In any e-commerce, every click counts, including:
View of product Add to cart Checkout Attempt
Each event generates data, and therefore creates millions/billions of events every single day.
Now think about what would happen if all this data were delayed, inconsistent, or lost.
Irrelevant recommendations Delayed pricing decisions Failed marketing campaigns
That's why data infrastructure for e-commerce is not just typically a backend database. It's far more important than that - it drives revenue, customer experience, and competitive advantage for an e-commerce company.
If you are a CTO/VP Engineering responsible for scaling your company’s e-commerce data infrastructure, you should be considering your platform as scalable and capable of handling:
High volume of event streams Real-time decision AI-enabled personalisation
While still being reliable and cost-effective.
In this guide, you will learn:
What is unique to e-commerce data infrastructure How supporting a massive scale of data effects architecture High-performing teams who design resilient, high-performance systems
Now let’s begin with the fundamentals:
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1: What sets e-commerce apart? E-commerce systems run in an extremely high-volume event environment.
1. Event Volume
Every user interaction creates data:
Views of a page Clicks on hyperlinks Search requests Transactions
At scale, this results in millions of events generated every hour.
2. Events are real/concurrent and continuous. Customer Expectations In Real-Time
The Three Requirements for Customers Are:
- Immediate Suggestions
- Dynamic Pricing
- Real-Time Inventory Updates
3. To Provide This Service You Must Have:
- Low Latency Data Pipelines
- Continuous Data Processing
- Seasonal & Spiky Traffic
Traffic Is NOT Consistent.
There Are Spikes In Traffic Due To Black Friday, Flash Sales And Marketing Campaigns Creating Immediate Spikes In Traffic Load.
4. There Are Many Types Of Data Consumers For Customers To Utilize Their Data Via:
- Product Teams
- Marketing Teams
- Analysis Teams And AI Systems
5. Revenue Impact
Unlike Other Industries, Revenue In E-Commerce Directly Correlates To Data.
The Longer The Delay, Caused By A Lack Of Efficiency, Results In:
- Decreased Conversions
- Customer Experience Is Affected
Key Takeaway
E-commerce Infrastructure Must Support Volume, Velocity And Variability; All At The Same Time.
2: Why Legacy Systems Are Not Suitable For E-commerce Scale
Legacy Systems Are Not Able To Support Today's Modern E-commerce
BATCH SYSTEMS LIMITATIONS
Batch Processing Systems Are:
- Time Interval Based
- Cause Delays To Process
- Limited Real-Time Functionality
Monolithic Architecture Problems
Monolithic Systems Are:
- Tightly Coupled
- Difficult To Scale
- Difficult To Maintain
Fragmentation Problems
When Systems Are Not Integrated:
- Data Is Inconsistent
- Duplicated Logic
- Increases Maintenance Costs
Cost Of Failure
Due To Lack Of Modern Infrastructure:
- Outdated Recommendations
- Inventory Mismatch
- Creates Poor Customer Experiences
For Example:
If You Are Running AnOutdated Inventory Update That Shows Inventory Availability It Will Result In Missed Orders Creating A Negative Impact To Revenue
What Are The Requirements Of Modern Systems?
- Real-Time Processing
- Scalable Architectures
- Constant Data
The Insight E-commerce Systems Do Not Fail From High Traffic Volume, They Fail Because They Are Not Designed To Support Continuous Data Flow.
Section 3: E-commerce Data Infrastructure Components
The Event Ingestion Layer gathers user interactions, system events, and transactions.
The Event Ingestion Layer captures user interactions, system events, and transactions.
High throughput is required by the Event Ingestion Layer for validated and fault-tolerant data capture.
The Streaming Processing Layer processes and modifies data as it is ingested.
The Streaming Processing Layer processes and transforms data in real-time with an aggregation and enrichment model approach.
The Storage Layer may support several types of data and storage types, including data lakes, lakehouses, and analytical warehouses.
