Data Lake vs Data Warehouse vs Data Lakehouse - simply explained

Table of Contents

1. Data Lake vs Data Warehouse vs Data Lakehouse: Key Differences
2. Data Warehouse Architecture for Developers
3. Data Lake Implementation Guide
4. Data Lakehouse: Best of Both Worlds
5. Data Lake vs Data Warehouse vs Data Lakehouse Comparison
6. Platform Selection Guide
7. Developer Decision Matrix

Understanding Data Lakes, Warehouses, and Lakehouses: The Developer's Guide

Before getting into comparisons, let's work out what we're actually talking about.

These architectures solve different problems, and understanding their core purpose helps you choose the right one.

Data Warehouses: Structure First

A data warehouse is a centralised repository designed for analytical queries. In other words, a highly optimised database specifically built for reporting and business intelligence.

Core characteristics:

• Schema-on-write: Data structure is defined before storage
• SQL-based: Standard SQL queries for analysis
• Optimised for reads: Fast analytical queries, slower writes
• Structured data: Works best with clean, organised data

Data warehouses emerged in the 1980s when businesses needed better reporting than operational databases could provide. Your PostgreSQL database is great for handling user transactions, but it struggles when marketing wants to analyse customer behaviour across millions of records.

Developer impact: Predictable query performance, familiar SQL interface, but rigid schema requirements mean data changes require careful planning.

Modern data warehouses like BigQuery and Redshift handle semi-structured data (JSON) better than traditional warehouses, but you'll still hit limitations with deeply nested or highly variable structures.

Data Lakes: Flexibility First

A data lake stores raw data in its native format—JSON files, CSV exports, images, logs, whatever you throw at it. No upfront structure required.

Core characteristics:

• Schema-on-read: Figure out structure when you query it
• Any data format: JSON, Parquet, images, logs, streaming data
• Scalable storage: Handle petabytes of data cost-effectively
• Flexible processing: Batch processing, real-time streams, machine learning

Data lakes gained popularity in the 2000s as companies generated massive amounts of unstructured data. Web logs, social media data, IoT sensors—traditional databases couldn't handle the volume and variety.

Developer impact: Ultimate flexibility for data storage, but querying requires more work. You might need Spark jobs or specialised tools instead of simple SQL.

Data lakes can quickly become "data swamps" without proper governance.

You'll need data cataloging, quality monitoring, and clear ownership models to prevent this.

Many companies struggle with data discovery and trust in lake environments.

Data Lakehouses: Best of Both Worlds?

A data lakehouse combines the flexibility of data lakes with the performance and reliability of data warehouses. It's the newest approach, emerging around 2020.

Core characteristics:

• ACID transactions: Reliable data operations like a database
• Schema enforcement: Optional structure when you need it
• Multiple formats: Structured and unstructured data in one place
• BI tool compatibility: Standard SQL interface for analysts

Lakehouses emerged because teams were tired of managing both data lakes and data warehouses. Why maintain two separate systems when you could get the benefits of both?

Developer impact: SQL queries on flexible data storage, but newer technology means less mature tooling and potential complexity.

Lakehouse ACID guarantees are eventually consistent, not immediately consistent like traditional databases.

Cross-table transactions aren't supported.

This matters for applications requiring strict consistency.

The technology is also newer, meaning fewer Stack Overflow answers and more complex troubleshooting.

Understanding Lakehouse Architecture: The Best of Both Worlds?

Now that you understand the trade-offs, let's examine how lakehouse architecture actually works under the hood. Understanding these technical components helps you evaluate whether the complexity is worth the benefits.

Technical Components That Make It Work

Delta Lake, Apache Iceberg, and Apache Hudi are the three main technologies powering lakehouse architecture. Easiest way to think of them is layers that add database-like capabilities to your data lake storage.

Delta Lake (developed by Databricks, now open source):

• Adds ACID transactions to data stored in cloud storage (S3, Azure Blob, GCS)
• Handles schema evolution without breaking existing queries
• Provides time travel—query your data as it existed last week
• Optimises file layouts automatically for better performance

Apache Iceberg (Netflix's approach, now Apache project):

• Similar ACID guarantees with better performance for massive datasets
• Excellent schema evolution and column-level operations
• Works across multiple compute engines (Spark, Trino, Flink)
• Handles partition evolution without rewriting data

Apache Hudi (Uber's solution):

• Focuses on incremental data processing and upserts
• Excellent for streaming data and real-time updates
• Optimised for analytics on frequently changing data
• Strong integration with Apache Spark

From a developer perspective, these technologies solve the "eventual consistency" problem of traditional data lakes. No more wondering if you see the latest data.

