"Real-time analytics" sounds like one requirement. In practice, it is usually four.
- Ingestion latency - how quickly data lands and becomes queryable.
- Query latency - how fast the analytical query itself runs.
- Data freshness - whether the query is hitting the latest data or a stale cache, buffer, or partially updated view.
- Concurrency - whether the system still behaves when dozens of people hit live dashboards at the same time.
Those four dimensions pull against each other more often than vendors admit. A platform can feel fast in a demo because it leans on caches. Another can ingest in seconds but stumble once a sales team, support team, and on-call engineer all open the same dashboard. A third can solve the problem cleanly enough, but only after you assemble Kafka, a stream processor, a serving layer, a warehouse, and the glue in between.
That is the lens for this comparison. I start with managed platforms, because that is where most teams should begin. Then I cover self-hosted open-source options, the difference between real-time analytics and streaming analytics, and finally the narrower SaaS tools that can still be the right answer for specific jobs.
This is not a feature-matrix exercise. ClickHouse has published deeper benchmark work elsewhere, and there are plenty of query-level shootouts on the internet. This piece is about what actually happens when a team tries to run live analytical workloads in production: fresh data, interactive queries, real dashboard traffic, and a sane operational model.
For a broader framework on choosing a database for this workload, see How to choose a database for real-time analytics in 2026.
Managed platforms for real-time analytics #
For most teams, managed should be the default. Unless you have hard requirements around sovereignty, cost structure, or deep infrastructure control, there is rarely much glory in self-hosting your first real-time analytics stack. Managed is usually the better choice; the more useful question is which managed platform breaks first for your workload.
ClickHouse Cloud #
ClickHouse Cloud is the cleanest managed answer when the job is genuinely real-time analytics, not "warehouse analytics, but a little faster than before."
The reason is architectural more than cosmetic. ClickHouse was built to query large, fast-moving datasets with low latency, and ClickHouse Cloud wraps that engine with managed ingestion, scaling, backups, and operational tooling. You are not trying to coerce a batch warehouse into behaving like a serving system. You are starting with a database that already expects fresh data and interactive reads.
Ingestion. ClickPipes makes continuous ingestion from Kafka, object storage, Postgres CDC, and other sources straightforward. What matters more than the connector count is that the ingestion path stays close to the query engine. You do not end up with the familiar pattern of "stream into one service, buffer in another, stage somewhere else, then finally expose it to SQL."
Query latency. ClickHouse is comfortable with sub-second analytical queries on data that arrived moments ago. That matters because a lot of systems can be fast on yesterday's data or on pre-computed rollups. Fewer are fast on data that just landed.
Freshness. The system does not force you to choose between a fast cache and a slow source of truth. For most teams, that sounds abstract until the first time they are debugging a live dashboard and realize the number on the screen is three minutes behind the number in the pipeline.
Concurrency. ClickHouse handles concurrent dashboard-style workloads better than platforms that were designed around smaller numbers of heavyweight warehouse sessions. Concurrency isn't free, but the default operating model is closer to what real-time analytics teams actually need.
The feature that matters most in practice is incremental materialized views. They shift work from query time to insert time, which lets you pre-aggregate or reshape data as it lands. That removes an enormous amount of architectural weight. In many stacks, the same job would be handled by a stream processor, a staging layer, and another serving system. In ClickHouse, it can often stay inside the database.
The real advantage here is fewer moving parts, not just raw speed. When ClickHouse Cloud is the right fit, the appeal is that one system can handle ingestion, storage, transformation, and query serving without forcing you to compose a four-service architecture just to keep a dashboard fresh.
The trade-off is that ClickHouse still rewards deliberate data modeling. The managed service removes most of the operational burden, but it does not remove the need to think about sort order, partitioning, materialized views, and access patterns. Teams that do that well get a lot back in return.
AWS #
AWS does not really give you one real-time analytics platform. It gives you a very capable kit.
