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Sparksee

sparsity-technologies.com

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Spec score 8.3 #4 of 26 in Mobile Databases

What it is

Sparksee is an embeddable graph database for applications that need to represent and analyse relationships among entities. It works against a database file from within an application process, so there is no cluster or query server to provision. The maker describes its compact bitmap-based representation as suited to graph computations in mobile and embedded environments. Built-in algorithms cover traversal, shortest paths, connectivity, community detection, and PageRank. Its API supports importing and exporting graph data, scripting, and Sparksee Cypher queries; Sparksee Algebra is also listed. API references cover.NET, Java, C++, Objective-C, Python, and openCypher. Sparksee supports ACID transactions, disk-level encryption, and recovery after database corruption. Keeping data on a user's device can support privacy-preserving apps and reduce reliance on cloud connectivity. Platforms include Android, iOS, Linux, macOS, and Windows, with API and self-hosted options. Eligible academic users can apply for a free licence; commercial licence pricing is on request.

Spec sheet

What it does
Sparksee is an embeddable graph database management system for representing and analysing relationships between entities.Read 15 Oct 2026 · sparsity-technologies.com
Product
Sparksee is an embeddable graph database management system for representing and analysing complex relationships between entities.Read 7 Oct 2026 · sparsity-technologies.com
Platforms
The trial request form lists Android, iOS, Linux, Mac, Windows Desktop, and Windows Store as operating-system choices.Read 7 Oct 2026 · sparsity-technologies.com
Deployment
Sparksee runs from an application process against a database file, with no cluster or query server to provision.Read 7 Oct 2026 · sparsity-technologies.com
Security
The product page says Sparksee offers disk-level encryption to protect data if a device is stolen, lost, or sold.Read 7 Oct 2026 · sparsity-technologies.com
Privacy
The product page says data can remain on a user device to support privacy-preserving applications.Read 15 Oct 2026 · sparsity-technologies.com
Support
Trial applicants receive a build, sample project and access to an engineer for modelling questions; technical support is provided through the documentation and support sections.Read 15 Oct 2026 · sparsity-technologies.com
Headquarters
Barcelona, SpainRead 28 Sep 2026 · sparsity-technologies.com
Algorithms
Built-in graph algorithms are provided as out-of-the-box tooling for application development.Read 15 Oct 2026 · sparsity-technologies.com
API capabilities
The API documentation covers database construction, node and edge operations, attributes, collections, import/export, scripting and graph algorithms.Read 15 Oct 2026 · sparsity-technologies.com
Bitmap architecture
The maker says bitmap-based structures support compact storage and high-performance graph computation in embedded and mobile environments.Read 15 Oct 2026 · sparsity-technologies.com
Company activities
The company LinkedIn page says Sparsity collaborates on EU-funded projects including LDBC, SoMatch and CoherentPaaS.Read 15 Oct 2026 · es.linkedin.com
Company facts
Crunchbase describes Sparsity Technologies as a private Barcelona spin-off from UPC-Barcelonatech, founded in 2010 by Josep Lluis Larriba Pey, with a listed headcount range of 1–10.Read 15 Oct 2026 · crunchbase.com
Connectivity resilience
Local graph processing lets mobile analytics continue when cloud connectivity is poor or unavailable.Read 15 Oct 2026 · sparsity-technologies.com
Data and queries
The API supports graph data import and export, scripting, and Sparksee Cypher queries, described as an implementation of openCypher with particularities.Read 7 Oct 2026 · sparsity-technologies.com
Deployment classification
GDB-Engines classifies Sparksee as self-hosted while also supporting embedding in an application.Read 15 Oct 2026 · gdb-engines.com
Documentation depth
Sparsity publishes a starting guide, complete user manual, high-availability material and language-specific reference guides in PDF and browsable HTML.Read 15 Oct 2026 · sparsity-technologies.com
Embedded deployment
The engine runs inside an application process against a database file, without requiring a cluster or query server.Read 15 Oct 2026 · sparsity-technologies.com
