integration, processes that took hours now take minutes.

Cloud analytics platforms make this possible through architecture designed for concurrent workloads. Your ETL (Extract, Transform, Load) job gets its own compute, and your executive dashboard gets another. The analyst running heavy ad hoc queries doesn't slow down everyone else. The ROI comes from eliminating exactly this kind of friction — not from any single feature, but from removing the bottlenecks that slow down decisions.

Scalability without infrastructure overhead

The elastic compute model behind cloud analytics means you can run concurrent analysis workloads during business hours, then scale down to minimal infrastructure overnight.

Think about what happens when your data science team needs to train a model. In traditional setups, they either compete for shared compute (slowing everyone down) or wait weeks for dedicated hardware. With cloud analytics, they spin up a large compute cluster for the training job, run it for four hours, then shut it down. They pay for what they used, and don't have to deal with procurement meetings or capacity planning cycles.

The real savings come from eliminating idle "just in case" capacity. Teams that migrate to cloud analytics platforms typically find they were paying for far more infrastructure than they actually needed.

Enterprise adoption shows production readiness

Adoption is now consistent even across regulated industries like financial services and manufacturing, which answers the "is this ready for serious workloads" question on its own.

Healthcare organizations have moved patient data to the cloud while maintaining security and compliance standards like HIPAA, and financial services have migrated transactional data with security controls in place. If those industries have solved compliance concerns, yours probably can too.

How cloud analytics works

Cloud analytics runs on three layers working together: a storage layer, a compute layer, and a services layer that coordinates between them. Understanding how the architecture works helps you use any platform better and avoid expensive mistakes.

Storage layer: where your data lives

The storage layer is where your data physically lives. In cloud platforms, this layer is separate from the compute that processes queries, which is the key architectural difference from traditional databases where storage and processing are tightly coupled on the same machine.

The storage layer holds your data and makes it available to whatever compute resources need it. It scales independently, meaning you can store petabytes without provisioning any processing power, and you only pay for the space you use. When compute clusters spin up to run queries, they read from this shared storage layer, which means multiple teams can access the same data simultaneously without creating copies.

Compute layer: elastic clusters that scale with demand

The compute layer is the processing power that runs your queries. These are the CPUs and memory that do the actual work of reading data, filtering rows, joining tables, and returning results. In cloud platforms, compute resources are provisioned on demand and billed by usage rather than owned as fixed hardware.

Clusters spin up when you start working and shut down automatically when idle, so you pay for minutes used, not servers sitting waiting. You can run multiple clusters simultaneously, so your ETL jobs, dashboards, and ad hoc analysis each get dedicated resources without competing for the same capacity.

Services layer: the invisible coordinator

The services layer is the control plane that coordinates everything else. It decides how to execute your query, manages user access, and keeps the system running smoothly. You never interact with it directly, but it's what makes the platform feel like a single coherent system rather than a collection of storage buckets and compute clusters.

The services layer handles authentication, optimizes your queries, and manages metadata so the system knows where everything is. It automatically decides the fastest way to execute each query, coordinates access so multiple users can work with the same data simultaneously, and keeps the whole system running without you tuning anything.

Choosing a cloud analytics approach

The right platform depends less on who has the most features, and more on whether it fits how your team actually works and where your data already lives.

Start with where your data lives

If you're already on Snowflake, BigQuery, Redshift, or Databricks, you don't need to move your data to get cloud analytics benefits. Look for platforms that connect directly to your existing warehouse. Moving data creates security headaches, governance complexity, and sync problems. The best analytics tools query your warehouse directly.

Match the platform to your team

Most organizations have both technical practitioners who want full control and business users who need guided exploration, so a platform that assumes everyone fits one profile fails half the team. Some tools assume everyone writes SQL; others assume no one does.

Platforms that force everyone into the same interface fail one group or the other. Your data scientist doesn't want a dumbed-down drag-and-drop tool. Your marketing director doesn't want to learn Python. Look for platforms that serve both, where technical users build analyses that business users can explore through a variety of methods that accommodate their skills and preference.

