A data pipeline architecture defines how data moves from source systems to storage, processing, analytics, and downstream applications in a reliable and scalable manner.
1. Source Systems
Data originates from databases, APIs, SaaS applications, files, logs, IoT devices, or transactional systems. Sources can generate structured, semi-structured, or unstructured data. The architecture should understand the source's data format, volume, frequency, and availability. Source characteristics determine whether batch, incremental, CDC, or streaming ingestion is appropriate. Examples include PostgreSQL, Salesforce, application logs, and REST APIs.
2. Data Ingestion Layer
The ingestion layer extracts data from source systems and transfers it into the data platform. It can use batch ingestion, incremental loading, CDC, or real-time streaming. Tools such as AWS Glue, Kafka, AWS DMS, and APIs can implement different ingestion patterns. A robust ingestion layer handles authentication, retries, duplicates, schema changes, and failures. Its primary responsibility is reliably capturing data without unnecessarily modifying the source data.
3. Raw Data / Landing Layer
The raw layer stores data as close as possible to its original form after ingestion. Cloud object storage such as Amazon S3 is commonly used because it provides scalable and relatively inexpensive storage. Raw data acts as a historical source that can be reprocessed when transformation logic changes. Typical organization includes partitions such as source/table/year/month/day. This layer provides replayability, auditability, and recovery.
4. Processing & Transformation Layer
The processing layer cleans, validates, joins, enriches, aggregates, and transforms raw data. Technologies such as Apache Spark, PySpark, SQL, AWS Glue, and Snowflake can perform these operations. Transformations may include deduplication, data-type conversion, business rules, aggregations, and dimension lookups. Large workloads may require distributed processing and optimization techniques such as partitioning and broadcast joins. The output becomes progressively more useful for analytical consumption.
5. Curated / Serving Layer
The curated layer contains cleaned and business-ready datasets designed for specific consumers. Data may be stored in a warehouse such as Snowflake or Amazon Redshift, or in lakehouse tables such as Apache Iceberg. Data models commonly contain fact and dimension tables following dimensional-modeling principles. This layer should provide consistent definitions and trusted metrics. BI tools, analysts, ML workloads, and applications consume this layer.
6. Orchestration Layer
Orchestration controls when and how pipeline tasks execute and manages dependencies between them. Tools such as Apache Airflow can define workflows as DAGs consisting of tasks and dependencies. For example: Extract → Validate → Load Raw → Transform → Quality Check → Publish. Orchestrators also provide scheduling, retries, backfills, failure handling, and dependency management. This turns individual processing scripts into a manageable production workflow.
7. Data Quality Layer
Data quality checks ensure that incorrect or incomplete data does not propagate downstream. Typical checks include null validation, duplicate detection, schema validation, record counts, referential integrity, freshness, range checks, and source-to-target reconciliation. Quality failures should be captured and routed to appropriate alerts or quarantine areas. In mature platforms, data quality is treated as part of the pipeline rather than as a separate manual activity.
8. Monitoring & Observability
Production pipelines need visibility into both technical and data-related behavior. Monitoring tracks metrics such as execution time, failures, throughput, processing volume, latency, and resource utilization. Data observability additionally monitors freshness, volume anomalies, schema changes, and data distributions. Logs, metrics, traces, alerts, and lineage help engineers identify the root cause of failures. The goal is not merely knowing that a pipeline failed, but understanding why it failed and what downstream data was affected.
Common Data Pipeline Architecture
A typical cloud data pipeline can be represented as:
Source Systems → Ingestion → Raw Storage → Processing → Curated Data → Data Warehouse/Lakehouse → BI/ML/Applications
For a streaming architecture:
Applications/IoT → Kafka/MSK → Stream Processing → Data Lake/Warehouse → Real-Time Analytics
Important Design Patterns
1. Batch Pipeline
Data is collected and processed periodically.
Example:
Database → S3 → Glue/Spark → Snowflake → BI
Suitable for daily reports, historical processing, and workloads where low latency isn't required.
2. Streaming Pipeline
Events are processed continuously as they arrive.
Example:
Application → Kafka/MSK → Spark/Flink → Data Lake → Real-Time Dashboard
Suitable for fraud detection, monitoring, IoT, and real-time analytics.
3. Lambda Architecture
Uses separate batch and streaming paths to provide both comprehensive historical processing and low-latency results.
4. Medallion Architecture
Data progresses through:
Bronze → Silver → Gold
- Bronze: raw data
- Silver: cleaned and standardized data
- Gold: business-ready data
5. CDC-Based Pipeline
Instead of repeatedly extracting the entire source table, the pipeline captures inserts, updates, and deletes.
Source DB → CDC → Kafka/DMS → Data Lake/Warehouse
This significantly reduces unnecessary data movement for large transactional systems.
Best Practices
- Make pipelines idempotent so rerunning a failed job does not create duplicate results.
- Prefer incremental processing instead of repeatedly processing the entire dataset.
- Separate raw and curated data so original data remains recoverable.
- Implement data-quality checks at important pipeline boundaries.
- Design for schema evolution because source schemas can change over time.
- Partition large datasets intelligently to reduce unnecessary data scanning.
- Implement retries and dead-letter/quarantine mechanisms for failures.
- Monitor both pipeline health and data quality.
- Secure data using IAM, encryption, secrets management, and appropriate access controls.
- Maintain metadata and lineage so you can determine where data came from and which downstream systems depend on it.
Key Interview Concept
A strong data pipeline is not simply a mechanism for moving data.
It is a system designed for:
Reliability + Scalability + Performance + Data Quality + Observability + Security + Recoverability
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