How DataWalk Compares to…
Graph Databases, Knowledge Graphs, and Link Analysis Tools
DataWalk is a highly advanced graph analytics application and platform, built on top of a hybrid graph-relational database. The breadth of DataWalk capabilities invites comparison to various types of graph solutions available on the market. See how DataWalk compares to them.
DataWalk vs. Graph Databases
Graph databases store and represent data using graph structures, providing a flexible and intuitive way for you to model and query complex relationships within the data. Because they’re designed to handle interconnected and complex data efficiently, graph databases are particularly useful in scenarios where it’s not just the data that’s important, but also the relationships between its entities, making them ideal for certain use cases compared to traditional relational databases.
As with typical stand-alone graph databases, DataWalk lets you perform sophisticated queries to gain insights from the connected nature of the data. But as a full-fledged data analysis platform, DataWalk goes considerably beyond the performance and functionality of a stand-alone graph database, providing a number of graph analytics capabilities on top of a unique graph-relational database hybrid.
Graph Database | DataWalk | |
What It Is | A database | A full-stack data analysis application |
Programming Required? | Yes | No, querying can be done visually or via API |
External Data Integration | Requires additional software to connect to and input from external data sources | Has built-in capability to integrate data from external sources with no additional software |
Complex Graph Queries | Poor | Excellent |
Big Data Graph Algorithm Performance | Poor unless data fits in memory | Excellent |
Data Cleaning & Transformation | Requires data to be cleaned and transformed with a separate ETL tool prior to loading | Accepts data as-is, cleaning and transforming data after loading |
Data Discovery | Requires writing new query, and waiting on new results, with any change to analysis path | Offers easy traversal and filtering of data on the fly, and quick changes to analysis path |
Data Lineage | Requires custom application to be written on top of the database to maintain lineage | Has data lineage capability built in |
Scoring | Requires manually assigning weights to each of numerous queries to create a score | Offers easy implementation and quick calculation of scores |
Unstructured Content | Offers no support for unstructured content | Enables storage and linking of images, PDFs, doc files, and other unstructured content |
DataWalk vs. Graph Databases
Graph databases store and represent data using graph structures, providing a flexible and intuitive way for you to model and query complex relationships within the data. Because they’re designed to handle interconnected and complex data efficiently, graph databases are particularly useful in scenarios where it’s not just the data that’s important, but also the relationships between its entities, making them ideal for certain use cases compared to traditional relational databases.
As with typical stand-alone graph databases, DataWalk lets you perform sophisticated queries to gain insights from the connected nature of the data. But as a full-fledged data analysis platform, DataWalk goes considerably beyond the performance and functionality of a stand-alone graph database, providing a number of graph analytics capabilities on top of a unique graph-relational database hybrid.
Graph Database | DataWalk | |
What It Is | A database | A full-stack data analysis application |
Programming Required? | Yes | No, querying can be done visually or via API |
External Data Integration | Requires additional software to connect to and input from external data sources | Has built-in capability to integrate data from external sources with no additional software |
Complex Graph Queries | Poor | Excellent |
Big Data Graph Algorithm Performance | Poor unless data fits in memory | Excellent |
Data Cleaning & Transformation | Requires data to be cleaned and transformed with a separate ETL tool prior to loading | Accepts data as-is, cleaning and transforming data after loading |
Data Discovery | Requires writing new query, and waiting on new results, with any change to analysis path | Offers easy traversal and filtering of data on the fly, and quick changes to analysis path |
Data Lineage | Requires custom application to be written on top of the database to maintain lineage | Has data lineage capability built in |
Scoring | Requires manually assigning weights to each of numerous queries to create a score | Offers easy implementation and quick calculation of scores |
Unstructured Content | Offers no support for unstructured content | Enables storage and linking of images, PDFs, doc files, and other unstructured content |
DataWalk vs. Knowledge Graphs
A typical knowledge graph application is a type of knowledge management system that represents information as interconnected nodes in a graph structure. It captures and organizes data using entities, attributes, and relationships, enabling users to access and analyze data efficiently. Knowledge graphs fall into several categories, reflecting a range of applications, structures, and methodologies. One important categorization is whether the knowledge graph is a standalone application or part of a broader solution offering additional capabilities to address specific use cases.
Most standalone knowledge graph products offer a common core of capabilities such as entity mapping, relationship analysis, and data integration, and are designed for specific use with minimal setup and customization. DataWalk offers all of the same knowledge graph capabilities found in many of these standalone applications but integrated into a full-featured graph analytics application and platform. The DataWalk knowledge graph works in synergy with the platform’s other technologies, enhancing the solution's effectiveness, and providing end-to-end capabilities that extend beyond what a standalone knowledge graph product can offer.