Medallion Architecture: Bronze, Silver, Gold

Lakehouse implementations typically use a medallion architecture to organise data quality and processing stages.

Bronze Layer (Raw Data Landing):

• Stores data exactly as received from source systems
• No transformation, validation, or cleaning
• Includes API responses, database dumps, log files, streaming data
• AKA "source of truth" backup

# Example: Raw user events landing in bronze
bronze_events = spark.read.json("s3://lakehouse/bronze/user_events/")

Silver Layer (Cleaned and Enriched):

• Applies data quality rules and basic transformations
• Standardises formats, handles nulls, removes duplicates
• Joins related data sources where beneficial

# Example: Cleaned events with user enrichment
silver_events = bronze_events \
    .filter(col("event_type").isNotNull()) \
    .join(user_profiles, "user_id") \
    .withColumn("event_date", to_date("timestamp"))

Gold Layer (Business-Ready Data):

• Aggregated, business-specific data models
• Optimised for specific use cases (dashboards, ML features)
• May include data marts for different departments
• Direct consumption by BI tools and applications

# Example: Daily user engagement metrics
gold_metrics = silver_events \
    .groupBy("user_id", "event_date") \
    .agg(count("*").alias("daily_events"))

Solving Data Warehouse Limitations

Traditional data warehouses struggle with:

Schema Rigidity: Adding a new column to your user events table requires careful ETL changes, testing, and coordination. With lakehouse architecture, you can start writing new JSON fields immediately and add them to your silver layer when needed.

Data Freshness: Most data warehouses batch-load data daily or hourly. Lakehouses can handle streaming updates, giving you near real-time analytics without separate stream processing infrastructure.

Diverse Data Types: Lakehouses store everything natively and let you decide how to process it. So if you want to analyse user behaviour alongside support chat transcripts and product images you can do.

Solving Data Lake Limitations

Traditional data lakes suffer from:

No ACID Guarantees: Half-written files, inconsistent data states, and "data swamps" where nobody trusts the data quality. Lakehouse formats provide database-level reliability.

Schema Discovery Problems: Finding usable data in a sea of files becomes a data governance nightmare. Lakehouse catalogs and schema enforcement make data discoverable and trustworthy.

Performance Issues: Querying massive Parquet files scattered across thousands of folders is slow. Lakehouse optimisation creates proper indexes and data layouts automatically.

Developer Experience (from my experience)

The Good:

• Standard SQL works for most queries (no need to write Spark jobs)
• Schema evolution doesn't break existing code 
  • Caveat: While lakehouses handle schema changes more gracefully, you still need to manage backward compatibility and coordinate with downstream consumers. "Breaking" changes like column type modifications still require careful planning.
• Time travel queries help with debugging and compliance
• Modern BI tools connect directly without custom integrations

The Challenging:

• Learning curve for concepts like Delta tables and partitioning strategies
• Performance tuning requires understanding file layouts and optimisation commands
• Debugging query performance involves both SQL and distributed systems knowledge
• Tool ecosystem is newer with fewer Stack Overflow answers

API Example:

# Delta Lake Python API
from delta import DeltaTable

# Read with time travel
df = spark.read.format("delta").option("versionAsOf", 5).load("/delta/events")

# Upsert operation (merge)
delta_table = DeltaTable.forPath(spark, "/delta/customers")
delta_table.alias("customers") \
    .merge(new_customers.alias("updates"), "customers.id = updates.id") \
    .whenMatchedUpdateAll() \
    .whenNotMatchedInsertAll() \
    .execute()

Query Performance: Well-optimised lakehouse queries can approach data warehouse performance for structured data in specific scenarios. However, columnar data warehouses still significantly outperform lakehouses for complex analytical queries with multiple JOINs and aggregations.

The reality: Cold start times, query planning overhead, and concurrency limitations in lakehouses add latency. For sub-second user-facing queries, traditional data warehouses remain the safer choice.

Platform Selection Guide

AWS

AWS offers multiple paths to lakehouse architecture, which can be both a blessing and a curse for dev teams.

What you get:

• Amazon Redshift Spectrum: Query data in S3 directly from your data warehouse
• AWS Glue: Managed ETL service with visual interface and code generation
• Lake Formation: Data lake setup and governance in a few clicks
• Athena: Serverless SQL queries on S3 data

Developer experience: AWS documentation is comprehensive but can be overwhelming. You'll spend time figuring out which service combination fits your needs. The upside? If you're already using AWS for your application hosting, everything integrates smoothly.

Learning curve: Medium to high. AWS assumes you understand their ecosystem of interconnected services.

