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Data Engineering
Get in touch with our Experts
Did you know that data engineering and preparation tasks consume 80% of the time spent across all analytics projects?
That’s right.
With the volume of data growing exponentially every minute, data engineering services are in high demand.
Our expert Data Engineering team at LatentView Analytics helps organizations monetize and maximize the value of their data by taking a curated approach. We build a strong foundation of data and generate insights from data mining. Our goals are to tackle critical issues that prevent businesses from exploiting opportunities to scale and transform themselves into data-savvy competitors.
The key tasks in data engineering include:
Consult on Analytics Assessment and Roadmap Strategy
Design Data Lakes and Pipelines for Machine Learning solutions
Migration to Modern Architectures including Cloud Ops from legacy systems
Why LatentView for Data Engineering?
Business-focused analytics
Business-focused approach to data engineering to align analytics and technology.
Scalable modern architectures
Workload-centric architectures to meet different needs of business stakeholders.
Global Talent
Proven experience in delivering analytics solutions to internet-scale companies using Hadoop and open source technologies, on-premise and on-cloud.
Key Challenges
Lack of Trustworthiness
Insights powered by a wide range of datasets
Enable pervasive analytics without sacrificing trustworthiness
Architecture ROT Trustworthiness
Business-focused views of data at many levels
Improve business agility even when saddled with legacy processes & infrastructure
Roadblocks: Insights to Value
Lack of expertise in pushing insights to ideation
Transition seamlessly from discovery to operationalization of insights
Lack of talent
Dearth of skilled personnel to tackle challenges.
Bring together teams with diverse analytics skillsets to turnaround other challenges.
Case studies
Data was migrated from legacy systems into a cloud-based data lake with extended periodic data refresh for the largest food distributor in the US. This reduced the cost of ownership and increased the throughput of cloud-hosted analytics solutions.
Related blogs
The Impact of Serverless Computing on Big Data
This blog will discuss the pros and cons of serverless architecture with a reference implementation detail.
Designing an ideal data ecosystem for at-scale analytics
This blog gives an ideal engineering stack to address data, infrastructure and governance related concerns.
Peer Learning
Knowledge shared is power multiplied! The process of sharing what we learn helps us reach out to data leaders who want to upgrade their operations and enthusiasts who seek to discover more.
We want to impart our expertise while creating healthy discussions on various online platforms like YouTube, LinkedIn, and Medium. We also aim to expand our horizons by conducting webinars and publishing blogs. Take a look at the impact that our knowledge sharing has created.
