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Data platforms

Building hybrib, cloud and on-premise Big Data platforms

We process various data sources in batches and real-time. We build hybrid, cloud and on-premise platforms.

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Hybrid

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Cloud

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On-premise

Our data and analytics platforms enable

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the acquisition and storage from internal
or external data sources

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processing rapidly generated data

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maintaining a long-term history
of the used data

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unifying data storage into one world
(data lake)

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reducing the burden placed on
data warehouses

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speeding up prototyping and deployment
of new business solutions

Benefits of our data platforms

Have you ever wondered what data you could actually use? The data-driven company is just around the corner.

Enterprise systems, production lines and clients produce a huge amount of data. Just catch them. They can take the form of various logs, transactions, signal data, sensory data, texts, images, voices or videos. We can process all this data, bring it closer to end users in the form of visualization layers and, thanks to that, use it efficiently in business. 
 

10+

on-premise Big Data platforms built in the CR

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optimizing production and logistics

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innovating business processes

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innovating communication with clients

Case studies

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Manufacturing - a hybrid data platform for data-intensive tasks

We built a hybrid data platform in one of the largest Czech manufacturing companies. The company uses it for tasks that require large volumes of data such as data visualization, advanced analytics and artificial intelligence.

Fast data processing on on-premise part

In the on-premise part, the manufacturer benefits from its large capacity (in the order of hundreds of TB of data), data processing speed, variety of formats used and a rich set of tools for reporting, analytics and artificial intelligence.

Easy scaling and computing power in the cloud

On a cloud built on Microsoft Azure, users appreciate the unlimited capacity for stored data, easy scaling, always-on analytics tools, availability of GPU machines for compute-intensive operations, and more. Very often, they use a dedicated collaboration zone on the cloud to collaborate with external entities to resolve any data security issues.

Internet provider - a Big Data platform with a capacity of 3 petabytes, built on a green field in 3 months

We built a Big Data platform for storing operational network data for a major Czech internet provider. The goal was to deliver a solution that is not tied to a specific hardware manufacturer or server type, and was also an excellent basis for follow-up activities and development. A complete reporting layer can be effectively built on the stored data and the information can be made available and visualized to end users.

3 PB of initial platform capacity, 300 compute threads, 2.5 TB RAM

In 3 months, we built and released the Big Data solution, which enables scaling of computing power and easy expansion of disk capacity according to future customer needs. The platform's initial storage capacity is close to 1 PB, it handles 300 compute threads and has 2.5 TB of RAM. Real data flows are in the tens of billions a day.

Read more here.

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Banking - data in one place, we transfer 4 TB of data per day

At the bank, we have created a Big Data platform that provides business users with streamed and batch data from various banking applications. To keep the batch data on the platform up to date, we have developed an offloading tool, called Adoki, which ensures the replication of data from the data warehouse and other banking databases and systems.

We transmit 24/7, in both directions, optimizing the burden on banking systems

Adoki provides data transfers under the constant control of a monitoring system. We optimize transfers so that they put as little strain on source systems as possible and do not affect user comfort. Runs 24/7.

On a daily basis, Adoki transfers around 4 TB of data in both directions (to / from the Big Data platform). 

Interested in a solution tailored to your needs? Contact us today

Thank you

We will contact you as soon as possible.

Dagmar Bínová

Big Data & Data Science Team Lead

Tomáš Plánička

Big Data Solution Architect