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

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

Cloud-Based Data Lake to Enhance Analytics Operations

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.