Data DevOps
Managing data infrastructure through a robust DevOps expertise that ensures enterprises run on the latest technologies aligned with the business roadmap
Our 7C’s Approach
Process
Standardise
- Monitor/analyze system metrics
- Data lifecycle management with 24/7 support
- Setup alert notifications and standard scripts
Improve
- Implement new tools
- Implement automation
- Tweak performance Issues
Innovate
- Build solution based on business needs
- Automate at scale and speed up deployment
- Enforce test and quality
Process
Standardize
Monitor/analyze system metrics
Data lifecycle management with 24/7 support
Setup alert notifications and standard scripts
Improve
Implement new tools
Implement automation
Tweak performance Issues
Innovate
Build solution based on business needs
Automate at scale and speed up deployment
Enforce test and quality
Our DataOps Expertise
Action Automation
Handle Petabytes of real time data and identify/ eradicate manual intervention
Data Governance
Prompt notification and escalation of anomalies are reported by engineers through
Communication
Automate and integrate; Escalate in multiple channels: Slack/Hangout/Email/PagerDuty calls
Democratize Data
Integrate code without refactoring, resulting in productivity improvements
Monitoring
Monitor and perform ETL pipelines with in-depth RCA and CAPA
DMAIC Process
Perform process improvements through Definition/ Measure/ Analyse/ Improve/ Control
24/7 Support
Get continuous support and maximum up-time from our dedicated pool of engineers
Cross Industry Competence
Delivering productivity improvements across Retail, CPG, BFSI, Hi-tech, etc
100% Tech Stack Coverage
We cover technologies across the data spectrum to cater to current as well as future needs
Tech Stack
Recommended Reads
Containerization of Py-spark using Kubernetes
Spark is a general-purpose distributed data processing engine designed for fast computation. The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application. It supports workloads such as batch applications, iterative algorithms, interactive queries and streaming.
Need for effective Log management systems – Comparing Splunk & Elastic Search
Continuous integration and Continuous Deployment have increasingly shortened the time taken to build applications that need frequent changes, while still maintaining a reliable delivery process.
