Data Engineering
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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
Architecture ROT Trustworthiness
Roadblocks: Insights to Value
Lack of talent
Case studies
Models were developed for a top global technology solution and service provider using Ridge regression, halo impact analysis, and S-Curves. The GUI-based tool helped allocate budget across various activities optimally, which influenced an uplift of ~ $200 Million in opportunity value annually.
Related blogs
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The Impact of Serverless Computing on Big Data
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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
