Technology · head to head
Apache Hadoop vs Heap

Apache Hadoop
Technology
The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Apache Hadoop the free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.; Heap no built-in A/B testing or feature flags; requires integration with separate tools for experimentation
- They diverge on capability: Apache Hadoop covers HDFS, Heap covers Autocapture.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Apache Hadoop and Heap actually diverge.
| Attribute | Apache Hadoop | Heap |
|---|---|---|
| Pricing model | open-source | Unknown |
| Platforms | Web | Web, iOS, Android |
| Founded | Unknown | 2013 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Technology).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Apache Hadoop
- HDFS
- YARN
- MapReduce
- HDFS federation and high availability
- Kerberos security
- Rack awareness
- S3A and object store connectors
- Ecosystem compatibility
Only in Heap
- Autocapture
- Retroactive analytics
- Session replay
- Funnel analysis
- User segmentation
- Path analysis
- Data science
- Virtual events
What people use each for
The jobs each tool is most often brought in to do.
Apache Hadoop
- Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Heap
- Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Heap
- Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Heap
- Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Heap
Heap
- User behavior analysisnot Apache Hadoop
- Conversion optimizationnot Apache Hadoop
- Product adoptionnot Apache Hadoop
- Customer journey mappingnot Apache Hadoop
- A/B testing analysisnot Apache Hadoop
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Apache Hadoop
- The free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.
- HDFS couples storage to compute, so adding capacity means buying whole nodes with CPU and memory you may not need, and the entire industry moved to object storage precisely because it lets the two be bought separately.
- The NameNode holds all filesystem metadata in memory, so a cluster with tens of millions of small files exhausts heap long before it exhausts disk, and the remedy is a file compaction job that somebody has to write, schedule and own indefinitely.
- Operating it is a distinct specialism covering Kerberos, YARN queue tuning, JVM garbage collection and the compatibility matrix between Hive, HBase, Ranger, Oozie and the core, and an upgrade touches all of them at once rather than one at a time.
- MapReduce is maintained for compatibility rather than actively developed, and new work goes to Spark or Flink, so a job written against MapReduce today is written against an API that will not gain anything further.
- Hiring is against you: the talent pool has moved to cloud data platforms over the past decade, so a Hadoop estate increasingly depends on a small number of individuals, which makes it a succession risk before it is a technical one.
Heap
- No built-in A/B testing or feature flags; requires integration with separate tools for experimentation
- Group analytics and advanced features require a sales conversation, not self-serve
- Cloud-only deployment; no self-hosted option for data security or compliance requirements
- Session replay lacks developer debugging tools compared to PostHog
- Pricing for Growth and Pro plans requires direct sales contact; no transparency on how pricing scales
Pricing, plan by plan
Apache Hadoop
FreeNo published plan breakdown. See the Apache Hadoop review.
Heap
Free- FreeFree
- Up to 10,000 monthly sessions
- Basic charts
- 6 months data history
- Growth$undefined/custom
- Custom session pricing
- Sense AI assistant
- 12 months data history
- Pro$undefined/custom
- Custom session pricing
- Account analytics
- Engagement matrix
- Premier$undefined/custom
- Custom session pricing
- Data warehouse integration
- Unlimited projects
Which should you pick?
Choose Apache Hadoop if
- You need hdfs.
- You want to start without paying.
- You also want yarn.
Choose Heap if
- You need autocapture.
- You want to start without paying.
- You work on Web, iOS, Android.
- You also want retroactive analytics.
Questions people ask
- Is Apache Hadoop or Heap better?
- Neither clearly leads. Apache Hadoop starts at Free and Heap at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Hadoop or Heap?
- Apache Hadoop starts at Free and Heap at Free.
- Does Apache Hadoop or Heap run on more platforms?
- Apache Hadoop runs on Web. Heap runs on Web, iOS, Android.
- Can I use Apache Hadoop for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Hadoop best used for?
- Apache Hadoop is most often used for operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage, running spark or flink under yarn on hardware you already own, using hdfs as the storage layer, keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdiction, maintaining legacy hive and mapreduce workloads during a staged migration to a lakehouse or cloud platform. Of those, operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage and running spark or flink under yarn on hardware you already own, using hdfs as the storage layer are not what Heap is typically brought in for.
- What can Apache Hadoop do that Heap cannot?
- Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Heap covers Autocapture, Retroactive analytics, Session replay, Funnel analysis.
Answered from the vendors’ own pages
Apache Hadoop: Is Hadoop dead?
No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.
Heap: What does Heap's autocapture feature do?
Heap's autocapture is a single code snippet that automatically captures every click, swipe, tap, pageview, and form fill on your website and apps without requiring manual event setup. Once installed, Heap captures the entire digital experience of every user on every platform with no ongoing engineering maintenance needed.
SourceApache Hadoop: Can I still get a free packaged distribution?
Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.
Heap: What are Heap's pricing plans and how much do they cost?
Heap offers a Free plan for up to 10,000 monthly sessions. Growth, Pro, and Premier plans use custom session-based pricing that requires contacting sales for a quote. Free includes basic charts and 6 months data history. Growth adds the Sense AI assistant. Pro adds account analytics. Premier adds data warehouse integration and dedicated customer success management.
SourceApache Hadoop: Do I need Hadoop to run Spark?
No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.
Heap: Does Heap include session replay and A/B testing?
Heap includes integrated session replay showing exactly what users did on your site. However, Heap does not include built-in A/B testing or feature flags. Teams requiring these capabilities must use separate tools or integrate with third-party platforms.
SourceApache Hadoop: What replaced HDFS?
Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.
Heap: What integrations does Heap support?
Heap supports over 100 integrations connecting to business tools including marketing platforms, CRMs, and data warehouses. This allows insights to reach relevant teams and ensures data flows to other business systems automatically.
SourceApache Hadoop: Is it cheaper than the cloud?
It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.
Heap: Does Heap offer self-hosting or is it cloud-only?
Heap is cloud-only and does not offer self-hosted options. Organizations requiring on-premises deployment should consider alternatives like PostHog which supports self-hosting alongside its cloud product.
SourceHeap: What is Sense and how does it help with analytics?
Sense Chat is Heap's AI assistant that enables users to access analytics without extensive technical knowledge. It allows teams to ask questions about user behavior and get answers directly without lengthy onboarding or technical expertise, making insights more accessible to non-technical stakeholders.
SourceRelated pages
More on Apache Hadoop
Other head to heads
- Apache Hadoop vs Asana
- Apache Hadoop vs ClickUp
- Apache Hadoop vs Figma
- Apache Hadoop vs Linear
- Apache Hadoop vs Apache Spark
- Apache Hadoop vs Supabase
- Apache Hadoop vs Thought Machine
- Apache Hadoop vs Trino
- Apache Hadoop vs Storybook
- Apache Hadoop vs Kubernetes
- Apache Hadoop vs PostHog
- Apache Hadoop vs Redis
- Apache Hadoop vs Finxact
- Apache Hadoop vs GitHub
- Apache Hadoop vs Microsoft Edge
- Apache Hadoop vs Netlify
- Apache Hadoop vs Productboard
- Apache Hadoop vs Mixpanel
- Apache Hadoop vs Amplitude
- Apache Hadoop vs Pendo
- Apache Hadoop vs Userpilot
- Apache Hadoop vs Segment
- Apache Hadoop vs RescueTime
- Apache Hadoop vs Lovable
- Apache Hadoop vs Coda
- Apache Hadoop vs Istio
- Apache Hadoop vs Terraform
- Apache Hadoop vs Intercom
- Apache Hadoop vs LaunchDarkly
- Apache Hadoop vs Monday.com
- Heap vs Asana
- Heap vs ClickUp
- Heap vs Figma
- Heap vs Linear
- Heap vs Apache Spark
- Heap vs Supabase
- Heap vs Thought Machine
- Heap vs Trino
- Heap vs Storybook
- Heap vs Kubernetes
- Heap vs PostHog
- Heap vs Redis
- Heap vs Finxact
- Heap vs GitHub
- Heap vs Microsoft Edge
- Heap vs Netlify
- Heap vs Productboard
- Heap vs Mixpanel
- Heap vs Amplitude
- Heap vs Pendo
- Heap vs Userpilot
- Heap vs Segment
- Heap vs RescueTime
- Heap vs Lovable
- Heap vs Coda
- Heap vs Istio
- Heap vs Terraform
- Heap vs Intercom
- Heap vs LaunchDarkly
- Heap vs Monday.com

