Big Data

Designing and building tailored big data analytics solutions in the Cloud.

Deriving insights from your data

You may have been collecting data for years, or perhaps you’re just starting out on your data journey. Either way, if you don’t use your data as a basis for making decisions that enhance your success, you’re missing a trick. After all, data is only as valuable as the insights that can be extracted from it, whether they’re about consumer behaviour, market trends or any other aspect of your business.

CloudZone

The data-driven Journry

Using both managed services in the Cloud and leading open source tools like Spark, Presto and more, we build data lakes, warehouses and analytics platforms that enable real-time collection, processing and analysis of your data, in a secure, scalable and cost-effective way. These solutions in turn power analytical and ML workloads and BI tools. Becoming a data-driven business doesn’t happen overnight; onboarding you on the journey, we help you make the necessary shift in culture, technology and strategy, while working with you and your stakeholders to build an in-house team that is committed to the process.

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Wide range of expertises

Cloud Migration

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    Happy Users Around The World

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    Growth Of Each Client Yearly

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Featured Case Study #Big Data

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DevOps

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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’sIt is a long established fact that. orem Ipsum.

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Our Blogs

Is BigQuery Omni the next revolution in Data Warehousing?

IDC predicts that global dataspace will grow up to 175 zettabytes (10 to power 21) by 2025. Even my driving license is connected to the internet using RFID, and therefore continuously generates data.   According to a cloud adoption survey by Gartner, 81% of companies that are using public clouds are using more than one […]

November 23, 2020

XGBoost Distributed Training and Parallel Predictions with Apache Spark

Background In Boosting (ML ensemble method), algorithms implement a sequential process (as opposed to Bagging where it is parallelised) that generates weak learners and combine them to a strong learner (as in all Ensemble methods). In Boosting, at each iteration of this process, the model tries to correct the mistakes of the previous one in […]

July 15, 2020