That can be a strength. If you already build most of your stack on AWS, the native building blocks are mature, broad, and well integrated. It can also be a weakness, because the moment you say "real-time analytics on AWS," you are usually talking about several services working together rather than one engine doing the whole job.
Redshift Streaming Ingestion is the most important piece on the analytics side. It can ingest directly from Kinesis or Kafka-compatible sources into Redshift materialized views, which is a real improvement over older S3-centered ingestion patterns. The setup is cleaner than it used to be, and the latency is much better. The catch is that the first materialized view is still a relatively narrow entry point: great for getting streaming data in, less great as the full answer to transformation-heavy analytics.
Zero-ETL integrations help, especially for Aurora into Redshift. They remove a lot of tedious pipeline work for transactional-to-analytical replication. But "zero-ETL" is a label, not a latency guarantee. Some AWS sources land close to real time. Others are slower. In practice, teams still have to evaluate source by source rather than assuming the phrase means the same thing everywhere.
Managed Service for Apache Flink is the serious option if you need actual stream processing: event-time windows, joins, stateful logic, triggers, and actions before data lands in the analytical store. AWS deserves credit here. If your problem is truly streaming, not just fast analytics on recent data, the Flink path is strong.
Concurrency is where the assembly cost becomes more obvious. Redshift has concurrency scaling and can absorb more parallel demand than it used to, but the story is still "add more capacity, more clusters, or more services as load changes." That is workable. It is just more plumbing than you wanted when the thing you wanted was fresh dashboards for a few dozen or a few hundred users.
That is the core AWS trade-off. The primitives are good. The integrated experience is not really the point. You are building a platform out of Kinesis or MSK, maybe Flink, probably Redshift, and whatever orchestration and observability glue your team prefers. If your organization already thinks that way, AWS feels natural. If what you want is the shortest path from raw events to always-fresh dashboards, it feels heavier than it needs to.
For a deeper comparison, see Redshift vs ClickHouse.
GCP #
Google Cloud has improved its real-time analytics story significantly, but it still feels like a portfolio rather than a single obvious answer.
That matters because GCP has genuinely good pieces. Pub/Sub is mature. Dataflow is powerful. BigQuery is easy to buy into organizationally. Bigtable is fast. AlloyDB is interesting. GCP doesn't lack tools; the best tool just depends very heavily on which exact slice of "real-time" you care about.
BigQuery Continuous Queries are now an important part of the story. They let BigQuery run continuously against incoming data and send results to BigQuery tables or downstream services. That closes some of the historical gap between "warehouse" and "stream processor." It is especially useful when you want event-driven SQL without adopting an entirely different platform. The nuance is that some of the more advanced stateful pieces are still maturing, so the feature is real and useful, but not a total substitute for a dedicated streaming engine.
BigQuery ingestion is also better than the old "BigQuery is only batch" stereotype suggests. The Storage Write API makes fresh data queryable quickly, and BigQuery can absolutely support near-real-time analytical workflows. Where it still feels different from a purpose-built real-time OLAP engine is under sustained pressure from fresh data, interactive users, and broad ad hoc querying at the same time.
BI Engine helps on the serving side by accelerating repeated or dashboard-heavy queries, but it is still an acceleration layer. That distinction matters. Systems built around caches and acceleration can feel excellent when the working set fits and the query patterns are predictable. They feel less magical once the workload broadens.
Bigtable has quietly become more relevant here because GoogleSQL support and continuous materialized views make it much more approachable for operational analytics than it used to be. For time-series data, operational metrics, or narrow high-speed access patterns, it can be excellent. But Bigtable is still Bigtable. You do not pick it because you want the broadest ad hoc OLAP experience. You pick it because you want a very fast wide-column system that now has a friendlier query story.
