In our high-level article, we explored how Hybrid Data Warehousing merges the structure of a traditional DWH with the flexibility of a Data Lake, offering a middle path between rigid modeling and data swamp 🐸. Now, let’s go deeper: into the architectural principles, real-world design patterns, challenges of this hybrid architecture.
Quick Recap ↻
A Hybrid Data Warehouse brings together two coexisting ecosystems:
- A Data Warehouse built for governance, structured data, and SQL-based consumption (BI, KPIs, dashboards).
- A Data Lake built for flexibility and scale, supporting big data processing, machine learning, and the handling of semi-structured and unstructured data — with native support for Python, Scala, and other languages.
In this model, data flows into either or both systems depending on the usecase, purpose, and performance. The goal isn’t to duplicate blindly but to balance control and agility.
Why this architecture ?
Dump everything into the lake and analyze it later ! 🚮
The rise of cloud object storage and big data tools led to a belief that Data Lakes would make Data Warehouses obsolete. But the outcome was often the opposite of agility: without metadata, structure, or lineage, many lakes turned into data swamps 🐸.
On the other hand, Data Warehouses are excellent at supporting business reporting but struggle with modern demands such as real-time ingestion, variable schemas, and large volumes of semi-structured data.
Also, business analysts and data analysts typically work well with SQL, but tasks like setting up a Spark cluster or running Hive jobs are more suited to data scientists. That’s why your architecture needs to align with the skills of your team, each role requires the right tools to be effective.
Hybrid DWH emerged as a pragmatic solution: use the DWH for reliability and trust, use the Lake for scale and flexibility, and orchestrate between them based on use case.
Core Architectural Principles
Data Warehouse Layering & Tools
The DWH still follows a layered model (as hybrid model):
- 1️⃣ Staging layer: where raw data structured lands after extraction, often minimally transformed.
- 2️⃣ DWH layer (or core): where structured data is cleansed, standardized, and modelized into a normalized model (3nF-ish or Data Vault) aligned with business domains.
- 3️⃣ Data Mart layer (or presentation): where structured data is reshaped into denormalized, domain-specific views for consumption by reporting tools or dashboards.
Principle tools : BigQuery, Snowflake, Redshift, Synapse (cloud MPP); ClickHouse, Greenplum, Teradata (on-prem); etc.
Data Lake Layering & Tools
Well-architectured (so governed) Data Lakes also adopt a layered approach:
- Raw Layer: Stores all ingested data in its original format (JSON, CSV, Avro, images, videos, etc.) as an immutable system of record. Unstructured data typically remains in this layer, while structured/semi-structured data progress through subsequent layers for processing.
- Conformed Layer: Converts raw files into standardized, columnar formats like Parquet or ORC for interoperability and performance. Non-conforming data is stored in the Quarantine Layer ⛔️.
- Cleansed Layer: Contains validated, de-duplicated, and enriched datasets, ready for downstream use.
- Presentation Layer: Applies business logic to produce consolidated datasets optimized for end-user consumption, dashboards or ML pipelines.
Principle tools: S3 (AWS or on-prem), ADLS, GCS, HDFS (Storage) ; Spark, Hive, Flink (Processing); Parquet/ORC (Formats); etc.
Multi-Tenant vs Non-Tenant
A a multi-tenant approach physically separates data by business unit, region, or legal entity, offering more control but increasing complexity and duplication.
In a non-tenant (centralized) architecture, all data is stored in a one unified Data Lake with logical separation by folder, schema, or metadata. This simplifies governance and enables cross-domain analytics, but reduces autonomy and can create regulatory issues. Thus, many organizations use virtualization techniques (e.g. RBAC, RLS) to simulate multi-tenancy in a one Data Lake.
However, regulations like GDPR or U.S. privacy law (like CCPA or HIPAA) may prohibit even the virtual co-location of sensitive data. This often requires physically separating data across regions, cloud accounts, or storage layers, turning the use of multiple data lakes from a design preference into a regulatory necessity.
Overview of Hybrid Data Warehouse Patterns
Pattern 1: “Lake as Source of Truth”
All raw data, structured, semi-structured, and unstructured, is first ingested into the Data Lake. Only consolidated, structured subsets are then transferred into the Data Warehouse for downstream analytics and reporting.
Key Advantages ✅:
- Full auditability and raw data retention for compliance and replayability
- Enables recovery from ETL failures without re-ingesting source data
- Centralized ingestion pipelines simplify onboarding new data sources
Trade-Offs ⚠️:
- Introduces data duplication, with the same datasets stored in two systems
- Schema fidelity impacts data quality and metadata richness : consuming raw Parquet files in a DWH typically provides less contextual information than ingesting data directly from the original source with full metadata.
Pattern 2: Parallel Ingestion
Structured data is simultaneously ingested into both the Data Lake and the Data Warehouse, enabling distinct use cases without bottlenecks.
Key Advantages ✅:
- Preserves rich schema metadata within the Data Warehouse
- Keeps raw data accessible for data science and exploratory workloads
- Avoids unnecessary transformations just to stage data in the lake
Trade-Offs ⚠️:
- Introduces data duplication, with the same datasets stored in two systems
- Requires synchronization mechanisms to maintain consistency
Pattern 3: Reverse Lakehouse
Consoldiated data from the Data Warehouse are exported back into the Data Lake to power machine learning and data product pipelines.
Key Advantages ✅:
- Ensures ML models use trusted, high-quality datasets
- Minimizes risk from inconsistent or incomplete training data
- Bridges the gap between BI and AI workflows
Trade-Offs ⚠️:
- Introduces data duplication, with the same datasets stored in two systems
- Requires strong governance of downstream lake datasets
The problem here.
In hybrid setups, we combine the strengths of DWH and Data Lake technologies to support both BI and AI on the same platform, but this comes with trade-offs:
- Two storage solutions → more people involved, higher costs
- Complex pipeline orchestration
- Fragmented governance and metadata
- Different storage formats and schemas
- Different update cadences
However, these trade-offs can be acceptable if, for instance, your company prefers to stick with legacy technologies (such as Teradata for banks) or opts not to use cloud-based solutions (such as Lakehouse or Data Fabric), in which case it may remain a reasonable compromise.
To conclude, Is this architecture still relevant?
Hybrid Data Warehousing let you mix the best tools for the job, whether it’s BI dashboards or machine learning workflows. It might be surprising, but a good number of large companies are operating at this stage today, but the problem of duplication, governance, and complexity are real challenges.
That’s why many organizations are now turning to architectures like Data Lakehouse or Data Fabric, or adopting approaches like Data Virtualization, all aiming to provide a more unified and streamlined data foundation.
👉 Stay tuned for our next deep dive: Architectural Deep Dive: Data Lakehouse or check the global overview.