Lunch Sessions - ArangoDB

Lunch Sessions

ArangoDB Graph & Beyond Lunch Break

In order to keep in better touch with our community, we invite you to join us for our ‘Graph & Beyond’ lunch break series. 

Every two weeks on Wednesday, we present you with a new 10-15min talk to learn more about ArangoDB – different use cases, new features we’re working on – delivered directly to your inbox. Grab some lunch, settle in your favorite chair, and watch a talk filled with practical examples for ArangoDB.

Recommendation Demo on Oasis

#2.7 – Recommendation Demo on Oasis

For this lunch session, we recommend leaving your head in the clouds as we go through our ArangoDB Oasis recommendation engine demo. Our ML developer relations engineer Chris Woodward will introduce you to ArangoFlix, a movie streaming site that shows an example of combining machine learning with a graph database. We show how to offer the most chill-worthy movies for your next watching session.

ArangoDB- DGL Adapter

#2.8 – Introducing the ArangoDB-DGL Adapter 

This lunch session introduces you to our DGL adapter. We walk through the base examples of how to use the adapter to export ArangoDB graphs to DGL. The Deep Graph Library (DGL) is an easy-to-use, high-performance, and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logic can be implemented in any major frameworks, such as PyTorch, Apache MXNet, or TensorFlow.

Introducing the ArangoDB-NetworkX Adapter

#2.9 – Introducing the ArangoDB-NetworkX Adapter

This lunch session will walk you through using our NetworkX adapter and how to easily convert ArangoDB graphs to NetworkX graphs and back again! See how using this adapter gives you the best of both graph worlds with all of the speed and flexibility of ArangoDB and the ubiquity of NetworkX.

Previous Graph & Beyond Lunch Sessions

View our recorded videos of the Graph & Beyond Lunch Sessions.

graph embeddings with AQL

#2.6 – Graph Embeddings with AQL

In this session, Sachin will put more light on this rapidly growing area and its industrial and research applications. In addition to this, he will also demonstrate how we can leverage graph embeddings with ArangoDB’s AQL query language. The key idea of this session would be to generate Amazon Product Recommendations with the help of ArangoDB’s  AQL query language.We are going to follow this github repository for our session.

Community vs. Enterprise

#2.5 – Community vs. Enterprise

Curious about how to improve performance, business continuity, and security across your No-SQL database? Turning your project into a full-blown production application? Join us during the upcoming Lunch and Learn on (DATA) to understand more about ArangoDB about ArangoDB’s unique capabilities with our Enterprise Edition capabilities.

graph vs relational

#2.4 – Graph vs. Relational 

Join us for our next Lunch and Learn session: Graph vs. Relational. In this upcoming recording, our Account Executive Enzo Zenuni will explore the differences between relational databases and graph databases. This recording will dive into what are they, what do they do well, and when should you use one over the other.

recommendation engine

#2.3 – Recommendation Engine

In this Lunch & Learn, we give an overview of the various approaches for recommending products/services as well as demo how these approaches can be implemented using both Search and Graph in ArangoDB.

how to contribute

#2.2 – How to Contribute

In this lunch and learn session Chris walks you through all the ways that you can contribute and the steps involved. Join Chris Woodward on March 9th, 2022 for this Lunch and Learn recording.

hybrid smartgraphs

#2.1 – Hybrid SmartGraphs

In this Lunch and Learn Session Chris will introduce you to Hybrid SmartGraphs! Join our Developer Relations Engineer Chris Woodward on February 23rd, 2022 for the first Lunch and Learn of 2022.

advanced aggregation queries with AQL

#23 – Advanced Aggregation Queries with AQL

Building on the previous lunch session, we will explore more variants of the COLLECT operation. Instead of grouping full documents, you can use a subset of attributes. Using multiple COLLECT operations is another interesting use case. We will also delve into COLLECT with AGGREGATE as an efficient way to compute statistical properties during grouping.

basic aggregation queries with AQL

#22 – Basic Aggregation Queries with AQL

The COLLECT operation in ArangoDB’s query language AQL is a versatile tool. It lets you group records based on attribute values but also deduplicate values, count how often each value occurs, and more. In this lunch session, we will take a look at the essential variants of COLLECT.

Graph Embeddings Lunch and Learn

#21 – Graph Embeddings

Join Sachin Sharma as he puts more light on Graph Embeddings, a rapidly growing area and its industrial applications.

Movie Search Lunch and Learn

#20 – Movie Search Demo

Sit down and get ready for Jackson Reimers to take you through our Movie Search Demo, available on ArangoDB Oasis.

Hot Backups and Restores in ArangoDB

#19 – Hot Backups and Restores in ArangoDB

Sit down with Kaveh Vahedipour as he walks you through the range of possibilities and highlights pros and cons. Most of the demonstration is done on a live ArangoDB cluster.