AlloyDB is Google's most interesting HTAP-style option. If your team wants PostgreSQL compatibility and moderate analytical acceleration inside the same operational database family, AlloyDB is compelling. It is especially attractive for teams that do not want to split their world too early into OLTP over here and OLAP over there. The ceiling is still different from dedicated analytical engines, but the simplicity can be worth it.
So the GCP story in 2026 is much better than the old caricature. It is no longer fair to say "BigQuery is batch, end of story." But it is still fair to say that Google spreads real-time analytics across multiple products: BigQuery for breadth, Bigtable for speed on narrower patterns, Dataflow for streaming logic, AlloyDB for HTAP-style use cases. That can be a feature if you like product choice. It can also be a source of ambiguity if you wanted one clear platform to bet on.
Azure #
Azure's strongest answer to real-time analytics is Eventhouse, and it deserves to be taken seriously.
That is the short version. The longer version is that Microsoft has one of the more credible cloud-native engines for this workload, but it comes bundled with a familiar Microsoft question: how much of the wider ecosystem bet are you comfortable making?
Eventhouse, part of Fabric Real-Time Intelligence, is the current center of gravity. Under the hood, this is the Kusto family of technology repackaged into the Fabric world, and it is a real engine, not a thin feature wrapper. It is built for high-velocity, time-based, semistructured data, automatically indexes and partitions on ingestion, and is designed for near-real-time exploration rather than warehouse-first batch behavior.
In practical terms, this means Azure has a native service that feels much closer to ClickHouse than to Redshift, BigQuery, or classic Snowflake warehouses. Eventhouse was built for real-time rather than retrofitted to it.
Fabric mirroring makes the story stronger. Microsoft now has a much more compelling path for continuously replicating SQL Server, PostgreSQL, and Cosmos DB data into OneLake in near real time. That reduces pipeline work and makes the surrounding platform more coherent than it was when teams had to stitch together more of the flow themselves.
The trade-off is not performance so much as ergonomics and scope.
First, the language. Eventhouse is KQL-first. KQL is good. In log, telemetry, and time-series work, many engineers actively prefer it. But it is still not SQL, and that difference is not theoretical. If your analysts, BI tools, and internal habits are deeply SQL-centric, KQL is a real adoption cost.
Second, the wider workload picture. Eventhouse is strong for real-time analytics, but Fabric still separates the Kusto-native real-time path from its broader warehouse path. If your company wants one engine and one query language for everything, that split matters.
Third, the platform bet. Eventhouse makes the most sense when Fabric is already becoming the center of gravity in your stack. If your organization is committed to Microsoft, that is not a downside. If portability or multi-cloud freedom matters, it is harder to ignore.
One other point matters for 2026 readers: Synapse Data Explorer is no longer where Microsoft's momentum is. That chapter is effectively closed. Eventhouse is the place to look for new real-time analytics work on Azure.
Snowflake #
Snowflake is more interesting for real-time workloads in 2026 than it was even a year ago, though the improvements are more constrained than the headlines suggest.
The picture is no longer as simple as "great warehouse, weak real-time story." Snowflake has put real work into both ingestion and serving. What a serious comparison should also reflect is how much of that new capability lives behind specific patterns and hard limits.
Ingestion. Snowpipe Streaming gives Snowflake a much tighter ingest-to-query path than classic Snowpipe ever did, and the headline throughput numbers are genuinely good. Reaching them in practice takes work. The high-end rates depend on massive write concurrency, which means you need to understand how to partition and parallelise your streams, manage channels, and handle offsets correctly. Teams that invest in that engineering get a lot out of Snowpipe Streaming. Teams that expect the marketing number to drop out of a naive implementation tend to discover there is more work involved than the brochure implies.
Serving. The more interesting change is on the query side. Snowflake now has interactive tables and interactive warehouses, explicitly aimed at low-latency, high-concurrency workloads like dashboards and data applications. That matters because it is Snowflake acknowledging that real-time serving needs a more specialised path than the classic warehouse pattern provides.