Capability | Standalone Knowledge Graph Software | DataWalk Platform (with integrated Knowledge Graph capabilities) |
Flexible Data Model | ✅ | ✅ |
Big Data Querying | Weak | ✅ |
Entity Resolution | ✅ | ✅ |
Scoring | ❌ | ✅ |
AI/ML / Embeddings | ✅ | ✅ |
Investigation Toolkit | ❌ | ✅ |
Integrated Maps | ❌ | ✅ |
360-Degree Summaries | ❌ | ✅ |
Reports | ❌ | ✅ |
Inferencing | Advanced* | Basic |
Histograms | ❌ | ✅ |
Collaboration | ❌ | ✅ |
Business User Interfaces | Limited | Comprehensive |
Knowledge Graph Output Operationalization | Limited | Excellent |
*only where based on semantic-web
DataWalk vs. Knowledge Graphs
A typical knowledge graph application is a type of knowledge management system that represents information as interconnected nodes in a graph structure. It captures and organizes data using entities, attributes, and relationships, enabling users to access and analyze data efficiently. Knowledge graphs fall into several categories, reflecting a range of applications, structures, and methodologies. One important categorization is whether the knowledge graph is a standalone application or part of a broader solution offering additional capabilities to address specific use cases.
Most standalone knowledge graph products offer a common core of capabilities such as entity mapping, relationship analysis, and data integration, and are designed for specific use with minimal setup and customization. DataWalk offers all of the same knowledge graph capabilities found in many of these standalone applications but integrated into a full-featured graph analytics application and platform. The DataWalk knowledge graph works in synergy with the platform’s other technologies, enhancing the solution's effectiveness, and providing end-to-end capabilities that extend beyond what a standalone knowledge graph product can offer.
Capability | Standalone Knowledge Graph Software | DataWalk Platform (with integrated Knowledge Graph capabilities) |
Flexible Data Model | ✅ | ✅ |
Big Data Querying | Weak | ✅ |
Entity Resolution | ✅ | ✅ |
Scoring | ❌ | ✅ |
AI/ML / Embeddings | ✅ | ✅ |
Investigation Toolkit | ❌ | ✅ |
Integrated Maps | ❌ | ✅ |
360-Degree Summaries | ❌ | ✅ |
Reports | ❌ | ✅ |
Inferencing | Advanced* | Basic |
Histograms | ❌ | ✅ |
Collaboration | ❌ | ✅ |
Business User Interfaces | Limited | Comprehensive |
Knowledge Graph Output Operationalization | Limited | Excellent |
*only where based on semantic-web
DataWalk vs. Link Analysis Tools
Link analysis software is used to analyze and visualize relationships between different data elements, such as people, organizations, or events. By representing the relationships as link charts, link analysis tools enable users to visualize and understand connections and patterns within complex datasets.
DataWalk provides the link analysis functionality of typical link analysis solutions but then goes far beyond with more advanced capabilities for big data integration, analysis of big data, entity resolution, enterprise functionality, and more.
Capability | Standalone Link Analysis Software | DataWalk Platform |
Link analysis | ✅✅✅ | ✅✅✅ |
Complex querying big data | ✅ | ✅✅✅ |
Data integration | ✅ | ✅✅✅ |
Data platform (source of clean, connected data) | ✅ | ✅✅✅ |
Generate risk scores | ❌ | ✅✅✅ |
Granular security for complex data | ✅ | ✅✅✅ |
Entity resolution | ✅ | ✅✅✅ |
Machine learning integration | ✅ | ✅✅ |
Present 360-degree summary of anything | ✅ | ✅✅✅ |
DataWalk vs. Link Analysis Tools
Link analysis software is used to analyze and visualize relationships between different data elements, such as people, organizations, or events. By representing the relationships as link charts, link analysis tools enable users to visualize and understand connections and patterns within complex datasets.
DataWalk provides the link analysis functionality of typical link analysis solutions but then goes far beyond with more advanced capabilities for big data integration, analysis of big data, entity resolution, enterprise functionality, and more.
Capability | Standalone Link Analysis Software | DataWalk Platform |
Link analysis | ✅✅✅ | ✅✅✅ |
Complex querying big data | ✅ | ✅✅✅ |
Data integration | ✅ | ✅✅✅ |
Data platform (source of clean, connected data) | ✅ | ✅✅✅ |
Generate risk scores | ❌ | ✅✅✅ |
Granular security for complex data | ✅ | ✅✅✅ |
Entity resolution | ✅ | ✅✅✅ |
Machine learning integration | ✅ | ✅✅ |
Present 360-degree summary of anything | ✅ | ✅✅✅ |