Encryption
Disk-level encryption is provided to protect data if a device is lost, stolen or sold.Read 15 Oct 2026 · sparsity-technologies.com
Engineering standards
The developer page states that Sparsity works with ASPICE and MISRA standards.Read 15 Oct 2026 · sparsity-technologies.com
Graph export integration
The API documentation describes export to Graphviz and GraphML and discusses third-party Blueprints integration.Read 15 Oct 2026 · sparsity-technologies.com
Graph model
Sparksee models data as a labelled, attributed multigraph with directed or undirected edges, typed objects, attributes and loops.Read 15 Oct 2026 · sparsity-technologies.com
History
Wikipedia records development beginning in 2006, a first release in 2008, the DEX name change to Sparksee in February 2014 and version 6.0 in 2021.Read 15 Oct 2026 · en.wikipedia.org
Independent benchmark limit
The same study found certain incoming-edge queries on Freebase samples exhausted available RAM and swap space in Sparksee, linked by the authors to a Gremlin implementation problem.Read 15 Oct 2026 · researchgate.net
Independent benchmark praise
A microbenchmark study reported Sparksee among the fastest systems for insert, update and delete operations, generally completing them below 100 milliseconds.Read 15 Oct 2026 · researchgate.net
Independent database profile
DBDB.io describes Sparksee as a native bitmap-based graph database with full ACID transactions, used especially for social-network analysis.Read 15 Oct 2026 · dbdb.io
Installation
Packages are downloaded and unpacked without a general installer, except for an Objective-C MacOS framework installer; Java packages can also be obtained through Apache Maven.Read 15 Oct 2026 · sparsity-technologies.com
Language bindings
Documented bindings are available for .NET, Java, C++, Objective-C and Python, with an openCypher reference.Read 15 Oct 2026 · sparsity-technologies.com
Latency
Local graph computation is intended to reduce response latency by avoiding a remote server round trip.Read 15 Oct 2026 · sparsity-technologies.com
Licence model
Sparksee is distributed under proprietary licensing with free academic, evaluation and certain startup arrangements rather than an open-source licence.Read 15 Oct 2026 · sparsity-technologies.com
Limit: API surface
GDB-Engines lists no REST API, no CLI, no GUI, no stored procedures and no authentication or authorization features.Read 15 Oct 2026 · gdb-engines.com
Limit: distribution
GDB-Engines lists no sharding, replication, distributed queries, automatic rebalancing or high-availability support.Read 15 Oct 2026 · gdb-engines.com
Limit: G2 reviewer complaint
The sole G2 review complained about glitches and missing peripheral-view integration.Read 15 Oct 2026 · g2.com
Limit: high availability
The user manual says high-availability features were deprecated in version 6.0, had not been updated since 5.2.3 and may eventually be removed.Read 15 Oct 2026 · sparsity-technologies.com
Memory footprint
Sparksee uses compact data representation and management to reduce resource requirements.Read 15 Oct 2026 · sparsity-technologies.com
Notable limits
The maker prices commercial licences based on deployment details, including platforms and whether the engine ships to customers; it publishes no fixed commercial price on the licensing page.Read 7 Oct 2026 · sparsity-technologies.com
Performance
The maker says Sparksee uses compact bitmap-based graph representations to support high-performance graph computations in mobile and embedded environments.Read 7 Oct 2026 · sparsity-technologies.com
Praise: ease of graph access
The sole G2 reviewer said Sparksee makes it easier for integrated software to access graph-oriented AI data.Read 15 Oct 2026 · g2.com
Rating on G2
0.0 out of 5 from 1 reviewRead 15 Oct 2026 · g2.com
Recovery
Sparsity states that Sparksee can recover databases after corruption and is resilient to crashes.Read 15 Oct 2026 · sparsity-technologies.com
Security and privacy compliance
Sparsity's privacy policy says it processes personal data under the GDPR and Spain's LOPDGDD and has implemented technical and organizational security policies.Read 7 Oct 2026 · sparsity-technologies.com
System requirements
Supported targets include 32- and 64-bit Windows, 32- and 64-bit Linux, Mac OS X, Android, iOS and QNX; Java requires JVM 5 or newer, .NET requires 2.0 or newer, C++ requires libc6 or Visual Studio 2005 or newer, and Python requires 2.7.Read 15 Oct 2026 · sparsity-technologies.com

