What do you do when your data starts to “split up”?

Imagine this scenario: Several microservices in your team should be like a well-trained band, each performing its own duties and coexisting in harmony. But I don't know when they started to separate. The order service only talks about orders, the user service guards user data, and the logistics side is in a league of its own. Just asking them to "reconcile accounts" takes a long time every day, let alone trying to quickly see the whole business. Doesn’t this feel a bit like driving while staring at a dozen different dashboards at the same time?

This is not a team-specific problem. The finer the microservices are broken down, the easier it is for data to be scattered into islands. Some people choose to synchronize manually on a regular basis, only to be woken up by alarms in the early morning; others try to stuff all data back into one database, which makes the system cumbersome and slow.

So the question arises: How can these "separated" data become a family again without affecting their independent lives?

A different way of thinking: turning the warehouse into a living room

The traditional data warehouse is like an archive room. The data is neatly archived as soon as it goes in, but it cannot flow. In the microservice architecture, what we need is a "public living room" - each service still lives in its own room, but the information that needs to be shared can be brought to the living room at any time for everyone to see and use.

This sounds simple, but there are a few hurdles in doing it. How to collect data. Each service may use a different database, and changes may occur at different times. picturekpowerWhen dealing with such projects, methods such as change data capture (CDC) are often used. It is a bit like installing a sensor on each data assembly line. Once a new "product" comes off the production line, it will be automatically marked, packaged, and sent to the living room. This is much more efficient and natural than knocking on the door at regular intervals and asking, “Do you have new data?”

Next is how to save. The layout of the living room is very important - it should neither be cluttered nor restrict everyone to sitting upright. A common approach is hierarchical design: after the original data comes in, it is first placed in the "buffer" and remains as is; then after some cleaning and conversion, it forms a "middle layer" with a clear theme and easy to find; it is combined into an "application layer" according to specific analysis or reporting requirements. It's like putting ingredients into the storage room, processing them into semi-finished products, and then cooking them into different dishes according to the recipe.

How to use it. A good data living room should have multiple doors. Some lead to BI tools, allowing reports to run on their own; some support ad hoc queries to answer emergencies at any time; more importantly, they can also reverse the data flow to the required microservices through APIs, allowing the data to truly circulate, not just the end point.

Why is this worth a try?

The benefits of doing this go far beyond making reports come out faster. It takes a load off the team. Development does not need to be interrupted by temporary data access requirements, and operation and maintenance does not need to worry about one batch task bringing down the entire database.

What's more important is speed. When the business asks, “How did that promotion perform last week?” you no longer have to piece together data from several systems; the answer is probably already there. This real-time nature is competitiveness when quick decisions are needed.

There’s another hidden benefit: resilience. The data warehouse is independent of the business database, which is equivalent to an additional stable data backup perspective. Even if a microservice fails temporarily, your analysis of the overall business situation will not be interrupted instantly.

From idea to reality, what are the key steps?

If you think this road is worth exploring, you can consider these directions when starting:

Where to start? Don’t expect to become fat in one bite. Choose a business domain with high data value and the most obvious pain points as a pilot, such as order or user analysis. Validate the entire process with a small, concrete project.

What to look for when selecting technology? There are many tools, but the core is to match your own rhythm. It depends on whether it is easy to integrate with your existing database and message queue, whether the community is active, and whether the learning cost can be afforded by the team. likekpowerWhen assisting customers, "whether it is easy to use" and "whether it is sustainable in the long term" are often evaluated with equal importance.

Does schema design matter? Very important, but don’t over-engineer the early stages. You can draw on some ideas from the "data lake library" to retain the original flexibility in the buffer and establish a certain contract between the middle layer and the application layer. The key is to maintain the ability to evolve.

How to manage quality? Set checkpoints on the pipeline through which data flows. For example, record the amount of data synchronized every day, monitor the null value rate of key fields, and compare whether the same indicator from different sources is roughly consistent. These simple monitoring can detect many problems in advance.

Maybe you can ask yourself: We introduced microservices in the first place so that the team can deliver value independently and quickly. So now, with the data we generate for these services, have we also built a "living room" that allows them to independently and quickly create information value?

This is not just a technology project, but a thinking about how to make data work together again. When the small streams generated by each microservice can smoothly merge into a calm lake, what you gain will not only be a clear reflection, but also a stronger vitality of the entire ecosystem.

Established in 2005,kpowerhas been dedicated to a professional compact motion unit manufacturer, headquartered in Dongguan, Guangdong Province, China. Leveraging innovations in modular drive technology, Kpower integrates high-performance motors, precision reducers, and multi-protocol control systems to provide efficient and customized smart drive system solutions. Kpower has delivered professional drive system solutions to over 500 enterprise clients globally with products covering various fields such as Smart Home Systems, Automatic Electronics, Robotics, Precision Agriculture, Drones, and Industrial Automation.