From Data to Decisions: A guide to serving insights for maximum impact
Master the final step in data engineering, serving data, to empower stakeholders, drive smarter decisions, and transform insights into action.
Every data journey has a destination. In our previous posts, we covered the early stages of the data engineering lifecycle: ingesting and transforming raw data into usable insights. Now, we’re at the exciting final leg, serving that refined data so analysts, data scientists, and ML engineers can work their magic.
Serving data is more than making data accessible; it’s about enabling stakeholders to extract real business value, supporting informed decisions and impactful outcomes.
In this post, inspired by insights from
’s Data Engineering course, we’ll explore ways to serve data for downstream applications/use cases;Analytics
Machine Learning
Reverse ETL
1. Analytics (BI, Operational, Embedded Analytics)
Definition: The process of identifying key insights and patterns within data.
The three most common forms of analytics:
a. Business Intelligence (BI)
Role of Data Engineers: They prepare and serve historical and current business data in structured formats, creating data pipelines for reports and dashboards.
Example: A marketing team uses BI dashboards to track campaign engagement and regional sales. Data engineers ensure the data is clean, transformed, and accessible, helping analysts spot trends and make informed decisions.
b. Operational Analytics :
Role of Data Engineers: They deliver real-time data streams for immediate actions and decisions, ensuring quick and reliable access to event data, logs, or transactions.
Example: An e-commerce platform monitors website performance metrics in real-time. Data engineers provide real-time log data to dashboards, enabling the operations team to react instantly to outages or traffic spikes.
c. Embedded Analytics
Role of Data Engineers: They allow historical and real-time data to be integrated into customer-facing applications, allowing end-users to access insights within products.
Example: A bank's mobile app shows users their spending habits and trends. Data engineers process and serve transaction data so customers can view real-time updates on their spending behaviour.
2. Machine Learning
Definition: Using algorithms to let systems learn from data and make predictions or decisions without explicit programming.
Role of Data Engineers: They create feature stores, manage data for model training, and provide data for real-time inference.
Example: A company predicts customer churn using a machine learning model. Data engineers manage the pipeline feeding historical and real-time customer data to the model.
3. Reverse ETL
Definition: Sending transformed and analysed data back into source systems to enhance or update them.
Role of Data Engineers: They extract and transform data from source systems and ensure results, like machine learning predictions, are integrated back into the original systems.
Example: After training a lead-scoring model, data engineers push results into the CRM system, enabling sales teams to prioritise the most promising leads.
Conclusion
Data engineers are essential for helping businesses get value from data, whether for analytics, machine learning, or improving operations. By making data clean, accessible, and easy to use, they enable fast decisions and support advanced modelling.
The data engineering process ends with the serving stage, where feedback helps refine the system. Listening to stakeholders and making improvements based on their input is key to building better, more impactful data solutions.
For more on the Data Engineering Lifecycle, explore our previous posts on:
The Crucial Midpoint of Data Engineering: Data Transformation
If you'd like to learn more about Data Engineering please check:
[Data Engineering Professional course] on Coursera/Deeplearning.ai.
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Thanks for sharing this amazing top view of Data Engineers. I have much better clarity on the tasks they do now.