Best Real-Time Analytics Platforms 2025: Databricks vs Snowflake vs Hex vs Fastero

We've noticed "real-time analytics" getting thrown around a lot lately. And honestly? It can mean about five different things depending on who you ask.

Some teams mean streaming ingestion—getting data from Kafka into their warehouse continuously. Others mean fast queries—sub-second responses on large datasets. Some are talking about dashboard refresh—visuals that update without clicking "Refresh." And then there's automated actions—when a threshold is crossed, something happens immediately.

All of these are valid definitions of "real-time," and they matter for different reasons. The problem is that most platforms excel at one or two of these, but not all of them.

This guide walks through Databricks, Snowflake, Hex, and Fastero with an honest look at where each one shines—and where you might need to supplement with other tools.

What "real-time" actually means (and why it matters)

Let's break this down with real examples, because abstract definitions don't help anyone.

Streaming ingestion

What it is: Continuously loading data as events happen (vs. batch loads every hour/day).

Example: Your e-commerce site tracks every product view, cart addition, and purchase. Instead of waiting for a nightly ETL job, events flow into your data warehouse via Kafka or CDC (change data capture) within seconds.

Why it matters: If your data is hours old before it even lands in the warehouse, no amount of query speed will make your dashboards "real-time."

Query latency

What it is: How fast your analytical queries return results.

Example: An executive opens a revenue dashboard with 12 charts, each hitting tables with billions of rows. Do they wait 30 seconds for every page load, or does it feel instant?

Why it matters: Even with fresh data, slow queries kill the interactive exploration experience. You want analysts asking follow-up questions, not making coffee while results load.

Dashboard refresh

What it is: Whether dashboards update automatically when data changes, or require manual refreshes/scheduled updates.

Example: Your customer success team monitors a "high-risk churn" dashboard. If a major customer's usage drops 50%, do they find out immediately, or 15 minutes later when the next scheduled refresh runs?

Why it matters: Scheduled refreshes are wasteful (they run even when data hasn't changed) and laggy (you miss changes between intervals). Event-driven refresh means dashboards update only when needed.

Action/alert latency

What it is: Time from data change → trigger → automated action (webhook, Slack alert, workflow).

Example: A customer upgrades from Starter to Enterprise. Your sales team should get notified immediately and Salesforce should be updated so they can engage while the customer is excited—not 20 minutes later when the next sync batch runs.

Why it matters: Real business impact comes from acting on insights, not just viewing them. If your analytics platform can't trigger actions, you're duct-taping external tools together.

The platforms, in depth

We're going to be honest here. Each of these platforms was built for different use cases, and they excel in different areas. There's no "winner"—just better fits for specific needs.

Databricks — built for streaming + ML at scale

Databricks brings a lakehouse architecture (combining data lake flexibility with data warehouse performance) and deep integration with machine learning workflows. It's built on Apache Spark, which means it's excellent for massive-scale data engineering and streaming ETL.

Where it shines:

• Streaming ETL: Structured Streaming handles continuous data pipelines well, especially when you need to transform, join, and enrich streaming data before landing it in Delta Lake.
• ML/AI workloads: If you're building feature pipelines, training models on petabyte-scale datasets, or deploying ML models that need fresh data, Databricks has the ecosystem (MLflow, Feature Store, etc.).
• Multi-cloud: Runs on AWS, Azure, and GCP with consistent APIs.

Practical tradeoffs:

• Expertise required: You need Spark knowledge. Delta Lake, streaming windows, and cluster tuning aren't trivial. Smaller teams often find the learning curve steep.
• Streaming latency: Structured Streaming is micro-batch by default (typically 5-60 seconds depending on configuration). Continuous processing mode exists but is experimental and limited.
• Not BI-first: Databricks SQL exists, but most teams still use Tableau/Looker/Mode on top. It's a data engineering platform with BI capabilities, not the other way around.

Real-world fit: You're processing petabytes, running complex ML pipelines, and have a team that knows Spark. You're willing to invest in expertise for power and scale.

