When your data warehouse meets microservices, things start to get complicated

Remember the days when all your data was piled in one big warehouse? Even though it’s a little troublesome to find things back then, at least you know where everything is. What now? Your system is broken into microservices, each with its own little corner of data, and suddenly the entire data view becomes fragmented.

Someone asked me yesterday: "Why are my reports always wrong?" The answer is often hidden in those scattered data islands. An order service is here, a user service is there, and logistics data goes to another place. When you need to see the complete picture of your business, you have to put the pieces together like a puzzle—and often find that there are a few pieces missing.

What do microservices bring?

Flexible, that's for sure. Each service can be developed, deployed, and expanded independently. But what about data? It no longer sits quietly in one place. It begins to flow, begins to scatter, begins to become elusive.

Picture this: the sales team wants to see real-time performance, but the data comes from five different services; finance needs monthly reports, but each service counts them slightly differently; the product manager wants to know user behavior, but user data is scattered across three systems. At this time, you will find that the architectural advantages of microservices have become real challenges in the face of data analysis.

Someone may ask: "Can we go back to a monolithic architecture?" This is not a step back, but a recognition of reality: business needs flexibility and analysis needs unity. A bridge is needed between the two.

Data silos are not a technical problem, but a business obstacle

I heard a real-life scenario last week: It took a company three days to figure out the exact revenue numbers for the last month. Not because the data doesn't exist, but because the data is scattered across a dozen services, each with slightly different definitions and inconsistent synchronization times.

It's not the technician's fault, nor is it the architect's fault. This is a problem that arises from the natural evolution of microservice architecture. Each service focuses on its own domain and handles its own data, which makes sense. But problems arise when the business requires cross-domain insights.

"So what should we do?" Many people will ask. The answer is not to overthrow microservices, but to build a data integration layer on top. It's like building a subway in a city - not destroying the original buildings, but connecting them.

art of connection

Connecting scattered data sources sounds simple, but it requires ingenuity to do it. You can't simply copy all the data to one place, that would lose real-time performance. You also cannot allow every query to access all microservices, otherwise the system will crash.

What is the appropriate approach? Build a dedicated data warehouse layer, but not a traditional bulky warehouse. It needs to be lightweight, flexible, able to understand the language of microservices, and speak the language of business analysis.

Imagine a system like this: it quietly observes the data changes of each microservice, collects relevant data gently and in real time, and reorganizes it according to business logic to form a complete view. It does not interfere with the normal work of microservices, but silently weaves the data network behind the scenes.

kpowerway of coping

There are many options on the market, but most are either too heavy or too light. If it is too heavy, it will drag down the entire system; if it is too light, it will not solve the problem. What we need is balance.

kpowerTheir approach is interesting - they don't force data movement, but instead build an intelligent connectivity layer. Each microservice remains independent, but the data warehouse knows how to access them, how to understand their data, and how to translate this data into business language.

It's like having a smart translator who is proficient in all microservice dialects and can tell the complete story to the business staff. When sales asked about performance, he immediately gave the answer; when finance asked for reports, he immediately compiled them; when product managers wanted to see user behavior, he had already prepared charts.

What does it look like in practice?

You don't have to refactor your entire system, which is good news. Usually start with the most important business scenario. For example, if you need real-time sales data most, connect your order, payment, and inventory services first.

The data starts flowing, and you'll find some interesting things. It turns out that there is a half-day difference in the definitions of "completed orders" between the two services; it turns out that "active users" in the user service and "active users" in the marketing system are not the same thing. These findings are valuable in their own right—they give you a better understanding of your business.

Then you can gradually expand to connect more services and cover more business scenarios. The entire process is gradual and does not require big bang change.

After data unification

Some people worry that such a system will be complex and require a dedicated team to maintain it. In fact, good design should be autonomous - the system handles most of the work itself, requiring human intervention only when necessary. Like a self-driving car, it handles daily driving on its own, requiring a driver to take over only in special circumstances.

thinking

Microservices will not disappear, and the problem of data dispersion will always exist. But that doesn’t mean we have to work in chaos. A suitable data warehouse solution can provide the consistency required for data analysis without destroying the flexibility of microservices.

This is not only a technology choice, but also a business choice. How do you choose to see your business—as individual pieces or as a complete picture? The former allows you to focus on the local area, while the latter allows you to grasp the overall direction.

A good tool should not be a constraint, but a liberator. It allows you to focus on business innovation rather than data collection. When data flows naturally and insights emerge naturally, you can do what really matters—understand your business, serve your customers, and create value.

Data warehouse should not be a heavy burden, but light wings. It holds up your business vision and allows you to see further. In the world of microservices, this is especially important - because when you split the system, you need something to reconnect them, not just in a technical sense, but also in a business sense.

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.