The details are where the picture narrows. Interactive tables are not a general "make my queries fast" option. They require a specific interactive warehouse type paired with a specific table type, and you are capped at ten interactive tables per warehouse. Queries on those tables also have a hard five-second ceiling, which means the path is useful for small, targeted queries that can complete inside that budget, rather than for making existing heavier analytical queries faster. You also pay additional cost for both the warehouse type and the storage type.
So the interactive path is a real improvement, but it is a narrow retrofit rather than a change in default behaviour. It fits a specific class of low-latency serving patterns, at the cost of extra configuration, extra billing lines, and a ceiling on what the queries can actually do.
If you stay on the classic warehouse path, the familiar trade-offs are still there. Snowflake is excellent when workloads benefit from caching, warehouse isolation, and batch-oriented economics. It works less well when you want always-fresh data, low latency, and high concurrency from the same default path. The core warehouse model still scales concurrency by adding clusters or adding warehouses, and the economics reflect that.
That is the honest way to think about Snowflake now. It can credibly do more of the real-time job than older comparisons imply, especially for teams already standardised on it and willing to work within the specific paths Snowflake provides. What it has not done is produce a system whose basic architecture is built around fresh analytical reads. The real-time answer is a set of targeted options layered onto a warehouse, with the limits that implies.
For teams already all-in on Snowflake, those constraints may be perfectly acceptable. For greenfield buyers, they are worth noticing.
For a deeper comparison, see ClickHouse vs Snowflake for real-time analytics and the cloud data warehouses cost and performance comparison.
Databricks #
Databricks has closed part of the gap, but it still approaches real-time analytics from a very different starting point than a dedicated analytical database.
That starting point is the lakehouse and Spark ecosystem. If that is already your center of gravity, Databricks can be a very strong platform. If your main requirement is simply fresh data, fast dashboards, and as little operational machinery as possible, it can still feel heavier than the alternatives.
Databricks is now better described as having several tiers of "real time," and that distinction matters. For managed freshness, Lakeflow, streaming tables, and materialized views have made it much easier to keep analytical tables up to date without as much hand-built pipeline work. For conventional streaming, Structured Streaming remains mature and widely understood. And for genuinely low-latency workloads, Databricks now has a more explicit Real-Time Mode, which is a meaningful step forward.
That means the old criticism that Databricks is "just batch" is now too blunt. The platform has done real work to improve streaming and near-real-time operation.
But the more important question is what kind of real time it delivers, and at what cost.
For analytical freshness, Databricks continuous pipelines can keep data fresh on the order of minutes.
For true low-latency streaming, the picture is more constrained. Databricks' Real-Time Mode is not a general "make the lakehouse real time" switch. It currently requires dedicated classic compute, disables some of the conveniences people associate with the rest of the platform, and supports a narrower set of sources, sinks, and operators than the broader Spark model.
That is the durable tradeoff. With Databricks, you are still fundamentally in a world of pipelines, checkpoints, table maintenance, cluster sizing, and cost/performance tuning. Photon and Databricks SQL improve query performance substantially, but the overall mental model remains closer to "operate a data platform" than "point dashboards at a continuously available analytical engine."
Databricks is strongest when real-time analytics is one part of a bigger lakehouse strategy. It is thinner when real-time analytics is the whole job, especially if the bar is low-latency serving with simple operations and predictable always-on cost.
For query performance comparisons, see the JOIN benchmark: ClickHouse vs Databricks and Snowflake.
Open-source OLAP: build your own real-time analytics platform #
Self-hosting only makes sense when you actually mean it.
Sometimes teams say they want open source when what they really want is lower cost or less vendor lock-in. Those are valid goals, but they do not automatically justify running your own analytical database. Self-hosting becomes reasonable when you have one of the hard reasons: sovereignty requirements, existing infrastructure expertise, platform-standardization goals, or economics at a scale where managed pricing is no longer attractive.
