DataOps

Create an agile and collaborative approach to building and managing the data and analytics infrastructure

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Seamlessly manage data across the end to end analytics lifecycle and improve productivity

The rapid adoption of technologies like 5G, AI and ML is expanding the global datasphere at breakneck speed. Building on the essential capabilities to convert this burgeoning data volume into business value can be a daunting task without a robust DataOps process. Sigmoid’s DataOps services help analytics leaders across organizations stay business-ready by seamlessly orchestrating voluminous data efficiently throughout the data lifecycle. The proven DataOps methodology ensures uninterrupted development, seamless integration, testing, deployment, and monitoring of enterprise data operations. With years of expertise in data automation, data governance and data infrastructure optimization, Sigmoid helps companies enhance data pipeline availability, reduce downtime, lower operations costs, and mitigate data risks.

Guidebook

Modernizing enterprise data operations with DataOps

Organizations face a host of challenges in streamlining data analytics and creating data pipelines. These may range from challenges due to proprietary choices, cloud, structural, or edge computing-related. The guidebook explains how dataops empowers businesses in the creation of processes that meet user needs throughout the life cycle of any data usage.

Download guidebook

Empower business teams faster throughout the life cycle of data usage

Data Quality

We ensure that your data quality is intact throughout the end-to-end enterprise data lifecycle.

Action Automation

We handle petabytes of real-time data and identify opportunities to eradicate manual intervention.

CI/CD for Data Pipelines

Our engineers integrate code where needed without refactoring, leading to productivity improvement.

Observability

We excel in data pipeline observability and perform in-depth RCA and CAPA.

24/7 Support

Our engineers are available round the clock to provide continuous support and maximum uptime.

Cross-industry Competence

We bring in best practices across the data analytics lifecycle from CPG, BFSI, Manufacturing, Hi-tech, etc.

Customer success stories

Our other offerings in data engineering

Data Pipelines

Automated data pipeline solutions reduce time to generate insights quickly for intelligent business decisions.

ML Engineering

Build new AI/ML solutions for rapid experimentation, live model performance, and effortless deployment of new predictive models as business needs demand.

Cloud Transformation

Modernize, migrate, and optimize cloud data performance with agility and reliability for optimal data usage.

Insights and perspectives

Blog
Need for effective Log management systems – Comparing Splunk & Elastic Search
Infographic
DevOps vs. DataOps
Guidebook
A guide to accelerating data governance with cataloging

FAQs

Early on in the data management process, DataOps can help enterprises identify which kind of data can be valuable so they don't spend time later sorting through it for quality. DataOps also helps teams communicate better with each other to find bugs and make analytics more efficient and accurate.

DevOps typically helps streamline and optimize the software development lifecycle, allowing for more and better releases. DataOps also helps improve quality and cycle time while utilizing new tools and approaches. DataOps uses DevOps to manage the critical challenges of an enterprise's data pipeline.

Yes, you can fit DataOps into your existing data ecosystem with a few changes. However, it is better for your environment to set up DataOps from Day 1 so you can ensure the heavy lifting around automation is taken care of from the beginning.

DataOps eliminates redundancies in the data fabric and ensures operational efficiency. In fact, DataOps gives enterprises the benefit of a smooth transition to the cloud that enables better digital transformation strategies.

Looking for DataOps consulting and implementation services.