Data Engineering & Analytics Infrastructure
Apache Spark
Distributed analytics engine with machine learning
- SF Score
- 8.3/10
- Pricing
- Free
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2 of 4 tools selected
Data Engineering & Analytics Infrastructure
Qlik Talend
AI-ready data integration and quality
- SF Score
- 8.3/10
- Pricing
- Contact sales
Add third toolCompare up to 4 side by side
Compare features, strengths, trade-offs, and best-fit use cases.
Comparing
Where each one leads
Read straight from the published figures below — not a ranking, and not a recommendation.
Highest SF Score
Qlik Talend8.3/10
2 of 2 are scored
Only one with a free plan
Apache SparkFree plan
free-plan status established for all 2
At a glance
The short version
The facts each tool publishes, lined up so you can scan down a column. Anything the catalog does not establish is marked as such rather than counted against the tool.
Apache Spark
- SF Score
- 8.3 / 10
- Starts at
- $0/mo
- Free plan
- Available
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Qlik Talend
- SF Score
- 8.3 / 10
- Starts at
- Not established
- Free plan
- Not offered
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Key differences
Where these tools actually diverge
The decision dimensions that separate this set, with each tool's own position stated plainly. No winner is declared — fit depends on your workflow.
Positioned for
Apache Spark
Data Engineering & Analytics Infrastructure
Qlik Talend
Data Engineering & Analytics Infrastructure
Pricing model
Apache Spark
Free
Qlik Talend
Contact sales
SoftFinders Score
Apache Spark
8.3 / 10
Qlik Talend
8.3 / 10
Feature comparison
Capability matrix
Grouped by area. Every state carries a word as well as a colour, and anything the catalog has not established stays neutral rather than counting as a missing feature.
2 rows differ 3 identical
| Capability | | |
|---|---|---|
| Overview | ||
| SF Score | 8.3 / 10Value | 8.3 / 10Value |
| Pricing model | FreeValue | CustomValue |
| Starts at | $0/moValue | Not established |
| Free plan | Yes | No |
| Category | AI Analytics Software › Data Engineering & Analytics InfrastructureValue | AI Analytics Software › Data Engineering & Analytics InfrastructureValue |
Scroll the matrix sideways to see every column.
Listed by one tool only
12 capabilities are published by a single tool in this set. That does not mean the others lack them — only that the catalog has not established an answer either way.
- Distributed ProcessingRun large jobs across compute clusters
- Spark SQLQuery structured data with SQL syntax
- MLlib LibraryBuild scalable machine learning pipelines securely
- Structured StreamingProcess streaming data with incremental computation
- GraphX AnalyticsAnalyze graph data at distributed scale
- Language SupportUse Python, Scala, Java, or R
- Data MovementMove data across cloud and enterprise systems
- Data QualityProfile, cleanse, and validate trusted data
- Pipeline DesignBuild repeatable integration and transformation jobs
- Governance ToolsManage metadata, cataloging, and lineage controls
- AI FoundationPrepare governed data for AI workflows
- Cloud OptionsUse Qlik Talend Cloud subscription tiers
Use cases
What each tool is built for
The workflows each tool publishes for itself. Cards appear only for tools that list use cases.
Apache Spark
- Process large datasets across compute clusters
- Build machine learning pipelines with MLlib
- Run structured streaming data transformations
- Query lakehouse tables using Spark SQL
- Support data engineering platform workloads
+3 more on the Apache Spark profile
Qlik Talend
- Build trusted AI data foundations
- Move data into cloud warehouses
- Improve data quality for reporting
- Govern data pipelines across teams
- Support enterprise migration and modernization
+1 more on the Qlik Talend profile
Pros & cons
Strengths and compromises
Each tool's published strengths and the compromises that come with them, as supplied — not scored or ranked against each other. Longer lists fold; nothing is left out.
Apache Spark
Pros
- Scales large data workloads across distributed clusters
- Supports multiple languages for analytical data workloads
- Open-source ecosystem reduces direct vendor lock-in risks
- Multiple cluster managers are officially supported
- Includes SQL ML and graph libraries
Cons
- Requires engineering expertise to operate well in production
- Not a business-user analytics application by default
- Cluster costs need careful ongoing management discipline
- Authentication is not enabled by default
- SparkR is deprecated in version 4.2
- Spark Connect differs from Classic execution semantics
Qlik Talend
Pros
- Strong data quality and integration capabilities for enterprises
- Qlik ownership strengthens analytics ecosystem alignment clearly
- Good fit for AI-ready data foundation programs
Cons
- Packaging can confuse former Talend buyers initially
- Custom pricing requires sales-led scoping and review
- Implementation needs data engineering ownership and governance
Pricing
What each one costs
Published pricing signals only. Where a tool has not established a price, that is what it says — never a zero and never "free".
Apache Spark
- Model
- Free
Open-source Apache project; infrastructure costs depend on hosting and managed services.
See full pricingQlik Talend
- Model
- Custom
Contact Sales
See full pricingNext step
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