April 11, 2025
Share this blog
Top Data Engineering Concepts and Architectures You Need to Know
As data becomes the backbone of modern enterprises, data engineering is increasingly central to how organizations ingest, store, transform, and serve information. For anyone working in analytics, machine learning, or software development, a solid understanding of data engineering concepts and architectures is now a must-have.
This blog explores the foundational and advanced data engineering concepts, core system architectures, and how they fit into the broader landscape of modern data infrastructure.
We'll break down:
- Key data engineering principles
- Essential architectural patterns
- Tools and technologies used in data engineering
- Best practices for scalable and reliable systems
- The role of data observability in modern data stacks
Let's get started.
What is Data Engineering?
Data engineering is the practice of designing, building, and maintaining the systems that collect, process, and store data at scale. It ensures that raw data is transformed into usable and reliable formats for downstream analytics, business intelligence, and machine learning applications.
Core responsibilities of data engineers include:
- Building data pipelines
- Designing data models
- Ensuring data quality and consistency
- Managing data storage
- Enabling access through APIs, queries, and platforms
Core Data Engineering Concepts
1. Data Ingestion
The process of collecting data from various sources such as databases, APIs, logs, IoT devices, and third-party platforms.
- Batch ingestion: Scheduled, large-volume jobs
- Streaming ingestion: Real-time or near-real-time data capture
Tools: Kafka, Flume, NiFi, Logstash
2. Data Transformation
Converting raw data into cleaned, enriched, and structured formats suitable for analysis.
- Cleaning (null handling, deduplication)
- Aggregation (summarizing)
- Normalization (standardizing formats)
Tools: dbt, Spark, SQL, Pandas
3. Data Storage
Choosing appropriate storage systems based on volume, velocity, and access needs.
- Data Lakes: Store raw, unstructured data (e.g., S3, ADLS, HDFS)
- Data Warehouses: Structured data optimized for querying (e.g., Snowflake, Redshift, BigQuery)
- Lakehouses: Combine lake and warehouse functionality (e.g., Delta Lake, Apache Iceberg)
4. Data Modeling
Structuring data to reflect business needs and ensure efficiency.
- Star schema, snowflake schema
- Normalized vs. denormalized models
5. ETL vs. ELT
- ETL (Extract, Transform, Load): Transformation happens before loading into storage.
- ELT (Extract, Load, Transform): Transformation happens inside the data warehouse.
Modern stacks increasingly favor ELT using tools like dbt.
6. Data Lineage
Understanding where data originates, how it moves, and how it's transformed throughout its lifecycle.
7. Data Governance and Security
- Access controls and role-based permissions
- Encryption at rest and in transit
- Auditing and compliance (GDPR, HIPAA)
8. Data Quality Management
Monitoring and validating data to ensure it's complete, accurate, consistent, and timely.
Key Data Engineering Architectures
1. Batch Architecture
Processes data in scheduled intervals (e.g., nightly jobs).
- Tools: Apache Hadoop, Airflow, Spark
- Pros: Simpler, cost-efficient for high-latency use cases
- Cons: Not suitable for real-time needs
2. Streaming Architecture
Processes data as it arrives.
- Tools: Apache Kafka, Apache Flink, Spark Streaming
- Pros: Low latency, supports real-time analytics
- Cons: Complex to build and maintain
3. Lambda Architecture
Combines batch and streaming.
- Batch layer + speed layer + serving layer
- Offers balance between latency and completeness
- Tools: Kafka, Hadoop, Spark, Cassandra
4. Kappa Architecture
Streaming-first; no separate batch layer
- All data flows through a single stream processing layer
- Simplifies maintenance but may limit reprocessing
5. Modern Cloud-Native Architecture
Focuses on scalability, decoupling, and ease of deployment.
- Based on tools like Snowflake, dbt, Fivetran, Airflow
- Built on cloud object storage (S3, GCS)
- Emphasizes modular ELT and observability
Tools Every Data Engineer Should Know
Orchestration
Ingestion
Transformation
Storage
Observability & Monitoring
- Great Expectations
- OpenLineage
- DataDog
- Rakuten SixthSense
Data Observability: The Missing Piece in Modern Architectures
Even the best-built pipelines can break silently without visibility. That's where Data Observability becomes essential.
Why it matters:
- Detect silent failures (volume drops, schema drift, stale data)
- Monitor pipeline health and SLAs
- Trace lineage and root cause
- Prioritize issues by business impact
How Rakuten SixthSense Helps:
- Real-time anomaly detection
- Freshness and schema monitoring
- End-to-end data lineage
- Seamless integration with modern data stacks
👉 Explore our interactive demo 👉 Learn more about Data Observability
Best Practices for Modern Data Engineering
- Design for Scalability from Day One
- Use Version Control and CI/CD Pipelines
- Automate Testing and Validation
- Embrace Modularity and Reusability
- Implement Security and Governance Early
- Enable End-to-End Observability
- Focus on Developer Experience (DevEx)
- Involve Stakeholders in Modeling Decisions
Final Thoughts
As data continues to grow in both volume and complexity, understanding core concepts and architectures in data engineering becomes essential for building sustainable, trustworthy systems.
Whether you're a new engineer, data leader, or platform owner, mastering these fundamentals will help you architect resilient, high-performance data infrastructure.
And with tools like Rakuten SixthSense, you can gain the observability needed to keep your pipelines healthy, your data reliable, and your teams efficient.
Ready to modernize your data stack? 👉 Try Rakuten SixthSense today and see how observability transforms data engineering at scale.