White papers, webinars, courses, and other useful resources to start and expand your journey to Graph Done Right.
Exploring Functional Depth in Graph Databases - Navigating the rapidly evolving graph database landscape requires a focus on the functional depth and versatility of modern solutions like ArangoDB. This concise guide is a key resource for those on the path to selecting a graph database that best suits their distinctive needs, offering insights into the unique advantages and advanced features of ArangoDB over legacy graph databases.
With the FBI estimating that cybercrime costs Americans over $6 billion annually and the World Economic Forum predicting it could reach $10.5 trillion by 2025, it’s clear that cybersecurity is a crucial concern for companies of all sizes.
Graph databases provide inherent advantages in scenarios where complex, multi-level relationships between data need to be established and queried at scale for efficient decision-making. ArangoDB’s Graph Done Right approach addresses the challenges faced by organizations in managing interconnected data and empowering a growing number of data users.
This infographic highlights the benefits of using graph databases for network management in today’s complex enterprise networks. The use of graph databases for network management is explained through various capabilities, including troubleshooting network errors, analyzing capacity, cataloging assets, scaling IT operations, optimizing infrastructure design, and planning downtime. It emphasizes that graph databases have been primarily used by data scientists, developers, and architects but have extensive applications in network management. Learn more about the role that graph technologies will play in data innovations, with 80% of data innovations expected to use graph technologies by 2025.
Explore how graph structures empower semantic queries, allowing nuanced storage and representation of intricate data relationships. This whitepaper delves into the transformative capabilities of graph databases in enabling unprecedented analytical depth. From social networks to interconnected data ecosystems, discover how specialized algorithms like graph traversal facilitate efficient analysis and retrieval of relational data, unveiling hidden patterns, recommending connections, and recording significant relational events. Gain insights into leveraging graph databases to optimize your relationship-centric data exploration and enhance decision-making processes!
Graph databases play a critical role, and the need to extract the most value and uncover the hidden meaning from the mountain of data you have in your company is not going anywhere. The visualization and how the data dots are connected help organizations see things in their data like they never have before.
This whitepaper compares relational database management systems (RDBMS) and modern Graph database systems — in particular, MySQL and ArangoDB. It describes their key concepts, contrasts both at the end of each section, and concludes by explaining what sets ArangoDB
Across industries, fraud is a growing problem resulting in a global annual loss of $3.7 trillion. Fraudsters became more sophisticated in hiding their activities by forming fraud rings, using stolen identities and other patterns. Traditional approaches still focus on discrete data missing many opportunities to identify or prevent fraud.
Multi-model lets organizations see data from different perspectives, its context and detect fraud patterns with graph database technology even within large scale datasets. In this white paper we will show how to convert data from relational to multi-model graphs, how various fraud detection queries work in ArangoDB’s Query Language (AQL) and how this Fraud Detection can be done at scale.
Enterprise Knowledge Graphs (EKGs) have been on the rise and are incredibly valuable tools for harmonizing internal and external data relevant to an organization into a common semantic model to improve operational efficiency for the enterprise and competitive advantage for the business units. On the other hand, EKGs can be difficult to develop and sustain, suffer from scalability issues, and can be difficult for business units to consume.
This White Paper describes some of these challenges and how a flexible data representation of a multi-model graph can address them.
In this white paper, we explain the resilience concept of ArangoDB 3. When an application runs on multiple machines or cloud instances, the probability of a machine failure is no longer negligible. Thus, if you run distributed applications and you want to sleep well, these applications need to be fault tolerant or resilient.
We show that an ArangoDB cluster with 640 vCPUs can sustain a write load of 1.1M JSON documents per second which amounts to approximately 1GB of data per second, and that it takes a single short command and a few minutes to deploy such an 80 node cluster using the Mesosphere DCOS.
When it comes to choosing the right technology for a new project, it can often be challenging to define the exact right tools that will match set-up criteria from start to finish. In this white paper, we explain what a multi-model database is, including a use case based on aircraft fleet management.
Read this overview to learn more about the highest-rated Graph Database, including use cases and benefits of every edition of ArangoDB.
Lunch Break Sessions
Recent Webinars Recordings
Jörg Schad and Heiko Kernbach from ArangoDB introduce you to the upcoming Custom Pregel Feature in ArangoDB 3.8 and how you can change you Pregel algorithms on the fly, without restarting the database or writing any C++ code.
Ewout Prangsma (ArangoDB ArangoGraph) and Ayal Rosenberg (WeBill.net) discuss the impact of a managed multi-model graph database on development efficiency.