Vertica-as-a-service
Significantly reduced the database management time for their customers, increasing Vertica's footprint on the market
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Executive Summary
Vertica (an advanced unified analytical warehouse) needed
a fully managed data warehouse.
As a service for Vertica DB, via an easy-to-use, automated managerial platform.This would significantly reduce the database management time for their customers, increasing Vertica's footprint on the market.
Vertica Systems is an analytical database management software company based in Cambridge, Massachusetts, and the United States. It has produced a column-oriented Vertica Analytics Platform, designed to manage exabyte scalability, query-intensive data.
Business Challenge
Vertica needed a solution that makes the data warehouse so easy to access that users can just use the database and not worry about managing it. The Vertica Systems Operations team required automation which made infrastructure, security, monitoring manageable on fingertips. The following were expected outcomes.
An efficient management platform for the customer's cloud.
Ability to create, scale and deploy database clusters in minutes instead of days.
Automation for their clients to auto-scale databases saves tons of money.
Customer Solution
Crest Data helped Vertica Systems build a next-generation automation management and monitoring solution, from scratch. This solution runs on AWS cloud infrastructure in a customer’s AWS account, empowering customers to preserve all negotiated pricing while automating the setup and management of the Vertica environment. With this, the customer has the data and compute resources on their secure cloud and can maintain preferred pricing flexibility with AWS. This solution architecture comprises various technologies like Terraform, Jenkins, Docker, Python, React, and AWS.
The platform was exposed via a UI interface built in React.
Terraform was used to manage the entire infrastructure as a code.
Python was used in an orchestration layer to invoke Terraform scripts and backbone to the UI layer.
Jenkins Parameterized Pipelines were used for continuous integration and testing.
Docker was used for containerizing services.
The Crest Difference
The distinctive aspect of Crest Data lies in our ability to handle and uphold Twitter's Splunk and Observability infrastructure, alongside leveraging open-source tools like Airflow and automation tools like Puppet. Through this approach, we achieve large-scale automation by effectively managing the environment, preventing deviations, and streamlining the deployment of changes. Furthermore, we conduct thorough root cause analyses of incidThis next-generation automation system helped the Vertica Systems Operations team achieve the demand for easy, high, and swift yet efficient availability of their data warehouse to the customers without worrying much about underlying database management.
These technologies helped achieve a scalable environment, making infrastructure and provisioning easy to manage.
It keeps the data warehouse consistent and in the intended state.