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Diego Ferreira

myEd Powers Personalized and Intuitive School Education Platform with DataStax Enterprise

By Diego FerreiraDecember 2, 2015

This post is one in a series of quick-hit interviews with companies using Apache Cassandra™ and/or DataStax Enterprise (DSE) for key parts of their business.  For this interview, we talked with Stephen de Montfort, Technical Director at myEd.

DataStax: Hi Stephen, many thanks for the opportunity of this interview. Could you please tell us a bit about myEd, what exactly you offer and your role there?

Stephen: Sure, thank you. Co-created with schools across Australia, myEd is a teaching and learning platform that enables teachers to quickly and easily create using their existing resources, sequences of learning (we call them quests) which they can then share with individual students, groups, or their entire class.

Students go on quests to unlock the badge for the learning their teacher has shared with them, and their teacher receives real-time feedback on their students progress, so they can identify where they are and how best to help.Teachers can then provide marking and feedback all in the same place as well as begin individualizing to cater for specific students needs.

There’s lots more myEd does – the best way to find out more is to check us out at and sign up!

So far as my role – I am the technical director at MyEd. I manage everything from DevOps to front end Development for our team. This involves a very broad spectrum of roles as you can probably imagine, but my first passion is in DevOps.

DataStax: What makes your personalization engine successful, what differentiates you to other similar applications? What makes you excel at customizing the user’s experience and unleashing the potential of teachers?

Stephen: We’ve spent the last 2 years living in schools co-creating myEd with passionate teachers and students across Australia – what this has enabled us to do is deeply understanding a teacher’s workflow in the classroom with their students.
While there are lots of great tools and products available to share learning with students, mark students work or provide students with feedback, what we found from working with teachers is that these tools often don’t align to their workflow. As a result the tools might be great in a particular use case, but don’t enable a teacher to be able to complete what they need in a single place and save them time.

Additionally many tools for teachers are complex. Our approach from a user experience perspective has been to take our experience working and building consumer applications and apply this to education to create a user experience that is seamless, intuitive and easy to use.

In terms of our personalization engine – this is a work in progress! It’s actually why we moved to Cassandra – we needed better insight into our user data so that we could offer more meaningful insights within the platform. The individualization that we enable is also driven by this data, allowing teachers to gain insight into what help their individual students need.

DataStax: Did you use a different technology before you started using Cassandra?

Stephen: Prior to Cassandra we weren’t using a different technology as we hadn’t yet sought to fulfil the business use case that Cassandra does. The business use case being an analytics service – we were trying to solve this case with several different products that would gather data in a siloed way, ultimately pushing us towards the fact that we needed to develop an in-house solution.

DataStax: Why did you decide to use Cassandra? What kind of data is stored there?

Stephen: We decided to use Cassandra because of its unique row structures and read and write efficiencies. We are storing time-series analytics data in Cassandra with automatic indexing in Solr. We keep all sorts of metrics from our platform to help us track the use of system features and user interactions, and Solr makes it easy for us to run ad-hoc queries easily to discover useful data about our users.

DataStax: How would you sum up the benefits you’ve achieved with DataStax Enterprise (DSE)?

Stephen: The benefits we’ve achieved, are that the integration of DSE has mostly been silent – meaning we haven’t had to worry much about what it’s doing, which has allowed us to focus on the use case. Because of the way DSE easily integrates Cassandra and Solr, we were able to quickly create an internal dashboard for ourselves and easily expose lots of data about our users, immediately proving the value of integrating the DSE product.

DataStax: What caused you to use DSE over open source Cassandra?

Stephen: DSE made it easy for us to setup and integrate with our existing infrastructure on AWS. As a startup, quick deployment time with new technologies is critical as it means we spend less time worrying about deployment and more time focussing on our customers. When we learned about Cassandra as a technology, we were thrilled to see its use cases. We were even more thrilled to be able to quickly deploy DSE’s AMI on an EC2 instance and be up and running in minutes. Some of the DSE features (Hadoop and Spark integration) have also made us rest easy about the future of development, because the tools we’ll need as our data grows are already integrated and accessible.

DataStax: What features from the DataStax Enterprise (DSE) stack are you using at the moment? What business use case do they fulfil?

Stephen: We are currently using Cassandra and integrated Solr. These two features working together solve our need for the storage and ad-hoc querying of analytics data. We also use OpsCenter heavily, as it makes it very easy to keep an eye on DSE nodes and their health. The specific business use case that Cassandra + Solr have solved for us very well is the ability to run ad-hoc queries on time-series analytics data. DSE’s automatic Solr indexing has made it easy to dump our analytics data into Cassandra and query it whenever we need to, giving us better insight into the way our users interact with our platform, and also allowing us to become more data-driven in our decisions.

DataStax: Tell us about the future of your project, do you intend to leverage other parts of DSE to make it a reality?

Stephen: We intend to grow our analytics service to be able to calculate aggregates and specific queries on time-series data ranging entire years moving forward. A year’s worth of user data can be a lot, so we plan on using Hadoop/Spark to be able to query/map-reduce huge data sets and form aggregates and cached results for quick lookups on predefined queries.

DataStax: What advice would you give to other users using Cassandra for the first time in their solutions?

Stephen: I would advise that beginners get their hands dirty with Cassandra as soon as they have the chance to. If you’re thinking about using Cassandra, you absolutely must have a play with the DSE product, it can take as little as 5 minutes to setup the DataStax AMI on an EC2 instance. For any fears you might have about migrating legacy data sets, we found it very easy to migrate an existing MySQL schema into Cassandra. Something that we as startups need to be able to do is to innovate quickly, and Cassandra has enabled this for us, and can for you too.



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