The Orchestration Layer manages pipeline executions, dependencies, and failures.
The Serving Layer serves data to applications, dashboards, and APIs while maintaining low latency and high concurrency.
Together all five layers manage end-to-end e-commerce data delivery with an event being generated, ingested into the system, processed and analyzed in real-time, stored for future reference, and served back to applications.
E-commerce systems are needed to have a continuous data pipeline rather than one that processes data periodically.
Section 4: Real-Time Use Cases Driving E-Commerce Success
Use cases requiring real-time systems are extremely important for several key functions of e-commerce applications.
Some of these functions include:
- Personalized Recommendations
- Dynamic Pricing
- Inventory Management
- Fraud Detection
Example: Recommendation Engine
A user views products but if the e-commerce system does not provide real-time processing, the recommendation engine will not be able to provide recommendations in near real-time. Thus, user experience will suffer due to delays caused by low latency processing.
The challenges of ensuring low latency, managing the high volume of data, and implementing complex data transformations between each of these layers will require e-commerce organizations to maintain both real-time data and batch data processing at the same time.
By having real-time capabilities, there is a direct increase in conversion rates and improved customer experience.
Section 5: Common Team Missteps
Organizations make several critical mistakes althoughThe early stages of a project can lead to excessive engineering, especially when it comes to constructing complex systems and high scale architectures without validating the needs for them. Not having observability of systems can create issues that go unnoticed and result in a difficult debugging process. Without data contracts, schema drift will occur and thus pipelines will break as well.
Real-time processing should not be required on all systems, and when you have a fragmented toolset of too many tools, there becomes an increased level of integration challenge and complexity.
Most failures arise from poor design as opposed to technological limitations.
Section 6: E-commerce Data Infrastructure Best Practices for Scalability.
The best performing teams operate according to a simple set of guiding principles.
First, design for scalability from inception, by building systems that are capable of evolving to meet increasing demands and accommodating spikes in activity.
Secondly, use a hybrid approach by using both real time pipelines and batch processes to create greater resiliency.
Thirdly, invest in observability which will provide you the ability to track the health of your pipeline, data freshness and system performance.
Fourth, standardize your data models to create a greater level of consistency and reliability.
Fifth, automate your validation processes to include schema checks and data quality monitoring.
Finally, always prioritize simplicity over complexity in order to achieve a successful outcome focused on maintainability.
Example of what an effective team looks like:
One of your customers is able to effectively handle spikes in traffic with out breaking; delivering consistent data and providing real-time insights into their customer's experience.
So you can see that scalability is not just about having a huge volume of data, but it's more about having reliable data when it counts.
LOGICIEL POV
The success of e-commerce today is not just about having products to sell. The success of e-commerce today depends on your system's ability to process data in real time, provide an excellent personalized experience, and scale without breaking. At Logiciel, we provide e-commerce companies with the ability to build a data infrastructure that is scalable, high performing and reliable in order to handle vast amounts of demand. If you are experiencing issues with your systems due to the increase of demand for your e-commerce business, now is the time to rethink your architecture. Contact Logiciel today to explore how our AI-first engineering teams can help you build scalable and high-performing data infrastructure for modern e-commerce.
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Frequently Asked Questions
What is data infrastructure in e-commerce?
Data infrastructure in e-commerce refers to the technology and process used for capturing, processing, storing and making available all the data collected through user interaction with an e-commerce business, in order to provide analytics, personalization, and operational decisions to support an e-commerce business's goals.
Why is real-time data important in e-commerce?
Real-time data allows for decision-making immediately, which improves personalization, pricing, and customer experience.
What are the biggest challenges facing e-commerce data systems today?
The biggest challenges facing e-commerce data systems are able to handle the high volume of events generated by customers; create real-time processing; and maintain data consistency.
How do companies process millions of event data each day?
By using scalable architectures, which can process the data in real-time.
