Petridisz
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Petridisz & Company
Data Into Insights, Insights to Action.
Unleashing the power of data and cloud for businesses - expert services in data engineering, cloud computing, data analytics, migration, and optimization.
Petridisz & Company
Our company was founded on the belief that data is a powerful tool that can drive growth and innovation in any business. We are passionate about technology and have a wealth of experience in the industry, which enables us to provide expert guidance and solutions to our clients.
We work closely with our clients to understand their unique business needs and challenges, and develop customized solutions that are tailored to their specific goals. Our expertise spans the entire data engineering and cloud computing stack, giving us a comprehensive understanding of the entire ecosystem.
More about usOur Services
Data Engineering
Designing and implementing data architectures, pipelines, and workflows to ensure that data is efficiently and effectively processed, stored, and retrieved.
Data Analytics
Developing and implementing data analysis and visualization techniques to enable businesses to extract insights and value from their data.
Cloud Migration
Assisting businesses with moving their data and applications to cloud-based infrastructure to achieve scalability, flexibility, and cost savings.
Cloud Architecture
Developing cloud strategies and architectures that align with business goals and enable effective data management and utilization.
Data Training
Our approach includes targeted training for each part of your team, so everyone is equipped with the skills and knowledge they need to succeed.
Case Studies
Case Studies
Conference Talks
Datafest Yerevan
MLOpsI discussed how to effectively use MLflow technology for managing machine learning experiments and models. I provided insights into the process of tracking and analyzing metrics of machine learning models using MLflow. Additionally, I demonstrated how the MLflow central model store can be used to manage the lifecycle of a model. I shared tips and best practices on how to effectively work with experiments and how to use the MLflow technology to improve the performance of machine learning models. Attendees of my talk gained a deeper understanding of how to leverage MLflow to optimize their machine learning workflows and achieve better results.
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DevTalks
Analytics EngineeringI explored how data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows. We talked about dbt, a tool that helps data teams work like software engineers—to ship trusted data faster. Hence we broke down the Data & Analytics Maturity Model into smaller pieces and rebuilt it with dbt. The purpose behind the top-down approach was not only to know the tech inside out but to have a deep understanding of how everything fits together. Afterward, we got our hands "dirty” and built out a dbt project, exactly how you would do it in the real world. Lastly, we discuss dbt best practices.
Contact UsIndustries We Serve
Automotive
The use of big data in the automotive industry has become increasingly important. Predictive maintenance, supply chain optimization, autonomous driving, quality control, customer insights, and predictive analytics are among the most popular applications of big data in this industry. These applications enable companies to analyze data from various sources to make more informed decisions, reduce risks, and optimize their operations. Big data also enables companies to collect data from connected vehicles and use it to improve vehicle performance, enhance the driving experience, and develop new services. Big data has revolutionized the way the automotive industry operates, enabling companies to be more efficient, innovative, and customer-centric.
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Telecommunications
Big data in the telecommunications industry can optimize network performance and improve customer experience. Companies can use customer churn prediction to reduce churn and retain customers. Real-time fraud detection can quickly identify and prevent fraudulent activity. Personalized marketing can create targeted campaigns that resonate with individual customers. Predictive maintenance can save time and money by minimizing the need for manual inspection and repair.
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Technology
Big data revolutionizes the technology industry by improving cybersecurity, developing better products, enhancing customer service, optimizing operations, and reducing costs. Companies can use data analytics to identify patterns and potential threats, inform product design, and offer personalized support. Predictive maintenance can prevent downtime and reduce costs by identifying potential hardware and software issues before they occur. Big data can also be used to optimize supply chain operations by analyzing data on supplier performance, logistics, and demand. Overall, big data has numerous applications in the technology industry that can help companies improve their operations, better serve their customers, and stay ahead of the competition.
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Financial Services
The financial services industry can benefit greatly from big data by improving fraud detection, customer service, risk management, compliance, and product development. Companies can use data analytics to identify fraudulent transactions and protect customers' assets. Big data can also provide insights into customer behavior and preferences, allowing companies to tailor their products and services to better meet their customers' needs. Additionally, analyzing data on market trends and economic indicators can help financial institutions make more informed investment decisions. Moreover, big data can help with risk management by identifying potential threats and reducing the likelihood of financial losses. Compliance can also be enhanced by using data analytics to monitor regulatory requirements and ensure that companies are meeting their obligations.
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Distribution & Logistics
By analyzing data on supply chain operations, companies can optimize their processes, reduce costs, and improve efficiency. By tracking inventory levels and delivery times, companies can predict demand and optimize their inventory management. Analyzing data on transportation routes, vehicle utilization, and fuel consumption can also help reduce transportation costs and improve delivery times. Furthermore, big data can be used to monitor and track shipments, enabling real-time updates on the location and status of goods. This helps to improve customer service by providing accurate delivery information and reducing the risk of delays or lost shipments. Finally, data analytics can also help companies identify potential risks and mitigate them before they become a problem. By using predictive analytics, companies can anticipate potential issues in the supply chain and take proactive measures to prevent disruptions.
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Manufacturing
By analyzing data from sensors, machines, and other sources, companies can identify inefficiencies and take corrective action to improve their manufacturing processes. Additionally, by monitoring quality control data, manufacturers can identify defects and take corrective action to improve product quality, ultimately reducing costs associated with returns and warranty claims. Big data can be used to optimize the supply chain, enabling manufacturers to more efficiently source raw materials, manage inventory levels, and reduce transportation costs. We can help manufacturers monitor and optimize energy usage, reducing costs and environmental impact. By analyzing data on energy consumption, companies can identify areas for improvement and implement energy-saving initiatives.
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Pharma
Predictive analytics can help in identifying new drug targets, optimize clinical trials and drug development. Supply chain optimization involves analyzing data on raw materials, supplier performance, and logistics to optimize the supply chain and reduce costs. Quality control analyzes data from production processes to identify defects and improve quality control. Customer insights involve analyzing data from clinical trials to better understand patient preferences, behavior, and satisfaction. Lastly, predictive maintenance can help in predicting equipment failures and minimize downtime.
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Explore Our Online Course
Independent bestseller dbt™ course on Udemy!
Learn to use the dbt™ platform professionally through the creation of an exhaustive, real-world, hands-on dbt – Airbnb project covering both Theory and Practice!
- Data Warehousing /
- Snowflake /
- Analytics Engineering /
- Data Build Tool (dbt)
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