DataOps Services
Manage enterprise data operations with robust DataOps expertise
With Sigmoid’s DataOps services, organize your data efficiently by creating a DataOps pipeline and keep it business-ready throughout the entire data lifecycle. We ensure a continuous development, integration, testing, deployment and monitoring of enterprise data operations. Our DataOps expertise carries data automation, data governance and data democratization while providing data lifecycle management.
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
Data 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 DataOps 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.
