Challenges in such migration include for example networking, DNS, and persistent data. This talk provides insights into how Kube-Arango, the OSS operator for ArangoDB, supports live migration of distributed stateful applications without impact on users. But outages, (Kubernetes) upgrades, resource considerations, and cost optimizations require the underlying infrastructure to be very dynamic including migration between Kubernetes cluster, datacenter, or even cloud providers. Consider for example ArangoDB Oasis, a managed Cloud Database service with over 200 deployments (aka highly available database clusters) across three major cloud providers and many regions. Operating a distributed database on a single Kubernetes cluster is interesting, but how about transparently migrating it from one cluster to another–potentially between different cloud providers– without impacting user workloads? Kubernetes has become the de facto default deployment for ArangoDB, a distributed Graph database.
The talk describes these differences and ways to address them in a systematic way. They use different forms of inference, and they deal with imprecision in different ways. They use dissimilar graph structures and vocabularies. They have different key performance metrics. The knowledge graph and machine learning communities use different approaches to data transformation and problem solving. By analogy, the ideal of the knowledge graph is the perfect crystalline structure of a diamond representing everything that is known about a domain in a logical way, whereas machine learning values flexibility of models trained on a subset of the data that can stretch like rubber to accommodate data never encountered before. Knowledge graphs and graph machine learning seem like a perfect match, though in practice there are subtle differences between the two domains that can cause friction.
This talk describes a story of lessons learned in a journey that started with the objective of developing an application that required integration of a knowledge graph with multiple machine learning models, which rapidly encountered the hard reality of impedance mismatches between the technologies, and how these differences were addressed using semantic models. We will cover Graph Basics, Graph Analytics, and Graph Machine Learning with many hands-on experiences. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different ways.
More recently, Graph Machine Learning applied directly to graphs using graph algorithms, and machine learning has demonstrated significant advantages in solving the same problems as graph analytics and problems that are impractical to solve using graph analytics. Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. In this workshop, you will gain hands-on experience with the latest topics in Analytics and Machine Learning: Graph Powered Machine Learning. They say the team they’ve assembled at ArangoDB cut their teeth at the New York Stock Exchange, Euronext, German Postal Service, DHL, and several international banks.From graph analytics to graph neural networks: Making the most of your graph data. In 2004, they cofounded their first startup, database consulting company triAgens, where they worked on NoSQL solutions for 15 years. “With ArangoDB, developers no longer face the painful trade-off between choosing a single database model or maintaining multiple databases - they can have the best of both worlds.”ĪrangoDB is Weinberger’s and Celler’s second venture.
#ARANGODB 27.8M SERIES IRIS SOFTWARE#
“Application developers have been held hostage for years by traditional database vendors,” said Bow Capital and Tibco software founder Vivek Ranadivé. It adds that more than 500 organizations worldwide - including Airbus, Cisco, Egress, Barclays, SAP, Concur, and Thomson Reuters - actively use it in production. But ArangoDB says that this past year its framework notched more than 7 million downloads on GitHub and 7,000 “stargazers” (users who’ve bookmarked its repository). ArangoDB claims that in a cluster with 640 virtual CPUs, ArangoDB can sustain a write-load of up to 1.1 million JSON documents (about 1GB) per second.ĪrangoDB competes for market share with other open source graph databases, including (but certainly not limited to) Redis, InfiniteGraph, OrientDB, InfoGrid, Dex, HyperGraphDB, BigData, and OQGraph. It can be used as a transactional document store managed with ArangoDB’s query language, AQL, and searched with a built-in full-text engine with similarity ranking capabilities. ArangoDB’s eponymous, C++-based platform uniquely supports key/value, document, and graph data models in a single database, and it offers features like synchronous replication and automatic failover.
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