Plans and prices

Academic licence Free Free to use for teachers, PhD students and university research groups; manual validation required Academic use · valid university or school ID Read 15 Oct 2026 · sparsity-technologies.com
Free trial Price not published 1-month subscription for free Trial build · sample project · platform and API-language selection Read 15 Oct 2026 · sparsity-technologies.com
Industry Priced on request Request licence Enterprise real-time graph applications · terms sized by deployment Read 15 Oct 2026 · sparsity-technologies.com
Startup Priced on request Request licence For SMEs · first 10 startups get a 3-year licence waiver Read 15 Oct 2026 · sparsity-technologies.com

Every Sparksee plan and price →

Strong points and limits

Strong points

  • Runs against a database file without a query server.
  • Includes traversal, community detection, and PageRank algorithms.
  • Supports ACID transactions and disk-level encryption.
  • API references cover six languages and openCypher.
  • Free academic licence available to eligible applicants.

Limits

  • Commercial licence pricing is on request.
  • Academic licence requires valid school or university ID.
  • Commercial terms depend on platforms and deployment details.

Who it suits

Sparksee suits developers building graph-powered applications for mobile, embedded, or other environments where a database file can run within the application process. Its academic licence is for teachers, PhD students, and university research group members, subject to application and validation.

Sparksee in depth

Read the full write-up

Sparksee is an embeddable graph database for developers building mobile and embedded applications around connected data. It is strongest when graph analysis needs to happen alongside the app, on a device or in a local process. Choose it for that architecture; look elsewhere if you need a separately provisioned graph service or predictable commercial pricing.

Overview

Sparksee operates within an application process and stores data in a database file, so deployment does not require a cluster or query server. That can simplify the infrastructure for local-first software and reduce reliance on cloud connectivity. The trade-off is that Sparksee is an embedded component, not a remote database service for applications to share.

The maker says its compact bitmap-based graph representation supports high-performance computation in mobile and embedded environments. That focus fits products that need to work with relationships close to where data is stored; actual fit still depends on the workload.

Key features

Built-in algorithms cover traversal, shortest path, connectivity, community detection, and PageRank. These give developers graph-analysis capabilities within the database. Sparksee also supports ACID transactions, graph import and export, and scripting.

Queries can use Sparksee Cypher (SCL) or Sparksee Algebra. SCL implements openCypher with particularities, so teams relying on exact openCypher behavior should account for differences. API references cover.NET, Java, C++, Objective-C, and Python, with openCypher also represented in the developer materials.

Disk-level encryption is intended to protect data if a device is lost, stolen, or sold, and the product can recover a database in the event of corruption. Keeping data on-device can also support privacy-preserving apps, though it means the application itself owns the local storage context.

Pricing

Sparksee uses a freemium model, with an academic licence at free. It is for teachers, PhD students, and university research-group members; applicants must provide a valid university or school ID, with manual validation. This is a targeted free route, not a general no-cost commercial plan.

The Startup plan has custom pricing and is aimed at SMEs building apps. The first 10 startups using Sparksee in an app receive a 3-year licence waiver, a meaningful early-stage incentive if eligible. Industry also has custom pricing, with terms sized by company size and deployment for enterprise-level, real-time graph applications. Commercial licence costs depend on deployment details, including supported platforms and whether the engine ships to customers, so teams should request terms before committing.

A free trial is available. Trial applicants receive a trial build and access to an engineer for modelling questions, which may help teams assess how their graph model fits before seeking a commercial licence.

Platforms

Sparksee supports Android, iOS, Linux, macOS, and Windows, as well as API and self-hosted deployments. The breadth suits teams targeting mobile and desktop environments or integrating the engine through its APIs. Its embedded model is the defining deployment choice rather than a hosted service.

Who it's for

Sparksee is a strong fit for developers building mobile or embedded applications that need to represent complex relationships and run graph algorithms locally. It is also relevant to academic users who meet the licence requirements. Teams that need a shared remote service, or that cannot budget around deployment-specific commercial terms, should consider a different architecture.

Pros and cons

Pro: Embedded, file-based operation avoids provisioning a cluster or query server.

Pro: Built-in graph algorithms and two query options support analysis without making every graph operation application logic.

Pro: Disk-level encryption and database recovery address device loss and corruption concerns.

Pro: Academic access is free for eligible applicants, and qualifying startups may receive a 3-year licence waiver.

Con: It is not a separately provisioned graph service, which rules it out for teams seeking that model.

Con: Commercial pricing depends on deployment and requires a request, making costs harder to establish upfront.

Con: Sparksee Cypher has particularities relative to openCypher, which may require query adjustments.

Alternatives

For a mobile database shortlist, see Mobile Databases; for a graph-focused comparison, see Graph Databases.

ObjectBox is worth considering for a mobile and embedded database alternative with a free core database plan and a similar range of listed platforms.

RxDB is a better match for teams prioritizing replication and real-time sync, with a free core plan and broad platform support.

Ditto may suit teams that want cloud device connections and storage quotas in a free tier; Sparksee instead centers on embedded graph storage and analysis.

Firestore is an option for teams seeking a document database across Android, iOS, API, and web platforms, with listed daily read and write allowances.

Hive offers a free plan with storage and workspace-member caps, which may suit teams whose needs are closer to that plan's limits than to graph-specific analysis.

Espresso is a free Android UI testing option for a single target app, rather than a graph database.

Drift is another free option in the mobile database category.

Realm is a free alternative whose technology is now known as MongoDB Atlas Device SDKs.

Verdict

Choose Sparksee if your mobile or embedded application needs local graph storage plus built-in analysis, and the academic licence or a deployment-specific commercial quote fits your situation. Its combination of file-based operation, graph algorithms, and device-level protections is the reason to choose it. Look elsewhere if you need a remote graph service or need commercial costs fixed before discussing deployment.

Verdict

Sparksee combines embedded graph storage, query options, and graph algorithms without requiring a separate query server. Academic access is free for eligible applicants, while commercial licensing requires a request.

Where Specifiction places it

Alternatives to SparkseeAll alternatives →