One can easily assume that data lakes are the natural evolution of the old, clunky data warehouses. Another can claim that the difference is all about your workload; that is, if it is more about machine learning, then you would be better with a data lake, and if it were more about reporting, then a data warehouse is what you need. That would be a huge understatement. In reality, the difference between data warehouses and data lakes depends on many points that are seldom discussed together. Let’s try to break them down:

The Big Picture: Monolithic vs. Modular

Data warehouses are typically all-in-one platforms. They integrate storage, compute, and metadata engines into a single cohesive system. This tightly coupled architecture allows for high performance and optimized query handling, but comes at the cost of flexibility.

On the other hand, data lakes embrace modularity. They decouple storage, compute, and metadata into separate components that can be swapped or scaled independently. This separation brings versatility and scalability, allowing businesses to optimize each layer individually. Although some data warehouses are starting to support plug-and-play components (e.g., MySQL storage engines or PostgreSQL add-ons), they still haven’t reached the decoupling flexibility of data lakes.

Storage Models: Rows, Columns, and Everything in Between

Row-Oriented vs. Column-Oriented Databases

In data warehouses, storage is typically handled by relational databases, which fall into two camps:

Row-oriented databases (e.g., MySQL, PostgreSQL, SQL Server) are ideal for scenarios where you read or write a small number of rows but require all columns. They're optimized for real-time updates and point queries based on primary keys. However, they struggle with aggregations across large datasets, especially if you only need a subset of columns.
Column-oriented databases (e.g., ClickHouse, SAP HANA, Apache Druid) shine when you need to read a few columns across many rows, which is common in analytics workloads. However, they come with downsides like poor performance on real-time writes and limitations in updates or deletes. They also prefer batch inserts to streaming.

Non-Relational Options

Although uncommon, some data warehouse implementations incorporate non-relational engines for specific tasks. These include:

Document databases (e.g., MongoDB)
Key-Value stores (e.g., Redis)
Wide-column stores (e.g., Cassandra)
Graph databases (e.g., Neo4j)
Full-text search engines (e.g., Elasticsearch)

These are typically used to support more flexible schemas or optimize certain types of queries.

Data Types and Limits

Data warehouses handle a wide range of data types, from basic (int, float, varchar) to complex (JSON, XML, binary). Some engines also provide unique types like UUID or IP address. However, warehouses come with inherent storage limitations:

Table size caps: e.g., PostgreSQL (32 TB), MySQL InnoDB (64 TB)
Column limits per table: PostgreSQL (1600), MySQL (1017–4096)
Row and index key size limitations

These limits exist primarily because data warehouses keep metadata in memory for fast querying. The memory footprint restricts how large a schema can grow.

Data Lakes: Limitless Flexibility

In contrast, data lakes have no schema restrictions by default. They store raw data as files—be it CSV, JSON, Parquet, ORC, or even binary blobs. The absence of enforced schemas gives you total freedom, but it also places more responsibility on the querying engine to parse, interpret, and validate data.

Files in a data lake are typically stored in object storage (e.g., Amazon S3, Google Cloud Storage, Azure Blob Storage), which offers extreme scalability and durability. Storage limits are effectively governed only by the size of individual files (typically 5 TB for most cloud providers).

The independent nature of files allows you, if you need, to use both a column-oriented file format (e.g., Parquet) and a row-oriented file format (e.g., Avro) at the same time.

Compute: Who Does the Heavy Lifting?

Data Warehouses: Memory-Optimized Compute

Data warehouses utilize memory buffers and indexes to maximize performance. This gives them a substantial performance advantage when handling structured queries. They often leverage multi-threading and, in some cases, multi-core execution per query.

Some data warehouses support replication to distribute workloads across multiple machines. However, replication introduces complexity and can lead to synchronization issues or latency during failover.

Thanks to their fixed schemas and reliance on block storage, data warehouses generally offer faster read/write speeds and better support for ACID compliance (Atomicity, Consistency, Isolation, Durability).

Data Lakes: Decoupled, Scalable Compute

Data lakes use separate compute engines such as Apache Spark, Trino, Hive, or StarRocks. These engines typically follow a leader-worker model where workers can scale dynamically without needing to restart the leader node. The compute layer can be deployed on virtual machines or containers, offering flexibility and cost-efficiency.

However, since data lakes rely on object storage, they tend to have higher latency and lower throughput compared to block storage used in data warehouses. Also, ACID compliance is limited, though modern data lakehouse technologies (like Delta Lake, Apache Hudi, and Apache Iceberg) are addressing this gap.

Scaling and Availability

Data Warehouses: Vertical Scaling and Replication

The scalability of a data warehouse is mostly bound by the hardware. Databases typically handle a limited number of connections depending on how they manage concurrency. There are two models in handling connections:

PostgreSQL: process-per-connection
MySQL: thread-per-connection

This limits them to thousands of concurrent users in most cases. To scale reads, replication is often used, but replication introduces complexity (e.g., sync issues, network delays).

Data Lakes: Horizontal Scaling by Design

Because of their decoupled nature, data lakes can scale horizontally with ease. Their storage backend object storage supports virtually unlimited concurrent IO operations. While compute engines do have limits, the query throughput can be scaled by simply adding more workers.

Data lakes also excel in availability and disaster recovery. Since they are built on top of distributed object storage systems, they benefit from built-in redundancy, high durability, and geo-replication features.

Which One Should You Choose?

There’s no one-size-fits-all answer, and for the lack of a better expression, it truly “depends on the use case”. However, from all the facts we mentioned, we can make some general recommendations. If you need huge performance, or great support for streaming, or don’t have huge volumes of data, then data warehouses should be a great fit for your needs. On the other hand, if you deal with very large amounts of data, or very different structures of data, or have a very non-uniform workload, then data lakes will be more suitable for you.

Final Thoughts

The lines between data lakes and data warehouses are increasingly blurred. Technologies like Snowflake, BigQuery, and Databricks are building bridges between the two, resulting in lakehouses that combine the flexibility of data lakes with the performance of data warehouses, to some degree at least.

Understanding the foundational differences between these two architectures remains critical. Whether you’re building a modern data stack from scratch or trying to scale an existing one, the decision between a data lake and a data warehouse will impact your performance, cost, and agility for years to come.

Data Warehouses vs. Data Lakes: Understanding the right fit