When you first log into the AWS console (like I did ~ 5 years ago), it can be really daunting. As of writing there are between 200-300 services available.

Pricing:

• Redshift: £0.25 per hour for smallest cluster (dc2.large)
• Athena: £4 per TB of data scanned
• Glue: £0.35 per DPU-hour for ETL jobs
• S3 storage: £0.02 per GB/month

Best for: Teams already invested in AWS ecosystem who want flexibility to choose their own architecture stack.

Pitfall to avoid: Analysis paralysis from too many options. Start with Athena + S3 for simple querying, then add complexity as needed.

Microsoft Azure

Azure's strength lies in enterprise integration, especially if your organisation uses Microsoft tools.

What you get:

• Azure Synapse Analytics: Unified platform combining data warehousing, big data, and machine learning
• Power BI integration: Seamless connection to Microsoft's business intelligence tools
• Azure Data Factory: Visual ETL designer with drag-and-drop interface
• Active Directory integration: Single sign-on with existing corporate accounts

Developer experience: Azure Synapse provides a notebook interface similar to Jupyter, making it approachable for developers with Python experience. The Power BI integration is genuinely impressive—build a data pipeline and create dashboards in the same environment.

Learning curve: Low to medium. If you know SQL and basic Python, you can be productive quickly.

Pricing:

• Synapse SQL Pool: £8 per DWU per hour (minimum 100 DWUs)
• Synapse Serverless: £4 per TB processed
• Data Lake Storage: £0.02 per GB/month
• Power BI Pro: £8 per user/month

Best for: Organisations already using Microsoft Office 365 or Teams who want tight integration with existing workflows.

If your finance team already uses Excel and Power BI, they can directly connect to your lakehouse data without learning new tools.

Databricks

Databricks created the term "lakehouse" and builds their platform specifically around this architecture.

They're opinionated about how things should work.

What you get:

• Unified Analytics Platform: Notebooks, ETL, ML, and BI in one interface
• Delta Lake: Built-in ACID transactions and time travel
• MLflow integration: Machine learning lifecycle management
• Collaborative notebooks: Real-time collaboration like Google Docs for data analysis

Developer experience: Databricks notebooks feel familiar if you've used Jupyter. The platform handles infrastructure complexity behind the scenes. However, you're locked into their way of doing things.

Learning curve: Medium. The notebook interface is beginner-friendly, but optimising performance requires understanding Spark concepts.

Thankfully there are a lot of great books and YouTube videos out there to help you learn.

Pricing:

• Standard clusters: £0.15 per DBU + underlying compute costs
• Compute costs: £0.05-0.20 per hour depending on cluster size
• Premium features: Additional £0.55 per DBU for advanced security and collaboration

Pricing warning: DBU costs add up quickly. A small development cluster can easily cost £200-500 per month if you're not careful.

When starting opt for single node clusters with an automatic kill time (~20 minutes).

Best for: Teams who want a cohesive platform designed specifically for data science & BI workflows.

Trade-off: Excellent developer experience but potential vendor lock-in and higher costs.

Google Cloud

One I'm less experienced with, so I did some research (we're both learning with this one!).

Google's approach focuses on serverless, managed services that scale automatically without cluster management.

What you get:

• BigQuery: Serverless data warehouse with machine learning capabilities
• BigLake: Lakehouse functionality built on top of BigQuery
• Dataflow: Managed Apache Beam for stream and batch processing
• Looker: Google's business intelligence platform

Developer experience: BigQuery's web interface is intuitive and fast. You write SQL queries and get results without thinking about infrastructure. The learning curve is gentle if you know SQL.

Learning curve: As of writing, I feel like it's on the lower side.

Pricing:

• BigQuery storage: £0.02 per GB/month
• Query processing: £4 per TB processed
• BigLake: No additional charges beyond BigQuery pricing
• Dataflow: £0.05 per vCPU hour + memory costs

Cost advantage: Pay only for queries you run. No need to keep clusters running 24/7.

Best for: Feels like it would suit teams who want to focus on analytics and applications rather than infrastructure management.

Limitation: From my research, less flexible than other platforms if you need custom data processing logic.

Open Source Alternatives

If you want to avoid vendor lock-in or have specific technical requirements, open-source solutions provide complete control.

Core components:

• Apache Spark: Distributed data processing
• Delta Lake, Apache Iceberg, or Apache Hudi: Lakehouse storage formats
• MinIO or native cloud storage: S3-compatible object storage (I use this for SAAS development)
• Apache Airflow: Workflow orchestration (Used at companies like Revolut)
• Trino or Presto: Distributed SQL query engines

Developer experience: Complete flexibility but significant operational overhead. You'll need someone comfortable with Docker, Kubernetes, and distributed systems concepts.