#18 – RecallGraph

The talk will examine the importance of data-versioning, some related concepts, and the specific capabilities of RecallGraph itself. Finally, a quick demo with an application that leverages Recallgraph’s time-traveling feature.

introduction to foxx microservices

#17 – Introduction to Foxx Microservices

Sit down with Chris Woodward as he shows you the ingredients that make Foxx work so well and then see how to cook up your first Foxx microservice in this ArangoDB Lunch Session.

ArangoDB for Beginners

#16 – ArangoDB for Beginners

In this lunch session, join Digital Marketing Manager Laura Cope who will give a basic introduction to ArangoDB and the different services ArangoDB has to offer through a non-technical description.

aggregating time-series data with AQL

#15.5 – Aggregating Time-Series Data with AQL

In this special 3.8 release lunch break session, we take a look at the two syntax variants of the WINDOW operation and go over a few examples queries with visual explanations.

#15 – Entity Resolution

In this lunch session, we show why a graph database is well-suited for Entity Resolution together with a demo in ArangoDB.

monitoring arangodb

#14 – Monitoring ArangoDB

In this lunch session, learn how to set up and read monitoring on ArangoDB instances, and take a walk through the most significant metrics and discuss alerting based on them.

kubernetes meets graphs

#13 – Kubernetes Meets Graphs

Join us in this Lunch Session to learn about how Kube-Arango makes deploying and managing ArangoDB on Kubernetes a breeze.

knowledge graphs

#12 – Knowledge Graphs

This lunch session is an introductory video and focuses on the general concepts of knowledge graphs. This video will answer the questions of what is a graph database, what is a knowledge graph, and how do you build a knowledge graph.

Lunch Break Fuzzy Search

#11 – Fuzzy Search

Fuzzy search is an umbrella term for approximate matching in text retrieval. A common application is to compensate for typos in search phrases. In this Lunch Session, we will take a look at different similarity measures and show how fuzzy search works in ArangoDB.

#10 – Oasisctl: Providing Full Control of your Oasis Cluster

In this Learn Break session, Site Reliability Engineer Marcin Swiderski will give an overview of Oasisctl, a tool created by the ArangoDB Oasis team in order to help automate common Oasis tasks.

#9 – ArangoML

In this Graph & Beyond Lunch Session Jörg Schad will give an overview of different parts of the ML pipeline and how ArangoDB fits in. In particular, we will be talking about feature engineering, Graph ML, Embeddings, MLOps, and Metadata.

#8 – Introduction to ArangoBnB

In this Lunch Break, Developer Relations Engineer Chris Woodward provides a peek at the ArangoBnB project, a Fullstack JavaScript web app that is being developed to showcase the upcoming ArangoSearch GeoJSON features.

#7 – Getting Started with ArangoDB Oasis

Looking to get a glimpse of ArangoDB Oasis, the managed service for ArangoDB? In this Lunch Break, Senior Software Developer Robert Stam will introduce the Oasis platform.

#6 – AQL Query Performance Optimization 101 (Part II)

This is Part 2 of the Lunch Break sessions covering the basics of AQL query performance optimization. This Lunch Break session covers AQL query performance optimizations for cluster setups. It is a follow-up session to the AQL query performance optimization session from 2 weeks earlier.

#5 – AQL Query Performance Optimization 101 (Part I)

This is Part 1 of two Lunch Break sessions covering the basics of AQL query performance optimization. Jan Steemann demonstrates if and how index setup can help AQL query performance.

#4 – Graph Analytics with ArangoDB

In this Graph & Beyond Lunch Session you learn about Graph Analytics with ArangoDB. We will take a short look at different graph algorithms such as Community Detection, Centrality Measures, and Recommendation, but also discuss challenges of scaling such analytics to enterprise use cases.

#3 – AQL for eCommerce Analytics

During this Lunch Session we will showcase various AQL queries to analyze an eCommerce dataset for common questions in retail, like:

  • What products are offered?
  • Where are they located in my store?
  • Which are the best selling products?
  • What can the data tell me about shopper purchasing behaviors?

#2 – ArangoSearch: Where Full-Text Search and Graphs Meet

In this Graph and Beyond Lunch session we will cover ArangoSearch, the full-text search engine natively integrated into ArangoDB. Chris Woodward will give an overview of how you can combine a wide range of search queries (e.g., phrase, proximity, or range), ranking (e.g., BM25 or TFIDF algorithms) and ArangoDB’s other data models (e.g., graph or document).

#1 – Fraud Detection with ArangoDB

We are going to use the multi-model graph capabilities within ArangoDB to identify fraud patterns (as used for money laundering) in financial datasets and catch some bad guys! Jackson Reimers will show various fraud patterns and how to detect them, using the ArangoDB graph visualizer.

Learn More About Graph Databases

Read our latest Graph and Beyond  white paper to gain insights into how ArangoDB graph databases can support many use cases.
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