End-to-end data engineering solutions that accelerate time-to-value and reduce cost-of-quality.
We build a robust data & analytics foundation with a focus on accelerating AI adoption across the business value chain through our digital accelerators, frameworks, and solutions.
Challenges
The cloud is getting complex.
Enterprises are rapidly moving to the cloud, based on the promise of scalability and cost savings. But multi-cloud environments can be complex to integrate and ingest data from.
Data is stuck in legacy silos.
Most enterprises have their data in legacy data platforms, which require them to re-engineer the codebase quickly and effectively.
ML engineering is time-consuming.
Today, enterprises spend weeks in experimentation, feature selection, productizing ML models etc., delaying time-to-insights significantly. And spend 4x the prod costs in non-production activities, delaying AI adoption.
How we help you overcome data engineering challenges?
Enterprises need thoughtful data engineering to sustain AI & analytics at scale. Our digital accelerators are designed to accelerate the full life cycle of data management covering data ingestion, data quality, catalog, data provisioning with focus on improving time to value and self-service analytics for different user personas. In order to scale analytics and AI, you need Tredence to deliver flexible, easy to extend and scalable data foundation.
Cloud data engineering advisory and strategy
Our data engineers help you clarify your AI vision; build a robust strategy across cloud, tech and data governance pillars; choose the right technology; and modernize your data platforms.
- Cloud assessment
- Application and platform modernization
- Data lakes, cloud data warehouse and BI enablement
- Cloud governance and operating model
- Cloud enablement and managed services (Scaled DevOps and MLOps)
Platform modernization for accelerated AI adoption
We enable rapid AI adoption by modernizing your data platforms with our use case-driven accelerators.
- T-Ingestor – a cloud agnostic Metadata-driven driverless ingestion solution to ingest data from any source to AWS, Azure or GCP.
- Code reverse engineering accelerators to modernize legacy data platforms by re-engineering the legacy codebase.
- Snowflake accelerators to operationalize the platform quickly and control costs.
- EASEL platform — a development workbench to facilitate and standardize the development of data science models and analyses.
Operationalized ML engineering with MLWorks
Our MLWorks accelerator is built to manage thousands of models across petabytes of data with ease.
- End-to-end model management for CI/CD and monitoring.
- Customized feature store for re-usable features across models, accelerating deployment.
- MLOps dashboard for model visibility pipeline traceability, RCA and troubleshooting.
- Model and data drift analysis
- Explainable AI
- Centralized support
FEATURED CASE STUDY
DataOps for a large telecom enterprise
Tredence built a DataOps platform to streamline data provisioning to users and systems to help transform the organization into Insights Driven Organization. Key capabilities in the platform include automated data and compute provisioning, data assets quality and certification, data security, catalog and automated provisioning.
More Case Studies
Customer Science Platform
for a large Retailer
Tredence delivered and continue to support a large-scale data platform on GCP Cloud to drive customer analytics with data ingested in batch, real-time and near real-time.
Data Platform Modernization
for a Global Chemicals Company
Tredence advised the CDO’s office on reducing costs, enhancing data management capabilities through a 2 year roadmap to create a comprehensive data platform to drive AI adoption.
Insights Platform Modernization
for a Building Materials Company
We modernized the client’s insights platform, re-engineered the data and insights applications on AWS Platform and continue to support the platform.
Why Tredence?
We are one of the world’s fastest-growing and most awarded management data engineering consulting and technology companies.
Repeatable solutioning
Our data engineering practice has robust, repeatable and customizable templates, frameworks and runbooks — for asset classification, data governance, data quality, cost optimizations etc. — that accelerate AI adoption at scale.
End-to-end data engineering capabilities
From AI vision and cloud strategy to DevOps and managed services, our teams have experience delivering end-to-end solutions for the AI and analytics needs of global enterprises.
Outcome-driven capabilities
Unlike tech-driven organizations, Tredence’s data engineering practice is outcome-driven, focusing on robust data model foundations with data elements and entities that go beyond documented use cases to deliver business value.
Full-stack cloud competencies
Our cloud team comprises 175+ cloud engineers, infrastructure architects, and data and analytics architects — many of whom are premium certified in leading technologies such as Azure, GCP, Snowflake and Databricks.
Reduced time-to-insights
Tredence reduces time-to-value by 30% vs traditional consulting and technology service companies leveraging our proprietary suite of accelerators.
Last-mile adoption that drives results
Best-in-class 94% NPS customer satisfaction score driven by our laser focus on empowering decisions to help our clients win.