With these requirements in mind, we set out to find a graph database that would fit our needs and combine it with existing datastores. We needed a solution that would enable both us and our customers to simply adjust queries when new data is added and get the information in a friendly, intuitive method. Simplicityĭue to the variance in our customers’ needs, adjusting our system logic to match each use case is an endless process. For example, finding which members pushed sensitive data to a repository without a pull request review and then uploaded the built artifact into an artifactory. We also wanted a solution that could enhance the existing data in our system to create more complex detections. For example, enabling the discovery of a build product from a private repository that was uploaded to a public artifactory. This included finding a way to answer unique customer requirements. We needed a flexible way to define and compose advanced detections and insights from all the new data sources we were adding. Our requirements for such a solution included: 1. Therefore, we needed a solution that would allow us to model all of the lifecycle’s parts (such as SCMs, build system and cloud environments), define the initial building blocks, and then allow our customers to easily fine-tune it to exactly fit their needs. Organizations use different tools and processes that define their development lifecycle. When looking at the challenges of securing the development lifecycle, it was clear to us that there was no “one-size-fits-all” solution. This makes them more flexible and effective to use in such cases. Graph databases are designed to effectively query many connected data points, unlike the “classic” relational SQL database where joining multiple tables can incur a significant performance penalty. For Cycode, a relevant use case is the need to visualize and understand the relations between an organization’s source code management systems, build systems and large numbers of cloud resources that make up their development lifecycle. Knowledge Graph enables storing data in a graph model and using graph queries to intuitively explore highly connected datasets. Another strong graph use case is a Knowledge Graph. However, they recently gained traction because they provide a good solution for use cases such as social networking, recommendation systems and fraud detection. ![]() In fact, they were already introduced 20 years ago, in the early 2000s. The graph is the element that links together the data in “relationships”, and then retrieves them, from nodes, edges, and properties. What is a Graph Database?Ī graph database (sometimes referred to as GDB or GraphDB) is a database that uses graph structures to represent and store data, enabling semantic queries of the data points. This blog post will compare the four graph databases that we tried: AWS Neptune, Neo4J, ArangoDB and RedisGraph, and explain why we eventually chose ArangoDB for our needs.īut first, let’s understand what a Graph Database is. In the process, we experimented with several graph databases, until we found the one that fit us the most. We needed these capabilities for our own internal use and also as a solution for giving our customers a flexible way to query their data. Here at Cycode, we were searching for a graph database to organize our data and to enable complex detections and insights. ![]() In traditional relational SQL databases, this would be typically modelled as a large number of tables. Such databases excel at querying data that is related to each other via a long chain of connections. ![]() Graph databases are a kind of database that uses graph structure for semantic queries on nodes and edges, as well as properties to model and persist data.
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