Snowflake — familiar SQL and elastic scale

Snowflake's strength is being a SQL-first data warehouse that just works. Elastic compute, automatic scaling, and zero-administration infrastructure make it popular with teams that want to focus on queries, not infrastructure.

Where it shines:

• SQL analytics: Probably the best pure SQL warehouse experience. Familiar syntax, excellent concurrency, and mature BI tool integrations.
• Elastic scaling: Warehouses spin up and down automatically. You're not managing clusters or tuning Spark configs.
• Data sharing: Snowflake's Data Marketplace and secure data sharing features are genuinely unique and powerful for multi-party analytics.

Practical tradeoffs:

• Real-time is layered on: Snowflake's core is batch-oriented. For "near real-time," teams use Snowpipe for continuous ingestion (which can take 1-3 minutes per file) + Tasks for downstream transformations. It works, but it's orchestration, not native streaming.
• Cost management: Auto-scaling is great until you realize a query ran on a larger warehouse than intended. Teams often implement approval workflows and cost guardrails.
• Action/alerting: Snowflake doesn't have native webhooks or workflow orchestration. You'll use dbt Cloud, Airflow, or external tools to wire actions based on data changes.

Real-world fit: Your team is SQL-first, you want predictable performance without infrastructure headaches, and you're okay orchestrating real-time workflows with external tooling.

Hex — modern, collaborative analysis

Hex combines notebooks, SQL, and interactive apps in a slick, product-like UX. It's designed for analysts who want to explore data, explain findings, and share interactive results with stakeholders—all in one place.

Where it shines:

• Collaboration: Version control, commenting, and shared context make it easy for teams to work together. Think "Figma for data."
• Interactive apps: Turn notebooks into shareable apps with parameters, dropdowns, and visualizations. Non-technical stakeholders can explore without writing SQL.
• SQL + Python + R: Analysts can use the tool they're most comfortable with, all in one environment.

Practical tradeoffs:

• Scheduled by default: Most teams run Hex projects on schedules or manually. There's no native CDC or Kafka trigger—though you can wire external webhooks to kick off runs.
• Not a data platform: Hex queries your existing warehouse (Snowflake, BigQuery, etc.). It doesn't store or transform data itself. That's by design, but means you still need a separate data stack.
• Real-time patterns require workarounds: For "near real-time," teams often pair Hex with dbt Cloud or Airflow to refresh models, then have Hex query the refreshed tables.

Real-world fit: Your analysts love notebooks, you want beautiful shareable outputs, and you're okay with scheduled or on-demand refresh patterns (or willing to wire external triggers).

Fastero — event-driven analytics platform

Fastero was built around a core belief: analytics should react to data, not wait for schedules. We started with triggers as the foundation, then built dashboards, notebooks, apps, and workflows on top of that event-driven architecture.

Where it shines:

• Event-driven by default: Dashboards and workflows refresh when data actually changes—via CDC triggers (PostgreSQL LISTEN/NOTIFY), Kafka events, webhooks, dbt job completions, API calls, or scheduled executions. You wire what makes sense for each use case.
• Unified platform: Notebooks, Streamlit apps, dashboards, and workflows live in one place. No duct-taping 5 tools together. SQL cells in notebooks can trigger downstream actions. Dashboards can kick off workflows. It's integrated by design.
• Production-grade API: Every query can become a versioned REST endpoint with signed URLs, IP allowlisting, rate limits, circuit breakers, and webhook subscriptions. Ship analytics as an API, not just a dashboard.
• Governance built-in: PII auto-classification, comprehensive audit logs with risk scoring, row-level security (ACL policies), SQL guardrails that block destructive queries, and cost estimation with approval gates. Most platforms charge enterprise prices for these; we include them by default.
• Natural language to SQL: Ask questions in plain English and get executable SQL with self-correction and schema validation. It learns from your existing queries and table usage patterns.
• Hosted compute: We host Streamlit apps and Jupyter notebooks with WebSocket-preserving proxies, file sync from the database (not just Git), and Kafka integration for instant updates.

