Graph + Search
Unite Graph and Search for Seamless Innovation. Elevate Development Speed and Insights with Integrated Full-text Search, Transforming Data Relationships into Actionable Intelligence.
“Using ArangoDB to combine graph and search capabilities within the same use case, we’ve dramatically simplified development.”
Get started with
Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
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Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.
Unified Data Insights
With
ArangoDB’s Integrated Full-Text Search not only provides unified access to different data models but also facilitates cross-model queries. Imagine uncovering hidden relationships between entities in graphs and associating them with textual context with no added integration.
Why it Matters: A holistic view of your data enables more informed decisions by identifying hidden patterns.
Imagine a retail scenario - relationships between customers, their purchase history, and interactions are stored in graph. Customer reviews, social media posts, and other textual data are stored within the same database using document storage. A single query blends both for instant insights.
Without
Some legacy graph database vendors might recommend integrating external full-text search engines alongside their graph databases to enhance text-based querying.
Maintaining synchronization between the graph database and the external search engine becomes complex. Changes in the graph data need to be accurately reflected in the search index to ensure up-to-date search results.
KEY QUESTION: How would the ability to perform integrated full-text searches across diverse data types enhance your organization's capacity to uncover insightful patterns and make informed decisions?
Efficient Development
With
ArangoDB's integrated approach empowers developers with a single query language to seamlessly combine graph traversal, document retrieval, and full-text search within a single query. This streamlines development efforts by eliminating complex data synchronization and query stitching.
WHY IT MATTERS: A simplified development process enables your development team to innovate faster and respond more swiftly to user needs.
Imagine introducing new features and better user experience to a healthcare application. When a new requirement arises, such as improving the search for rare medical conditions, developers an swiftly integrate advanced full-text search capabilities.
Without
In the absence of integrated full-text search, developers are pulled away from their core tasks and have to spend valuable time integrating a separate search solution. This also introduces complexities in development and maintenance.
KEY QUESTIONS: How might the streamlined development process, achieved through integrated full-text search capabilities, contribute to more efficient feature development and deployment within your applications?
How would this benefit your overall developer efficiency by building features rather than managing complex integrations?
Advanced Search Capabilities
With
ArangoDB's integrated full-text search leverages an extremely mature and powerful indexing and querying architecture. Specifically, ArangoDB uses a combination of various text analysis techniques such as tokenization, stemming, and linguistic analysis to break down textual content into meaningful units. These units are indexed and stored efficiently within ONE unified database, allowing for faster and more accurate text-based searches.
WHY IT MATTERS: Advanced search capabilities matter because they enable users to perform nuanced queries on textual content.
With ArangoDB's advanced search, users can find not only exact matches but also related terms, synonyms, and contextual understanding.
Without
To compensate for their limited search capabilities, some legacy graph databases might recommend integrating external search engines. This involves adding another vendor for full-text search alongside the graph database to handle text-based queries.
However, this workaround introduces complexities in terms of data synchronization, query coordination, and maintaining multiple systems.
KEY QUESTIONS TO ASK: How would your graph-oriented applications suffer with only rudimentary search features?
What would integrating a third party search solution mean in terms of cost and complexity?
Operational Costs Savings
With
ArangoDB eliminates the need for maintaining separate systems for different data models and search functionalities. The most obvious cost savings is clear - no additional vendors to license or manage, and no integration costs.
But there’s more. One of the hidden cost savings of integrated full-text search is that resource utilization is optimized; there’s no need to allocate resources to multiple systems. This leads to better hardware utilization and more efficient scaling strategies.
WHY IT MATTERS: By reducing infrastructure and maintenance expenses, you can allocate resources more strategically to core business activities, innovation, and growth initiatives.
Without
Maintaining separate systems for databases and search engines can lead to increased operational costs.
The inability to consolidate data storage and search functionality within a single platform increases the need for separate licensing, infrastructure, and operational resources.
KEY QUESTIONS: In your unique situation, what would be the specific vectors of savings from not dealing with this extra overhead?
How might these cost savings increase your ability to invest in innovation and growth initiatives?
Get started with
Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.