Notion's data team built exactly this in Hex. Their data scientists work in notebooks with Snowpark and Python, while their CX team uses published apps as a simple UI for pulling customer details. Sales combines Salesforce and Snowflake data without writing queries. Same platform, different interfaces.

Figma has a similar story. One self-taught researcher used Hex's Notebook Agent to build the company's NPS and Product Health programs solo, doing work that would normally require months of dedicated data science support.

Evaluate AI capabilities

Every vendor claims AI features now. Most are thin wrappers around chat APIs that generate SQL of questionable quality. Ask pointed questions:

• How easy is it to debug or extend an analysis started by an agent?
• How easy is it to fix incorrect answers when users report them?
• How strong are the observability workflows: can you see and flag incorrect responses?
• How easy is it for people to share analytical conversations with the data team?
• How well does the tool integrate with the rest of your ecosystem?

See our Head of Product's full recommendation on evaluating AI tools. The difference between useful AI and demo-ware is whether it actually knows your data. AI that generates generic SQL isn't much better than asking ChatGPT to write queries. AI that understands your business definitions, your table relationships, and your data quality rules can genuinely accelerate work.

Don't underestimate collaboration

Data work is collaborative, but most tools force awkward handoffs. The analyst builds something in one tool, exports it, shares via email, gets feedback via Slack, rebuilds with changes, exports again. Hours disappear into coordination overhead.

Look for platforms where multiple people can work simultaneously, where an analyst can build an analysis in the morning and a stakeholder can explore it that afternoon without anyone exporting anything. Real-time collaboration is what lets you move at the speed the business actually needs.

When Infinite Lambda built an emissions report for the World Health Organization in Hex, they needed both: full technical transparency for data validation, and an intuitive interface for policy stakeholders. The same notebook that let analysts audit every transformation step could be shared as an interactive report, no rebuilding required.

Where cloud analytics is heading

The infrastructure shift is just the foundation. Cloud analytics is evolving toward platforms that integrate AI natively — not as a bolted-on ChatGPT wrapper, but as a core part of how teams interact with data.

Hex brings that infrastructure together with AI analytics built around three things: ask a question in plain language through Threads, publish answers as interactive data apps anyone can explore, and keep the AI grounded in governed definitions through Context Studio. Data scientists still get full SQL and Python when they need that depth. Business users get natural language when they don't. Everyone works from the same context in real time, so no one's emailing files or guessing which version is current.

The platform connects directly to your cloud data warehouse. No copying data. No security headaches. The analysis happens where the data already lives, with AI assistance built into every workflow.

Try Hex free to see AI analytics built for your cloud data, or request a demo to explore how data teams and business users collaborate in the same environment.

Frequently Asked Questions

Does cloud analytics work with an on-premise data warehouse?

Cloud analytics tools are built to connect to cloud data warehouses like Snowflake, BigQuery, and Redshift, so an on-premise warehouse changes the setup. Some platforms support hybrid connections through a secure gateway or VPN, letting you query on-premise data without moving it. Others require replicating that data into a cloud warehouse first, which adds a sync step and a second copy of your data to maintain. If you're mostly on-premise today, check a platform's warehouse integrations against your specific setup before assuming it'll just work.

How is cloud analytics different from a data lake?

A data lake is a storage layer: a place to hold raw, unstructured, or semi-structured data at low cost. Cloud analytics is what you do with that data once it's stored, the querying, visualization, and action-taking layer that sits on top of storage. Many cloud analytics platforms connect to a data lake, a warehouse, or a lakehouse that combines both, so the two aren't competing categories. The practical question is whether your analytics platform can query your storage layer directly, or whether you need to move data into a separate system first.

What happens to my cloud analytics costs if usage spikes unexpectedly?

Most cloud analytics platforms bill based on compute time or credits consumed, so a spike in usage shows up as a spike in cost rather than an outage. Look for platforms that let you set spending caps, alerts, or auto-scaling limits on compute clusters so an unexpected surge doesn't turn into an unexpected bill. Some vendors also separate storage costs from compute costs, which makes it easier to see which side of a spike you're actually paying for. If you want a concrete example, Hex pricing breaks the two out separately.