Event kick-off + welcoming
From ingest to visualisation - an integrator's journey
Talk abstract: The recent pandemic has forced a change towards cloud computing and remote working. Customer experiences have changed as have the operational systems and procedures. In order to support these new models, data integrators have to find the best way to move, link and socialise data.Conventional data stores don’t work well in these environments. However, graph databases provide mechanisms to link and resolve data queries in a much more flexible and cost-effective way. Mike explains how Capventis uses Dgraph to help with integration - and how Dgraph has reduced development and data management costs.
From SQL to Dgraph, our journey with MissonBase
Talk abstract: MissionBase is a small non-profit startup. After growing to over 500 users and implementing advanced ACL we faced challenges and needed to change our tech stack. After struggling to build our own GraphQL implementation, we believed there had to be a better way. We found Dgraph and fell in love. This is our story.
Application practice of distributed graph database across the KE Holdings
Talk abstract: This talk answers three core questions: How do you choose the best graph database (one that is ideal for the unique production environment’s needs) from multiple options? How can a knowledge graph with ten billion nodes achieve a millisecond query? How are KE Holdings’s 48 billion ordered triple datasets stored in the database?Learn why KE Holdings needed a graph database, and then model your selection process on how they chose the right graph database for their business needs. Pan Gao then explains how to deploy the graph database, as well as principles, optimizations, and trade-offs.By the end of this talk, you’ll know if you need a graph database, how to choose your ideal solution, and what to do to get up and running with Dgraph.
How FactSet used Dgraph and DQL to build a Point-in-Time Model of Financial Objects
Talk abstract: The universe of financial objects includes Countries, Companies, Securities, Stock Exchanges, Indexes, Funds and more. These objects are related in many different ways - and many of these relationships change over time. With this universe loaded into Dgraph, our clients are able to easily explore these relationships as they exist today or at any time in the past.
Building a Centralised Knowledge Graph to Power Your Analytics
Talk abstract: A centralized knowledge graph is vital in today’s connected world. In this talk Alex will walk us through how Knights Analytics uses Dgraph to build a knowledge graph in the never ending battle against financial criminals.
Badger + Genomics
Talk abstract: Rapidly advancing genomic technologies are creating vast quantities of increasingly dense data sources. This data can add significant value across human, plant, and animal applications. However, efficient storage and management of that data can quickly become a burden. To help our clients manage and generate insights from their data, we built a genomic data store and analytics platform on top of BadgerDB - the same database that powers Dgraph.
From Zero to Hero with Dgraph’s Community
Talk abstract: In the past year, I have worked on a Github-like system that helps data scientists track and manage their research. Our data looks like a huge graph of branches and versions, so it looked like a graph-based database would be a perfect candidate.It was quite a journey. We first tried GQL and then Slash GraphQL in its early versions. At some point, we hit the limits of GQL and we started to move into DQL. We ran into trouble quite often, and I found myself asking a lot of questions. At the beginning, total newbie stuff, and eventually pointing out real problems, and asking for new features to help make the product better for everyone. I found myself being invited to conversations regarding the product and GQL. The core team would often post polls to decide how features should be implemented, and I got to post my say on it. When the time came and we switched to DQL, my knowledge of the DB’s inner workings made it surprisingly easy to switch, and again I joined the conversions, this time regarding DQL’s future.
How I made a Powerful Cache System Using Go and Ristretto
Talk abstract: I discovered Go language but didn’t have any time to follow multiples tutorials to learn it. But one day I discovered Træfik reverse-proxy project when I wanted to switch my infrastructure into fully dockerized one. I’m Træfik user since v1.4 but after many months using it I encountered an issue : there were no caching system in this reverse-proxy. I scrolled over the internet to know if any solution exists but nothing appears.Then I decided to write my own Træfik cache system, but the main question was “Which language?” - PHP ? Nah. - Nodejs ? What a joke ! - C++ ? I didn’t learn this language at school and it’s really insane to learn.Then I was on Træfik github repository when I decided to write it in Go. Another good point: that’s compatible with docker integration.So I started the project and called it Souin Let's see together how I bring it up from code to deployment.
Customer 360 in Health Analytics
Talk abstract: Hailing from a Fortune 100 company, Rachit, the senior platform architect shares with us how the company uses Dgraph to build a Customer 360 platform for the health analytics field. He will go through the criteria required to build a customer 360 system, and why the company chose Dgraph.
Analytics at Scale
Talk abstract: In this talk, I will go through the scalability and technical problems that we faced at my company, a Fortune 100 telecommunication company, whose solution required Dgraph. I will share the technical difficulties we faced, and what we did to overcome them.
How Kin uses Dgraph to Connect People
Talk abstract: Kin is a tool that helps startups find their markets. It is powered in part by Dgraph. This talk will walk through why we chose Dgraph as the tool backing our service.
How Dgraph helped in building a model of addresses
Talk abstract: In this session I will be talking about efficiently storing and querying a complex and unbounded data of addresses.