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Latest ebooks
NiFi Complete Guide
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#happy #book #bigdata
MLOps: Build Feature Stores Faster
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Our authors
Rafał Małanij
Business Development Executive
Rafał is tech enthusiast who was always amazed by data and what insight we can get out of it. He started his career in IT over 13 years as tech data warehousing guy, then moved to Project Management and Business Development. Right is involved in Data analytics and Cloud projects.

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Albert Lewandowski
Big Data DevOps Engineer
Big Data DevOps Engineer in the GetInData where he is responsible for automation, creating Infrastructure as Code, monitoring and maintaining Big Data platforms on-premise and in public clouds. He is also a member of the Internet of Things working group in the Ministry of Digital Affairs, Smart City consultant in Almine and editor in Antyweb.

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Krzysztof Zarzycki
CTO and Co-founder at GetInData
A talented engineer, software developer & researcher. More than 10 years of experience in big data, analytics, HPC, distributed systems, data warehouses, data science.

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Mateusz Pytel
Google Cloud Area Lead
Experienced in heavy load and high availability system design (batch and stream processing). Architecture designer, supervision and implementation of multiple data oriented projects involving Google Cloud Platform.

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Marek Wiewiórka
Chief Data Architect at GetInData
Big data architect with 15+ years of experience, DEV/MLOps practitioner with strong background in classic data warehousing solutions and database designs. Helping customers to build modern data analytics platforms by combining containerization with big data technologies.

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For 4 years GetInData has been building Machine Learning Platforms for dozens of clients such as PLAY - the biggest Polish telecom or ING Bank with over 14.1M primary customers from over 40 countries. However, we were building data lakes and other Big Data solutions long before that.
We have developed projects that have brought tangible benefits to our clients (from small startups to the biggest companies on the market) by accelerating the Machine Learning processes, e.g.:




For ING Bank we reduced data discovery time by 30%, transferred servers’ layer to the platform as xrdp containers in 5 months, meeting the regulations of over 40 different countries' markets. Download the customer story to get more insight: ING Customer Story.

For PLAY we delivered architectural guidance and navigated the project from the PoC phase to successful full scale deployment in production. As a result, PLAY is currently using a scalable, secure, extensible Data Platform that can easily be queried for analytical, business and marketing purposes in real time, with a reduced operational cost. Download the customer story to get more insight: PLAY Customer Story.

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