Embrace the Power of Digital Transformation
A recent study conducted by Gartner found that poor data quality is a significant barrier to successful digital transformation in the energy sector, with data errors and inconsistencies costing a company an average of $15 million per year. In the rapidly evolving energy industry, addressing data challenges is paramount to unlock the full potential of digital transformation. It’s all about keeping up with the times and unleashing the industry’s true potential!
Continuous real-time data integration and processing have become indispensable for trading organizations to optimize trading strategies and stay ahead of the market. This is particularly evident in the utilization of specific data streams such as price data, plant outage information, weather data, and IoT device data where sensors are being deployed widely across the industry, providing a vast quantity of data. As a result, numerous companies in the energy industry are leveraging Apache Kafka, for managing mission-critical transactional workloads and facilitating robust big data analytics.
Understanding Challenges of Data Management in Endur
More often than not, trading organizations face these two major challenges:
ADDRESSING DATA INTEGRATION ISSUES
If you want to make Endur’s data easily available across all the storage and processing systems it interacts with, it is important to address the following two major integration challenges within Endur:
Event Data Firehose: In the energy industry, there is a growing demand for sub-second latency, massive-scale data processing, and underpinned by a robust data architecture. They seek to process and analyze event data in real-time, often dealing with petabyte-scale volumes. That’s where specialized data systems come into play. Specialized data systems, like OLAP, search, storage, batch processing, and graph analysis, help address diverse data processing needs.
Log-Structured Data Applications: Addressing the compatibility issues of Endur with log-structured applications can contribute to creating a seamless and robust data ecosystem also allowing you to leverage various technologies such as – Complex event processing (CEP), Enterprise Application Integration (EAI), Enterprise service bus (ESB) and
- Event Sourcing - Used for smaller, in-memory use cases such as real-time PnL calculations and market risk measures like VaR, PFE, and cVaR allowing to capture and process events in real-time.
- Change data capture (CDC) - Enables extracting data from Endur's database in a log-friendly format.
PROVIDING ACTIONABLE INSIGHTS IN REAL-TIME BASIS
In the ETRM landscape, the time gap between events occurring and decisions being influenced is often too long, ranging from minutes to days. This approach is often inadequate for today’s commodity markets with the rise of algorithmic trading, data-driven order booking, and data science-driven financial models. Computing derived data streams from your ETRM. In this case, Endur’s transactional, risk, and metadata data streams is essential for real-time decision-making.
Empower Endur’s Capabilities through Seamless Integration with Kafka
Over the past decade, Kafka has become the go-to data backbone for businesses undergoing transformation. It provides an ecosystem that prioritizes speed and enables the development of new applications, while also facilitating real-time and event-driven business operations.
Endur’s capabilities can be enhanced by leveraging Kafka’s event streaming platform to facilitate real-time data integration, processing, and analytics. Also, providing a robust foundation for mission-critical transactional workloads and big data analytics in the energy trading and risk management industry.
Ensure Real-time Data Processing and Scalability with Value Creed’s Expertise
| ✔ | Analyze your business requirements and data ecosystem to uncover opportunities where streaming applications can enhance operational efficiency, enable real-time decision-making, and drive business growth. Our comprehensive range of use cases is designed to address a wide spectrum of needs and goals specific to your organization. Here are just a few examples of the diverse use cases we cover: Risk Limits & Monitoring, Real-time positions, What-if scenarios, AI/Machine Learning use-cases etc. |  |
| ✔ | Set up the RDS (Risk Data Stream) service involves configuring various components and processes to ensure smooth data flow and efficient risk management. We can provide guidance and support in setting up and configuring the RDS service, enabling you to effectively manage and analyze risk data in a streamlined and efficient manner. |  |
| ✔ | Setup of risk data stream packages curated over simulation result scripts that leverages your simulation result scripts effectively, providing expertise in risk management and data processing. This will enable you to gain valuable insights, make informed risk assessments, and drive better decision-making within your organization. |  |
| ✔ | Develop robust and scalable microservices tailored to your specific needs, offering expertise in financial model engineering, risk management reporting, credit aggregation, payment netting, compliance monitoring, and more. |  |
Revolutionize Your Data Landscape with Value Creed
By partnering with Value Creed, you can leverage our experience, technical skills, and industry insights to enhance your data transformation capabilities with Kafka in Endur. Our guidance will enable you to achieve efficient and scalable data processing, leading to improved decision-making, risk management, and operational efficiency in the energy and commodities sector.
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