Practical tradeoffs:

• Smaller connector ecosystem: We're focused on operational analytics and event-driven workflows, not competing with Databricks on petabyte-scale ML. We integrate with major warehouses (BigQuery, Snowflake, Redshift, Postgres, MySQL, Oracle, Athena, MSSQL) but don't have 200+ destination connectors.
• Newer platform: Databricks and Snowflake have 10+ years of enterprise features and edge-case handling. We're growing fast and have enterprise-grade governance built in, but we don't have every possible feature checkbox.
• Different philosophy: We're API-first and event-driven. If you want traditional scheduled BI dashboards with drag-and-drop chart builders, Looker or Tableau might feel more familiar. If you want operational workflows that react to data, we're a better fit.

Real-world fit: You want dashboards that refresh on change, you're building operational workflows (alert → action), you value integrated tools over best-of-breed sprawl, and you care about governance and API access.

Side-by-side comparison

Here's how these platforms stack up across the dimensions we discussed:

Platform	Streaming ingestion	Query latency	Dashboard refresh	Action/alert	Best for
Databricks	Excellent (Structured Streaming)	Fast (Spark/Delta)	Batch + scheduled jobs	Via external tools	Petabyte‑scale ML/AI + streaming ETL
Snowflake	Good (Snowpipe, Tasks)	Excellent (SQL)	Scheduled + event‑layered	Via external tools	SQL‑first analytics with elastic scale
Hex	N/A (queries existing warehouses)	Depends on warehouse	Scheduled + on‑demand	External webhooks	Collaborative analysis + interactive apps
Fastero	Good (CDC, Kafka, webhooks)	Depends on warehouse	Event‑driven by default	Built‑in (webhooks, workflows, API)	Operational dashboards + event‑driven workflows

When to choose each platform

Choose Databricks if:

• You're processing petabytes and need streaming ETL at scale
• ML/AI is core to your analytics (feature engineering, model training, deployment)
• You have Spark expertise or are willing to invest in it
• Multi-cloud portability matters

Choose Snowflake if:

• Your team is SQL-first and wants minimal infrastructure management
• You need rock-solid performance for concurrent BI queries
• Data sharing across organizations is important
• You're okay orchestrating real-time patterns with external tools

Choose Hex if:

• Collaboration and shareable interactive apps are the priority
• Your analysts love notebooks and want a modern UX
• You already have a warehouse and want a great exploration layer
• Scheduled or on-demand refresh is sufficient

Choose Fastero if:

• You want event-driven architecture, not scheduled batch jobs
• Your workflows are operational (detect → alert → act), not just exploratory
• You care about integrated tools (notebooks + apps + dashboards + workflows)
• You need production-grade APIs with versioning and governance
• You want PII detection, audit logs, and SQL guardrails without paying enterprise add-on fees

The honest truth about "real-time"

Here's what we've learned after years of building analytics tools:

"Real-time" is not a binary. It's a spectrum. A dashboard that updates every 5 minutes might be "real-time enough" for executive reporting, but useless for fraud detection.

Most platforms excel at 1-2 dimensions. Databricks is incredible at streaming ingestion and processing, but you'll still schedule most dashboards. Snowflake has amazing query performance, but you're orchestrating real-time workflows externally. Hex makes collaboration seamless, but triggers aren't native.

The right platform depends on your use case. If you're training ML models on streaming data, Databricks wins. If you're building SQL dashboards for a 100-person company, Snowflake is probably simpler. If you're an analyst sharing insights with stakeholders, Hex is delightful. And if you're building operational workflows where data changes need to trigger immediate actions, that's where Fastero fits.

Try it yourself

We offer a free 30-day trial (no credit card required). Connect your warehouse, set up a trigger, and see what event-driven analytics feels like.

👉 Start your free trial

💬 Book a Demo

---

Last updated: November 2025. Platform capabilities evolve quickly—always verify features with official docs and your specific workload.