Learning curve: High. This approach requires strong DevOps skills alongside data engineering knowledge.

True cost calculation: While the software is free, factor in:

• DevOps engineer time for setup and maintenance (£100k+ annually)
• Cloud infrastructure costs (similar to managed services)
• Opportunity cost of not building application features

Best for: Teams with strong infrastructure capabilities who need custom functionality or have strict data sovereignty requirements.

Making Your Choice

Choose AWS if: You're already using AWS services and want maximum flexibility to build custom architecture.

Choose Azure if: Your organization uses Microsoft Office 365 and wants seamless integration with existing business tools.

Choose Databricks if: You're building data science applications and want a cohesive platform designed specifically for machine learning and BI workflows.

Choose Google Cloud if: You want simple, serverless analytics without infrastructure management complexity.

Choose open source if: You have strong technical capabilities and specific requirements that managed services can't meet.

Budget-Conscious Recommendations

For startups (< £1000/month budget): Start with Google BigQuery or AWS Athena. Both offer pay-per-query pricing that scales with usage.

For growing companies (£1000-5000/month): Azure Synapse or Databricks Community Edition provide good balance of features and cost control.

For enterprises: Databricks or AWS with dedicated support contracts. The higher costs are justified by reduced operational overhead.

Choosing the Right Architecture: Developer's Decision Matrix

This framework covers the most common scenarios for development teams.

The 5-Minute Architecture Decision Tree

Step 1: What's your team's SQL comfort level?

• High: Everyone writes complex queries confidently → Consider all options
• Medium: Basic JOINs and aggregations, some struggle with subqueries → Data warehouse or lakehouse
• Low: Mostly application developers who avoid SQL → Data warehouse only

Step 2: What data types are you working with?

• Mostly structured (database exports, CSV files, API responses) → Data warehouse
• Mix of structured and files (logs, images, documents) → Lakehouse
• Primarily unstructured (images, videos, IoT sensors, social media) → Data lake

Step 3: What's your primary use case?

• Business intelligence and dashboards → Data warehouse
• Machine learning and data science → Data lake or lakehouse
• Both BI and ML equally important → Lakehouse
• Regulatory reporting with strict requirements → Data warehouse

Step 4: What's your performance requirement?

• Sub-second query responses for user-facing features → Data warehouse
• Batch processing overnight is acceptable → Data lake
• Mix of real-time and batch requirements → Lakehouse

Step 5: What's your budget reality?

• Under £500/month → Data warehouse (BigQuery, Athena)
• £500-2000/month → Any option works
• £2000+/month → Consider total cost including team time

Team Skill Assessment Matrix

Future-Proofing Your Choice

Red flags that suggest you might need to change later:

• Choosing data warehouse but planning machine learning features within 12 months
• Choosing data lake but business users need self-service analytics
• Choosing lakehouse without team members comfortable with distributed systems concepts

Safe migration paths:

• Data warehouse → Lakehouse: Relatively straightforward, export and restructure
• Data lake → Lakehouse: Natural evolution, add governance layer
• Lakehouse → Data warehouse: More complex, requires restructuring

Architecture future-proofing checklist:

• ✅ Can your choice handle 10x data growth?
• ✅ Does it support your planned features in the next 18 months?
• ✅ Can new team members become productive quickly?
• ✅ Are you avoiding vendor lock-in where possible?
• ✅ Does the total cost scale predictably?

The "When in Doubt" Default

If you're still unsure after this framework: choose a cloud data warehouse (BigQuery, Redshift, or Azure Synapse).

Why this is the safe choice:

• Familiar SQL interface reduces learning curve
• Predictable query performance for business users
• Mature ecosystem with abundant documentation
• Easy migration path to lakehouse architecture later
• Lower operational overhead for development teams

You'll hit walls with unstructured data, real-time streaming, and machine learning use cases. But these limitations become clear gradually, giving you time to plan migrations.

You can always evolve: Most successful data architectures start simple and add complexity as requirements become clearer. It's easier to migrate from a well-designed data warehouse to lakehouse than to untangle a poorly planned data lake.

TL;DR Summary

• Data warehouse wins most scenarios - familiar SQL, predictable performance, mature tooling, but limited with unstructured data and real-time use cases
• Choose lakehouse only if you need both BI and ML capabilities with mixed data types and have team members comfortable with distributed systems
• Platform choice depends on your ecosystem - use what integrates with your existing tools
• Team skills matter more than technical perfection - pick what your team can actually implement and maintain
• Start simple, evolve later - easier to migrate from warehouse to lakehouse than fix a poorly planned data lake