Modern data platforms must handle diverse data types, support real-time and batch processing, scale to petabytes, and provide reliable analytics. The architecture you choose determines whether your data platform becomes a competitive advantage or a technical debt burden.
The Modern Data Stack
Today's data platforms combine several layers, each optimized for specific functions:
- Ingestion: Collect data from various sources (APIs, databases, files, streams)
- Storage: Data lakes and warehouses (S3, GCS, Snowflake, BigQuery)
- Transformation: ETL/ELT pipelines (dbt, Airflow, Spark)
- Computation: Processing engines (Spark, Flink, Databricks)
- Analytics: BI tools and data applications (Tableau, Looker, custom apps)
1. The Medallion Architecture
The medallion architecture (bronze, silver, gold) is a best practice for organizing data in a data lake:
Bronze Layer (Raw Data)
Store raw, unprocessed data exactly as ingested. This is your source of truth.
- • Preserve original data format
- • Append-only storage
- • Partition by ingestion date
- • Minimal transformation
Silver Layer (Cleaned Data)
Cleaned, validated, and enriched data ready for analytics.
- • Data quality checks and validation
- • Deduplication
- • Schema enforcement
- • Basic transformations
Gold Layer (Curated Data)
Business-ready, aggregated data optimized for consumption.
- • Business logic applied
- • Aggregations and metrics
- • Optimized for query performance
- • Ready for BI tools
2. Data Lake vs. Data Warehouse
Modern platforms often use both:
- Data Lake: Store raw and processed data in open formats (Parquet, Delta Lake)
- Data Warehouse: Structured, query-optimized storage (Snowflake, BigQuery, Redshift)
- Lakehouse: Combine both—lake storage with warehouse capabilities (Delta Lake, Iceberg)
Best practice: Use a data lake for storage and flexibility, then load curated data into a warehouse for fast analytics.
3. ETL vs. ELT Patterns
The modern approach is ELT (Extract, Load, Transform):
- Extract: Pull data from source systems
- Load: Load raw data into the data lake
- Transform: Transform data using SQL or Spark in the platform
ELT is preferred because it's more flexible—you can transform data multiple times without re-extracting. Tools like dbt make ELT transformations manageable.
4. Real-Time vs. Batch Processing
Modern platforms support both:
Batch Processing:
- • Scheduled jobs (hourly, daily)
- • Large data volumes
- • Cost-effective for historical analysis
- • Tools: Airflow, Spark, dbt
Real-Time Processing:
- • Stream processing (Kafka, Kinesis)
- • Low-latency requirements
- • Event-driven architectures
- • Tools: Flink, Kafka Streams, Spark Streaming
5. Data Quality and Governance
Data quality is non-negotiable. Implement:
- Schema validation: Enforce schemas at ingestion (JSON Schema, Avro)
- Data profiling: Understand data distributions and anomalies
- Data lineage: Track data flow from source to consumption
- Data catalog: Document datasets, schemas, and ownership
- Quality metrics: Monitor completeness, accuracy, consistency
6. Scalability and Performance
Design for scale from day one:
- Partitioning: Partition data by date, region, or business key
- Clustering: Co-locate related data (BigQuery clustering, Snowflake clustering)
- Caching: Cache frequently accessed data (Redis, Memcached)
- Materialized views: Pre-compute aggregations
- Query optimization: Use columnar formats (Parquet) and compression
7. Modern Data Platform Tools
Recommended tool stack:
Storage
- • AWS S3, GCS, Azure Data Lake
- • Snowflake, BigQuery, Redshift
- • Delta Lake, Apache Iceberg
Processing
- • Apache Spark, Databricks
- • Apache Flink, Kafka Streams
- • dbt for transformations
Orchestration
- • Apache Airflow
- • Prefect, Dagster
- • AWS Step Functions
Streaming
- • Apache Kafka, Confluent
- • AWS Kinesis
- • Google Pub/Sub
8. Cost Optimization
Data platforms can get expensive. Optimize costs:
- Storage tiers: Move old data to cheaper storage (S3 Glacier, GCS Coldline)
- Compute optimization: Right-size clusters, use spot instances
- Query optimization: Reduce data scanned, use partitions
- Lifecycle policies: Automate data retention and deletion
- Reserved capacity: For predictable workloads
Conclusion
Modern data platforms require careful architecture. Start with the medallion pattern, choose the right storage layer, implement ELT pipelines, and prioritize data quality. The platform you build today will determine your analytics capabilities for years to come.
At Tengri Vertex, we design and build modern data platforms that scale. From data lake architecture to real-time streaming pipelines, we help organizations turn data into competitive advantage.