Content Oriented Web
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About the Project
Content Analytics is a leading eСommerce optimization platform for brands. The company's system helps brands to appear first among millions of SKUs available online. E-Commerce brands can store, catalog, and syndicate content to optimize their business and increase sales.

Goals of the Project

● Cloud services cost reduction

● Routine processes automation

● Easily manageable cloud-based ecosystem construction

● Deployment time reduction and failure minimization

● Decrease time-to-market of the released features

● Higher website performance
The project had limited technical documentation. Thus, we couldn't conduct the full analysis of the system until we audited the infrastructure, deployment pipelines, and monitoring system. It was essential for seeing the big picture and building a step-by-step optimization plan.
Along the way to 40% Cloud costs cut, we have not only halved the price but also optimized the infrastructure and automated deployment processes. Below you can see the in-depth solution developed by our engineers.
Challenge
Solution
Cost Reduction
Expenses on AWS cloud services were an issue of immense importance for CA. As AWS offers multiple tools and services that Content Analytics didn't use, the overspending issue arose. The expense reduction process took us several steps to execute a fourfold price cut:

  1. Migration of all big servers from Amazon to Azure. This allowed a considerable cost reduction. Later, the client considered the need for additional cost-cutting.

  2. Server optimization. Our team conducted an in-depth analysis of the existing servers on Azure, which allowed us to optimize the processes. It helped cut the expenses by 40%.

  3. Migration of the servers from Azure to Oracle. Content Analytics got a special offer from Oracle Corporation for servers hosting with the initial configuration. Since the migration to Oracle, the price for cloud services has dropped fourfold.
Routine Processes Automation
To test a new feature CA team had to clone new production servers which became extremely time-consuming. Before migration to Kubernetes, it took CA about 20 minutes to create a new environment for a client.

OpsWorks Co. helped CA automate copying, creating, removing, and migration of servers. Now, creating a new production environment for a client requires pressing a single button in their CI/CD tool. Once automation has been executed, it takes 20 seconds maximum.

Content Analytics was already using Jenkins as a Continuous Integration tool for its infrastructure.
Cloud-based Ecosystem Construction
There are hundreds of servers to host Content Analytics infrastructure. Previously, the CA team used OpsWorks for the servers management and configuration. However, it turned out to be quite difficult and time-consuming. Because OpsWorks supports Chef recipes to handle installing, configuring packages and deploying apps, CA team considered it inefficient to write and execute them.

As our primary goal was to reduce costs, we decided to migrate the CA infrastructure to Kubernetes, which consequently simplified the management of the servers.
Deployment Optimization
As it was mentioned before, Content Analytics owns hundreds of servers. When a feature was tested, engineers deployed code on tens of servers simultaneously. If one of them failed, the deployment stopped, and they had to launch the process manually.

We used Jenkins to optimize and advance the infrastructure. With this technology, test environments are created and killed due to emerging needs.
Monitoring Flexibility
Initially, Content Analytics was using Nagios as a monitoring system for its infrastructure. Nagios approach had several disadvantages. It involved manual work for severs creation and removing. Even though it took only five minutes to execute either adding or deleting a server, it turned extremely time-consuming for CA infrastructure that has hundreds of servers.

Another issue concerns proactivity. The Nagios system notifies a user about an issue after it emerges and doesn't analyze the trends to predict glitches. The monitoring solution developed by our engineers was based on Prometheus and Zabbix integration. We use Prometheus for clusters monitoring and Zabbix as an auxiliary tool. Also, it involved ELK Stack implementation for logging
Project Results
During our collaboration, we have accomplished hundreds of tasks and consequently made significant changes to the CA infrastructure. The main improvements include:
Deployment time reduction from 30 to 5 minutes by virtue of optimizing and improving deployment scripts and processes.
Fourfold costs reduction due to databases migration from RDS to Azure.
No routine job owing to automated on the production and testing stages.
Shorter time-to-market and improved customer satisfaction due to automating and optimizing CI/CD Pipeline.
Technologies we use
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