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Stream
Data Platform

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SDP

Platform enables business to be agile - react fast and automatically

  • Enables near real-time Stream Data Processing and Artificial Intelligence
  • Extremely fast, resilient, designed for distributed environment
  • Adopts evolutionary event-centric approach for building data-intensive applications
Advanced Analytics

Automate Decisions by Artificial Intelligence

  • Build advanced Machine Learning models over fast data pipeline
  • Use automated decisions based on AI on data in-motion in real time
  • Visualize data in charts and dashboards
  • Pipeline integration with H2O.ai platform
Connectitivy & Interoperability

Collect and process in-motion data

  • Collect huge flow of in-motion data and process them continuously
  • Connect your sources continuously in and out from the pipeline
  • Preview events and schemas in the pipeline
  • Use existing ready-to-use connectors
  • Store data for offline processing into data lake

Platform Management

  • Cluster information and setup, services health check
  • Dynamic configuration, control platform through web application
  • Dive deep into platform performance, many detailed metrics

Virtualization

  • Infrastructure as a code
  • Modules and applications containerised
  • Cluster orchestration, Hybrid infrastructure possible
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Usecases

360 Degree
Customer view

Increase revenue by learning about existing customers/users and deliver superior modern customer experience.

Gather all users interactions from multiple channels that are locked in separate silos and access them in real-time. Modern Digital Businesses have invested in modern computing infrastructure to integrate all of their customer data in a single location for real-time access of users and applications. Event-Driven Approach is used to deliver superior customer experience, application leaders supporting modern customer experience must prepare for a major shift of focus - moving from data consolidation and process optimization to responding to events as they come up in real time. This is a key to sustain a competitive advantage and deliver superior customer experience. Responding to events as they occur also offers possibility to serve personalized context-aware offers and content.

Scoring and Anomaly
Prediction

Predictions based on online behaviour data could significantly improve provided online services and reduce operational cost.

Advanced Machine Learning models based on behavioural, device fingerprint and transactional data can predict customer scoring and reveal potential anomalies. That leads to early detections and saved resources. Conversions/approvals are more efficient and completed online in near real-time.

Operational
efficiency

Every online channel outage or poor customer experience is like a closed store for customers and leads to significant issue in revenue loss, customer churn or decrease of brand loyalty.

Revelation of potential issues with performance or stability in advance results in investigation, finding the root cause and finally fixing the solution causing the failure or further outage. Operational insights are generated in real-time based on aggregation of all operational data into one single platform. What is more, data is also enriched by detailed monitoring of running applications/systems. Also, alerting capabilities and operational insights help predict and prevent undesired outages.

IoT Pipelines

In year 2020, there will be over 26 billion connected devices across the globe, this comes with the opportunity to gain value from an enormous flow of data.

Platform is used as back-end infrastructure for many Internet-of-things (IoT) deployments, everything from sensors to complex IoT device like a car. Fast moving data requires unique techniques to keep up with the flow in real-time. Swarm intelligence – assembled by combining and analysing various sources of data (e.g. traffic/weather/charger stations) supports quick reactions to data in context. As a result Swarm Intelligence needs to be developed (to gain the possibility to “see over the corner” – assembled by combining and analysing various sources of data (e.g. traffic/weather/charger stations) supports quick reactions on data in context.

Real-time fraud/anomaly
Detection

Along with scaling digital businesses and on-going automation of its fraud detection processes, it can help organizations gain clearer view of hidden misuse or patterns (e.g. payment card fraud, anti-money laundering).

Online attacks are growing every year and financial loses are increasing with new techniques. Fraud detection systems must be resilient, operating 24/7 and flexible to change, they must report attacks and anomalies immediately. Stream Data Platform can offer single storage for all interactions and transactions and provide complex checks in near-real time.

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Technology

Technical scheme SDP
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What we do

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Our Team

We are still hiring into team! Do you want to build fast data evolutionary systems? Would like to work with the latest technologies and disrupt current approaches? We are looking for talented T-shaped people, experts in their field and have an overlap into other disciplines. Can you identify yourself as a developer, devops engineer, data engineer or data scientist? Join us.

Lukáš Matějka

Head of Development Centre

Lukas is involved in the area of fast data processing and its use. He has experience in software development with large-scale integration application systems, and he has participated in one of the first big-data application for web archiving in the Czech Republic.

Aleš Rybák

Senior Architect

Ales is a software architect who has been involved in many large enterprise implementations from back-end to front-end systems. Ales has a huge insight in many areas of software and IT infrastructures.

Jan Kučera

Team Leader

Honza is an experienced team leader and backend developer. He has dedicated his career to Java-based ecosystem (Java, Scala, Kotlin). He has been involved in design of many large-scale systems processing of big data, monitoring, and B2B applications.

Martin Bartoš

Data Scientist

Martin has participated in various projects as a data analyst and above all he has been focused on the implementation of several mobile games. He prefers data analysis in Python, R and SQL. Previously, he also devoted himself to digital online marketing and website design. His project work focuses on machine learning technologies and effective data visualization.

3M
T-Mobile
Zentiva
Metlife
BNB Paribas
ALD Automotive
Fresenius Kabi
Confluent
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Contact Us

Are you interested? Don't hesitate and leave us a message. Thank you.