7 Major Stages of the Data Engineering Lifecycle

A solid data foundation makes life easier for all data professionals—analysts, scientists, and engineers alike. But what exactly defines a well-implemented data pipeline?

Let’s step into the shoes of an ecom giant like Flipkart, and explore how they approach different stages of the data engineering lifecycle.

At each stage, we’ll uncover the strategies, decisions, and trade-offs Flipkart makes to ensure scalability, reliability, and efficiency.

1. Data Collection & Storage

To build a data engineering pipeline, we first need data. But where is this data being generated, and how do we ingest it into our systems for further processing?

What Happens Here?

Data is collected from multiple sources such as:

• APIs (e.g., payment gateway APIs, third-party marketing data, weather APIs for demand forecasting)

• Databases (e.g., MySQL for order management, PostgreSQL for customer profiles)

• Event Streams (e.g., real-time sales transactions, customer interactions, clickstream data, IoT-based inventory tracking)

• Logs and Flat Files (e.g., user activity logs from web servers, application logs, CSV reports from vendors)

Once collected, data is stored in an appropriate system:

• Data Lakes (AWS S3, Azure ADLS, GCS) for unstructured/semi-structured data.

• Data Warehouses (Snowflake, BigQuery, Redshift) for structured data.

• Lakehouses (Databricks Delta Lake) for both structured and unstructured data.

Example Use Case:

Flipkart collects real-time sales transactions from its e-commerce platform, customer data from its relational databases, payment details via APIs, and application logs from its microservices architecture. This data is initially stored in Google Cloud Storage (GCS) and later ingested into BigQuery for analytics.

Key Tools:

Apache Kafka, AWS Kinesis, GCS, S3, ADLS, Snowflake, Delta Lake.

2. Data Processing & Quality

At this stage of our Data Engineering Lifecycle, we have Raw data which is often messy, redundant, and incomplete. How do we clean, validate, and transform it into meaningful information that is ready for business use?

What Happens Here?

• Transformations like filtering, deduplication, and aggregations.

• Handling schema evolution and incremental loads.

• Conducting data quality checks to catch nulls, anomalies, and inconsistencies.

Example Use Case:

Flipkart removes duplicate customer entries from its database, aggregates daily sales, and validates transaction data using Great Expectations before loading it into analytics tables. It also applies sentiment analysis on customer reviews using NLP models.

Key Tools:

Apache Spark, Databricks, dbt, Apache Beam, Great Expectations, PySpark.

3. Data Modeling

Once data is ingested and cleaned, how do we structure it efficiently for fast querying and analysis? What kind of schema should we use, and how do we optimize data storage?

What Happens Here?

• Define schemas, relationships, and partitions.

• Normalize or denormalize data based on analytical needs.

• Implement indexing, partitioning, and clustering for performance optimization.

Example Use Case:

Flipkart structures its sales data into a star schema, with a fact table for transactions and dimension tables for products, customers, and time. This enables efficient sales trend analysis across different product categories and regions.

Key Tools:

dbt, Snowflake, BigQuery, Azure Synapse, Databricks.

4. Data Orchestration & Workflow Automation

Data pipelines often consist of multiple interdependent steps. How do we ensure they execute in the correct order, handle failures, and run on schedule? This is one of the most important steps of any Data Engineering Lifecycle.

What Happens Here?

• Define task dependencies and execution order.

• Schedule ETL jobs dynamically.

• Implement retries, failure handling, and alerting.

Example Use Case:

Airflow is used to schedule and monitor Flipkart’s ETL pipeline, ensuring data is processed every hour. Failed jobs trigger Slack alerts to the data engineering team for immediate resolution.

Key Tools:

Apache Airflow, Prefect, Dagster, Azure Data Factory.

5. Governance & Security

Data is a valuable asset, but it also comes with risks. How do we enforce security, track data lineage, and ensure compliance with regulations?

What Happens Here?

• Implement Role-Based Access Control (RBAC) and data encryption.

• Ensure compliance with regulations (GDPR, HIPAA).

• Track data lineage for auditability.

Example Use Case:

Flipkart enforces data access controls, ensuring only authorized users can view customer PII data. It tracks data lineage to ensure compliance with GDPR and audits every data modification in its data lake.

Key Tools:

DataHub, Apache Atlas, Collibra, Monte Carlo, Immuta.

6. CI/CD for Data Pipelines

How do we ensure that our data pipelines are reliable, version-controlled, and deployed smoothly without breaking production systems?

What Happens Here?

• Implement Git-based version control for pipeline scripts.

• Automate testing & deployment using CI/CD workflows.

• Ensure reproducibility with Infrastructure-as-Code.

Example Use Case:

Flipkart uses GitHub Actions to automate testing and deployment of new data transformation scripts in Databricks, ensuring smooth updates without breaking existing pipelines.

Key Tools:

GitHub Actions, Jenkins, Terraform, Docker, Kubernetes.

7. Data Serving, Monitoring & Optimization

Once data is processed, how do we make it available for end users? How do we monitor performance and optimize costs?

What Happens Here?

• Provide data access for analytics, ML models, and dashboards.

• Monitor pipeline performance, cost efficiency, and SLA adherence.

• Optimize query execution (caching, indexing, cost control).

Example Use Case:

Flipkart uses Looker dashboards connected to BigQuery for real-time sales analytics, ensuring queries are optimized for cost and speed. Query execution logs are monitored to detect long-running queries, triggering performance tuning actions automatically.

Key Tools:

Power BI, Looker, Prometheus, Datadog, OpenTelemetry.

Conclusion

And there you have it – the 7 stages of the Data Engineering Lifecycle! From the very first step of data collection to the final stage of monitoring and maintenance, each stage keeps the data wheels turning smoothly. By mastering these stages, you’re not just managing data; you're unlocking its full potential to drive smarter decisions and business success. So, whether you're orchestrating pipelines or fine-tuning your CI/CD, remember: data engineering is the backbone that powers it all—let’s keep those data flows moving!

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