If you are in that camp, the most common open-source choices for real-time analytics are ClickHouse, Apache Pinot, and Apache Druid.
ClickHouse is the broadest single-engine option of the three. It can do real-time analytics, general OLAP, observability, and a meaningful amount of warehouse-style work in one system. That breadth matters operationally. The more jobs one engine can do, the fewer edges you have to manage. ClickHouse also supports SQL standard UPDATE and DELETE, which keeps workflows like CDC, compliance deletion, and late-arriving corrections inside the database rather than stitched together around it. It is also the same core engine family used by ClickHouse Cloud, which gives teams a cleaner path between self-hosted and managed than many open-source projects can offer.
Apache Pinot is best understood as a high-concurrency real-time serving layer. It is very strong when the query patterns are relatively well understood and the job is to serve user-facing analytics fast and at scale. That is why it shows up so often in product analytics and customer-facing dashboards. The trade-off is that Pinot is narrower. It is not the system most teams want to use for broad warehouse exploration or truly open-ended analytical work, which means it often lives beside something else. Pinot is also append-only, with no SQL standard UPDATE or DELETE, so corrections, CDC, and compliance-driven deletions have to be handled outside the engine. The net is very high query performance at serving scale, paid for with very high operational complexity.
Apache Druid occupies a similar space: fast ingestion, strong aggregation performance, especially on time-oriented workloads, and an architecture that makes sense when serving speed is the priority. Like Pinot, it tends to be happiest as part of a larger analytical estate rather than the only system you run. Druid has the same append-only constraint, with no SQL standard UPDATE or DELETE, so the same class of mutation work has to live outside the database. Druid brings similar operational complexity without matching Pinot's peak query performance or concurrency, and the project is showing its age. It has the least active development of the three, and new adopters increasingly pick elsewhere.
The operational shape is the other clear difference, and it matters more than it first sounds. Pinot and Druid are multi-component distributed systems by default. Pinot has separate Controller, Broker, Server, and Minion roles, with ZooKeeper as the coordination layer. Druid is heavier still: Coordinator, Overlord, Broker, Historical, MiddleManager, and Router services, plus ZooKeeper, plus an external metadata database like MySQL or Postgres, plus deep storage. Each role has to be sized, deployed, monitored, and debugged independently, and running either system locally for development usually means standing up a multi-container Docker stack that approximates the production topology.
ClickHouse sits at the other end of the spectrum. It is a single binary. The same engine runs as chDB inside a Python or Jupyter notebook, as clickhouse-local for one-off queries against files or tables from the command line, as a simple server on a laptop, as a single-node production deployment, or as a clustered multi-node deployment with ClickHouse Keeper for coordination. There is no external metadata service to stand up, no half-dozen service types to orchestrate, and adopting ClickHouse for a small job does not require pretending to run a distributed system first. Scaling up later is a configuration change rather than an architectural rewrite.
Specialized serving systems can still earn their keep when your workload is specialized enough to justify them. For most teams, the operational tax of Pinot or Druid is ongoing and real, not a one-time setup cost.
Do you actually need streaming analytics? #
A surprising number of teams looking for a "real-time analytics platform" do not actually need streaming analytics.
That is an important distinction, because the architectural consequences are very different.
Streaming analytics means processing data in flight: event-time windows, stream-stream joins, triggers, stateful logic, fraud decisions, alerts, and actions that happen before the data ever lands in its long-term analytical home.
Real-time analytics usually means something more mundane: get the data in fast, maybe reshape it on ingest, and let users query it while it is still fresh.
Those are not the same problem.
A credit-card fraud decision that has to happen in tens or hundreds of milliseconds is a streaming problem. A dashboard showing the last 30 seconds of order volume is usually not. A pipeline that enriches events and routes them downstream in real time is a streaming problem. A metrics page that updates every few seconds generally is not.
