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Current data engineering methods are struggling

91% report frequently receiving request for analytics with unrealistic or unreasonable expectations, and 87% say they are blamed when things go wrong
Data World

Data Engineering

A framework designed for increased data demands

The rapid adoption of data analytics and advanced analytics by Industry, both of which rely on big data is a challenge for data engineers around the world. Data architectures and frameworks were often not built to comply with these demands, and so data engineers spend large amounts of time re-engineering solutions, integrating multiple data systems and maintaining data quality.

VROC has designed an end-to-end suite of products to reduce the burden on data engineers, which includes data storage, management, visualisation and analytics, and can also be coupled with auto AI and remote operations control.

DataHUB4.0 is built for industrial big data, with its distributed data storage allowing for scalability, reliability, speed and data integrity. The automatic ingestion and pre-processing of out of order, linear, non-linear and time-series data reduces the time data engineers and data professionals spend wrangling data. Greatly improving collaboration and productivity.

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End-to-end Auto AI Pipeline

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Advanced Data Engineering

Automating data engineering processes for greater efficiencies

The advent of DataOps and MLOps which are designed for automation and ease of execution will greatly benefit data engineers and data administrators. With the hope that statistics like this one from the IDC improve, which states that 28% of AI/ML projects fail due to a lack of necessary expertise, production-ready data, and integrated development environments.

Applying methodologies such as DataOps and MLOps along with automated tools, will help to automate data orchestration and the delivery of analytics, AI and machine learning models to the point where companies can scale the use of advanced analytics to achieve rapid results.

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