We recently asked some mediation-specific questions to the followers of AppTrice and our personal contacts to understand how many people are aware of what a mediation platform is and how it can be used. I was surprised to see that the majority of respondents were very familiar with the topic. Many people shared issues and concerns related to the products they are currently using or have used in the past, and we really appreciate this information because it helps us build a better product!
However, we also found that some people are struggling to understand how it could be used beyond processing usage records for billing. I want to shed some light on this and share some common usage scenarios, as well as what to consider when looking for the right solution to meet your needs.
Well known fact – event mediation is like a translator for telecom data. It takes information from different systems (like mobile networks elements), organizes it, and ensures it’s in the right format for analysis, billing or revenue assurance. This helps telecom companies track usage accurately and improve their services. This is a constant process; while you’re reading this post, tens of millions of usage records will be processed by every major US carrier.
Another common use case is data transformation during data migration. Let’s assume you want to migrate data from Database A to Database B. A and B could represent SQL and NoSQL databases, or one could be an in-memory database with a sophisticated CLI – feel free to imagine any exotic set of source and destination.
During the data migration, you might want to transform some values, whether it’s simple normalization, cleanup, or even replacing values based on specific reference lookups. This may not seem too challenging when migrating a few thousand records, but what if you want to migrate 100 million or even 100 billion records? This is where event mediation comes into play.
Rest assured, the majority of modern mediation platforms can handle this task for you. But what’s the difference between mediation products from different vendors? The difference is significant, here are some important points to consider:
- Consider the product and licensing costs, along with the hardware footprint required to complete this task.
- Evaluate expected performance: how many records per second (RPS) you should anticipate. This will help you estimate how long it will take to migrate all the data.
- Assess the initial setup, configuration, and testing. How long will this take, and how complex will the process be?
- Consider the reliability of the process. Will you need to control it manually, or will everything be automated? It’s not ideal to have to restart a process manually every hour.
- Look for observability features with embedded smart analytics. You should have a clear view of ongoing progress and performance, which will help you detect potential issues through AI-powered anomaly detection or projections.
- Sometimes, you may need data balancing options if you’re using multiple instances for data migration. It would be inconvenient to have to balance this manually.
- Ensure you have an efficient transfer process if you’re migrating data between different data centers or across cloud instances globally. This process must be reliable enough to handle latency due to distance or other network imperfections.
Can you just create your own solution? The answer is: it depends on your situation. Sometimes, homemade scripts might work, or engaging a contractor with the proper skills could be the best fit for your needs. However, this is mostly relevant in situations where the task is straightforward and the number of records to migrate is not large. Otherwise, it’s like reinventing the wheel; it will require a lot of resources to build and make it usable.
Are there some other areas where mediation platforms could be used? Absolutely! Here some ideas:
- Finance and Banking: Managing transaction data, it could be a part of real-time fraud detection solution and compliance reporting.
- Healthcare: Integrating data from various healthcare systems, updating patient records, and pre-processing data for medical billing.
- E-commerce: Handling order processing and logging, transactional-based inventory management.
- IoT (Internet of Things): Collecting and processing data from sensors, managing device communication, and enabling real-time analytics.
- Logistics and Supply Chain: Tracking shipments and integrating data from multiple suppliers.
- Media and Entertainment: Processing streaming data, measuring user interactions for marketing.
- Energy and Utilities: Deliver energy consumption data, streamline interexchange between devices.
- Manufacturing: Collecting data from production lines, enabling quality control processes.
- Retail: Processing point-of-sale transactions, supporting customer loyalty programs.
- Insurance: Integrating data from various sources, customer data enrichment, integration with external data sources.