The reason this matters is that stream processors are powerful and expensive, in both infrastructure and attention. Apache Flink is excellent if you genuinely need event-time stateful processing. It is also more system than many analytics teams actually need.
A lot of workloads that sound like streaming turn out to be ingest-time transformation plus fast queries. Running counts, rollups, session summaries, denormalized serving tables, and dashboard-friendly aggregates often fit that pattern. If your end goal is still a query result, not an in-flight action, you should at least ask whether a database with strong ingest-time computation can do the job more simply.
This is why incremental materialized views matter so much in ClickHouse. They let you push work to insert time and then query the result directly, which gets you many of the benefits teams seek from streaming systems without forcing you to run a separate stream processor. Other platforms now have their own versions of this idea, but ClickHouse makes it unusually central to the platform.
Kafka sits a little outside this distinction. It is an event backbone, not an analytics engine. Many teams will use Kafka no matter which analytical database they choose. The question is whether Kafka is feeding a simple analytical store or a bigger streaming stack.
The honest recommendation is still the same. If you need true in-flight processing, use Flink. If you mostly need fast analytics on fresh data, start with a database that is good at that. The most common architectural mistake in this space is paying the operational price of streaming infrastructure for a problem that never needed it.
For more background on the distinction, see What is real-time analytics.
Use-case-specific platforms #
Not every team should build on a general-purpose analytics platform.
Sometimes the right answer is to buy the narrower tool and move on.
Product analytics platforms are the clearest example. Amplitude and Mixpanel can get a team from zero to funnels, retention, cohorts, and experiment analysis quickly. If the job is "we need decent product analytics next month," that speed matters more than architectural purity.
The trade-offs are familiar. The data often lives in a separate silo. Custom analysis eventually runs into product boundaries. Joining product events with warehouse data, business metrics, or infrastructure telemetry gets awkward. And pricing tied to event volume tends to feel fine until it really does not.
That is why some teams eventually rebuild product analytics on top of a general-purpose system like ClickHouse. The upfront work is higher, but the upside is substantial: one platform, one query language, one place where product events can live beside revenue data, operational metrics, support signals, and the rest of the business. At scale, that can be both more flexible and cheaper.
The right choice usually depends on timing.
Turnkey SaaS wins when:
- you are a small team,
- you need answers quickly,
- product analytics is the main job,
- and the complexity of building your own stack is clearly not worth it.
Building wins when:
- event volume is large enough that SaaS pricing starts to hurt,
- the product data has to join with the rest of your stack,
- or you want one analytical platform across product, business, and operational use cases.
Neither path is inherently smarter. They optimize for different moments in a company's life.
For dashboard considerations, see Real-time data visualization.
Choosing a platform #
Most of these platforms can ingest data, run SQL, and power dashboards. That is not the interesting part anymore.
The interesting part is what happens when the data is fresh, the query patterns are mixed, and real people all show up at once.
If you want the shortest path to fast ingestion, fast queries on fresh data, and high concurrency without assembling a multi-service architecture, ClickHouse Cloud remains the cleanest answer in this comparison. It is purpose-built for the workload, and that shows up not only in performance but in how much less architecture you have to drag around.
If you are committed to AWS and happy to build from primitives, AWS gives you a capable path - just not a simple one.
If you are in Microsoft's world, Eventhouse is a serious contender and arguably the strongest cloud-native real-time engine outside ClickHouse. The question there is less about performance and more about KQL, Fabric, and platform commitment.
If you want to self-host, ClickHouse is the broadest single-engine option.
And if what you truly need is event-time processing before data lands anywhere, stop looking for an analytics database to do a stream processor's job. Use Flink.
The simplest way to make the decision is to ask which of the four dimensions you can least afford to compromise on. Freshness narrows the field quickly. Concurrency narrows it in a different way. Simplicity narrows it faster than any benchmark table will.
That is usually how these decisions get made in real life anyway.
