itembase Connects Data Across eCommerce Platforms with DataStax EnterpriseNovember 5, 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 Ramo Karahasan, Co-founder and CTO at itembase.
DataStax: Hello Ramo, thanks for your time today. Could you please tell us a bit about itembase, what you offer and your role there?
Ramo: Hey, I’m the Co-Founder and CTO of itembase. itembase’s mission is to build the largest eCommerce network in the world. We’re offering a platform that allows different entities in the eCommerce market, including merchants, solution provider and users, to connect and control their data through the itembase platform. Our platform makes sure that the data from a merchant’s different eCommerce stores are standardized on the platform and exposed via the data connect API to other eCommerce entities. Imagine I have three shops on Magento, Bigcommerce, Shopify for instance and I want to use the same accounting software. itembase takes care of getting the data from all three platforms, standardize it and make it available to the single instance of the accounting software. The shop does not need to install the accounting software as a plugin/app on those different web stores.
DataStax: What exactly makes your personal inventory innovative, what differentiates you to other similar applications? You offer it directly to consumers, shops and as a solution, correct?
Ramo: The personal inventory is currently not our focus. There will be news in 2016.
DataStax: Did you use a different technology before you started using Cassandra?
Ramo: Not in a distributed manner. We’ve used “old-fashioned” technologies in a non-scalable manner.
DataStax: Why did you decide to use Cassandra? What kind of data is stored there?
Ramo: We’ve benchmarked a lot of typical systems in this space. We’ve chosen Cassandra for different reasons:
- High write throughput on commodity hardware, since we’re tracking quite some transactions from eCommerce shops per day and it allows us to manage infrastructure costs effectively;
- The simplicity of scaling horizontally, through unexpected loads;
- Automatic auto balancing of clusters;
- No SPOF.
DataStax: How would you sum up the benefits you’ve achieved with DataStax Enterprise (DSE)?
Ramo: We mainly use DSE Search for our applications that are driven by time series data and run automatically for our customers.
DataStax: What caused you to use DSE over open source Cassandra?
Ramo: The benefit of having DSE Search available for our automated solutions, like automated customer retention tools, mail statistics, etc. We’ve started testing out Spark, which will be probably used mid-term.
DataStax: Can you give us some details on your usage of Solr with DSE Search? How does this capability enhance itembase’s functionality for your services?
Ramo: Some of our products are running fully automated on certain time intervals. This automation requires operations on bigger datasets to guarantee high quality of the service. One important aspect is the fault tolerance of the Search cluster, so that the automated services can run hassle-free, without having human intervention. Furthermore, we’re using DSE Search as an API for the mailstats we’re providing to our customers. Mailstats are gathered and stored per product we’re providing in Cassandra and exposed via DSE Search to our views.
DataStax: Tell us about the future of your project, do you intend to leverage other parts of DSE to make it a reality?
Ramo: Yeah, we’re planning on running anonymized behavioural analysis of the information we’re having to gain more knowledge out of the information and support our network (and each individual) to reach more success in what they’re doing in eCommerce. Furthermore, another topic will be the provisioning of useful data to our micro service that we’ll create in 2016 for our B2C application, which is currently the online inventory for end consumers, the shopping email address. We’re thinking of using machine learning based tool to also have anomaly detection in data behaviour, to detect if the data we’re tracking is changing within time. This has more like a monitoring/intervention aspect.
DataStax: What advice would you give to other startups that are thinking about using Cassandra for the first time in their solutions?
Ramo: To start with Cassandra if you never worked with distributed databases before can be challenging in the beginning. When we started there was not really an “academy” to learn and read about DSE or Cassandra. Nowadays everything seems to be better documented and the community is growing and supporting well. Use the community!
SHARE THIS PAGE