With digital transformation becoming a business norm, more and more organizations, irrespective of their size, are opting to take advantage of emerging technologies. Amidst the rise of assistive technological models such as machine learning, automation, and data integration; a lot have really come through for businesses. Delivering an increase in yield profits as well as customer satisfaction has been the top priority of this shift in paradigm, but aside from its stellar results, it has streamlined operations and made them more efficient. With all these promising new frontiers being looked into like Energy Trading and Data Integration, one does question what the future of technology holds with these trends in mind.


Energy Trading, Effective Energy Ecosystem and Blockchain Technology


The energy industry functions on a very simple dynamic; demand and supply. However, maintaining the balance between supply and demand sometimes becomes challenging. A reliable and consistent supply of energy is crucial to the current socio-economic systems as well as to sustain the modern line. With the demands for energy hiking up, companies and businesses alike have started to look into energy technology platforms.
The fact that energy trading applies to all kinds of energy such as electricity, gas, oil, CO2 and even coal. You can trade actual physical units of energy production as well as harnessed or produced energy using the basis of commerce. What is interesting about energy trading is that it is transparent. Energy being a key resource, a highly detailed and reliable information density needs to be maintained. Disruptive technologies like Blockchain are being looked into for energy traded and this is where the future of energy trading is headed as well. Trends within the energy trading industry are evolving with the increase in quality and reliability of data analytics, storage, and communication. With an increase in transparency and reliability, more parties are becoming interesting in energy trading and this is where a model like Blockchain comes in. By moving energy trading to a distributed, digital data ledger you can encourage buyers and sellers to engage in the business. With each transaction being visible and fair, the end user will feel empowered and keen to process business transactions within the circuit. This will also target more prosumers and lead to a far better controlled and secure trading system. With blockchain being incorporated into energy trading, energy transmission and distribution will become more advanced and streamlined as well. A more sustainable and end-user friendly energy ecosystem will lead to more commerce and development. Talent situated in remote areas with limited resources could be scouted and empowered making it all come full circle. Now seems like as good as a time as any to get into energy trading and help your business.


Data Integration Shaping Technology – ETL UDM and Machine Learning


In terms of data, ETL means to extract, transform and load. This concept is hugely applied in data gathering and warehousing. ETL might sound like the traditional approach to data integration and sure enough, it really is so. ETL has remained the same for quite a long time until recently with the emergence of new technologies like cloud-native tools and platforms it’s about to go obsolete. Data integration is being simplified as a whole, and new data architectures are being introduced to help scout and store useful data. Energy data especially is a real challenge. With the abundance of so much data available, you can’t be bothered to identify and download all of it. With approaches like ETL, it becomes excessively tedious unless you’re employing a trusted service for the job. Technologies like Unified Data Management (UDM) that take the best of data warehouses, data lakes and streaming and then combine them together minus the overhead of ETL.
What UDM accomplishes is:

supply. However, maintaining the balance between supply and demand sometimes becomes challenging. A reliable and consistent supply of energy is crucial to the current socio-economic systems as well as to sustain the modern line. With the demands for energy hiking up, companies and businesses alike have started to look into energy technology platforms.
The fact that energy trading applies to all kinds of energy such as electricity, gas, oil, CO2 and even coal. You can trade actual physical units of energy production as well as harnessed or produced energy using the basis of commerce. What is interesting about energy trading is that it is transparent. Energy being a key resource, a highly detailed and reliable information density needs to be maintained. Disruptive technologies like Blockchain are being looked into for energy traded and this is where the future of energy trading is headed as well. Trends within the energy trading industry are evolving with the increase in quality and reliability of data analytics, storage, and communication. With an increase in transparency and reliability, more parties are becoming interesting in energy trading and this is where a model like Blockchain comes in. By moving energy trading to a distributed, digital data ledger you can encourage buyers and sellers to engage in the business. With each transaction being visible and fair, the end user will feel empowered and keen to process business transactions within the circuit. This will also target more prosumers and lead to a far better controlled and secure trading system. With blockchain being incorporated into energy trading, energy transmission and distribution will become more advanced and streamlined as well. A more sustainable and end-user friendly energy ecosystem will lead to more commerce and development. Talent situated in remote areas with limited resources could be scouted and empowered making it all come full circle. Now seems like as good as a time as any to get into energy trading and help your business.
Data Integration Shaping Technology – ETL UDM and Machine Learning
In terms of data, ETL means to extract, transform and load. This concept is hugely applied in data gathering and warehousing. ETL might sound like the traditional approach to data integration and sure enough, it really is so. ETL has remained the same for quite a long time until recently with the emergence of new technologies like cloud-native tools and platforms it’s about to go obsolete. Data integration is being simplified as a whole, and new data architectures are being introduced to help scout and store useful data. Energy data especially is a real challenge. With the abundance of so much data available, you can’t be bothered to identify and download all of it. With approaches like ETL, it becomes excessively tedious unless you’re employing a trusted service for the job. Technologies like Unified Data Management (UDM) that take the best of data warehouses, data lakes and streaming and then combine them together minus the overhead of ETL.
What UDM accomplishes is:

  • Reliability and performance of a data warehouse
  • Low-latency and real-time data integration of a streaming system
  • Cost efficiency and scalability of a data late

All this within a single backend and multiple storage systems that smartly avoid data duplication and data consistency issues. With data integration and warehousing simplified, you can employ any service to get you the best data integration service. Machine Learning and Artificial Intelligence are doing wonders for technology-based solutions. Incorporating machine learning to suggest datasets, transforms and rules for a data integration project optimizes it to a far greater degree. Using AI, these suggestions get better with the passage of time meaning your system will learn to adapt and grow from the experience. Machine learning techniques are also being applied to automate entity-relationship modeling. Therefore, data integration is shaping the future of technology by providing solutions to handle and harvest data better. Data being crucial to businesses and development can now be harnessed to an optimal standard. With the decline in ETL, technology is abandoning conventional approaches to data making the most out of it for all kinds of reasons.