Data Lake vs Data Lakehouse: Key Differences Explained for Businesses

If you’re here for a while you’ve seen the words “data lake” and “data lakehouse” quite a bit. If you’re like most business leaders or data professionals, you may have wondered — what’s the real difference and what business actually needs?

What is a Data Lake?

Before diving into the topic of data lake vs data lakehouse, let’s first look at the definitions of both.

A data lake is really a large bucket in the middle which lets you store any kind of type of information structured, semi-structured or unstructured in its native format. This is a giant digital storage tank, if you will. You pour in everything: Logs, images, videos, sensor data, CSVs, JSONs, anything goes and you just work it out after you pour it in.

At the time, this “store first, think later” paradigm was a turning point and was particularly important in a context where traditional data warehouses are costly to scale, and with a fixed structure. Data lakes, by contrast, are flexible, easy to keep up to date and much more cost-effective, particularly if implemented on a cloud object storage platform such as Amazon S3 or Azure Blob Storage.

Challenges with Data Lakes

In theory, data lakes were amazing, but in reality, a number of uncomfortable truths became apparent to businesses:

1. Data quality challenges: If not managed appropriately, data lakes can become “data swamps” — filled with raw, poorly documented and unreliable data.
2. No ACID transactions: Traditional data lakes don’t support transactional consistency, which means your data pipelines can break or produce incorrect results.
3. Slow query performance: Queries of raw, unoptimized data in object storage are painfully slow for business intelligence (BI) use cases.
4. Lack of support for BI tools: Most analytics and reporting tools are designed to operate with structured data rather than raw data in a data lake.

Yes, data lakes have provided businesses with the capacity to store all their data, but making it useful was another story.

What Is a Data Lakehouse?

A data lakehouse is a new data architecture that brings the best of both worlds together: The low-cost, flexible storage of a data lake with the data management, reliability and performance of a data warehouse.

Put simply, the data lakehouse was created because of all the frustrating parts of data lakes, while preserving the good parts.

How Does a Data Lakehouse Work? 

The magic is the new metadata and transaction layer over the data lake storage. This is made possible by technologies such as Delta Lake (Databricks), Apache Iceberg and Apache Hudi. These open source table formats deliver ACID transactions, schema enforcement, data versioning and time travel natively to your existing cloud storage.

This is a quick summary on how it works:

1. Data remains in open formats (such as Parquet) in inexpensive cloud object storage.
2. There is a transaction log associated with the data, which ensures consistency in the data.
3. Data can be efficiently read by query engines such as Apache Spark, Trino, or Presto.
4. BI tools can integrate directly to the same extent as they do with a traditional warehouse.

This architecture means your data scientists, engineers and business analysts can all access the same data source without the duplication of data to multiple systems.

Data Lake vs Data Lakehouse: What’s the Difference?

Let’s now dive into the nitty-gritty: the data lake vs data lakehouse comparison that is crucial for most businesses to make a wise choice about.

1. Data Governance and Quality

Typically, governance is an afterthought in a traditional data lake. There may be a data catalog or you can tag the datasets but there’s no provision for rules to be enforced. There are problems with data quality in most instances, with inconsistent or duplicate data.

In contrast, a data lakehouse ensures schema-on-write, that is, checking data structure when it is written, not when it is read. This will significantly enhance data quality and enable better “data trust”.

2. ACID Transactions

This is a big one. Traditional data lakes are not suited for ACID transactions (Atomicity, Consistency, Isolation, Durability). When data is written to a job, part of the data may be corrupted or incomplete if the job fails in the middle of the write.

ACID transactions are completely supported in data lakehouses (with Delta Lake or Iceberg format). That means that failed tasks won’t mess up your data — something enterprise workloads simply need.

3. Query Performance

Data Lakes aren’t designed for quick SQL queries. Scanning terabytes of raw files is time-consuming and costly.

Data lakehouses offer capabilities such as data indexing, Z-ordering, data skipping, caching, and much more, which greatly enhance query speed — at times even outperforming traditional data warehouses.

4. Support for Streaming and Batch Data 

While both architectures are capable of batch data processing, data lakehouses definitely have an advantage with regards to real-time/streaming workloads. Batch and streaming pipelines can be seamlessly managed without having to maintain separate infrastructure.

5. Cost

Building and maintaining data lakes is typically more affordable because all you are doing is storing data in file format on object storage. Data lakehouses add a little overhead (query engines need computing, metadata management), but you save money on having a separate data warehouse.

Must Read: Top 10 Best Practices for Efficient Data Management

Data Lake vs Data Warehouse vs Data Lakehouse

Perhaps you’re saying to yourself, “Where does the data warehouse come into the picture?

Good question. The brief version:

A data warehouse (Snowflake, Google BigQuery, Redshift) works great with structured data and quick SQL analytics but is expensive, rigid and closed-format. A data lake is inexpensive and easy to create, but is also cluttered and difficult to query. A data lakehouse is a combination of the openness and scale of the data lake and the reliability and efficiency of a data warehouse.

For organizations that already have a data warehouse in place, the data lakehouse model can save costs and complexity, particularly for machine learning users and teams that utilize BI analytics.

What’s the Best Option for your Business?

The straight answer: it depends on the point you are at in your data maturity process.

For startups or small businesses just starting to gather data, a simple data lake could suffice to get them going. It’s low maintenance and inexpensive.

For mid-to-large enterprises deployed with BI reporting, data science, and real-time analytics, the data lake vs data lakehouse dilemma is very easy and obvious: Lakehouse! It’s a worthwhile investment in terms of performance, governance and reliability.

Final Thoughts

It’s not a data lake vs data lakehouse debate, rather, a data lake architecture vs a data lakehouse architecture. Data lakes made data storage accessible to everyone. Data lakehouses are making data more accessible.

When you face problems like slow queries, data quality issues, and the expense of maintaining a data lake in addition to a data warehouse, you may be ready to seriously consider adopting a data lakehouse architecture. It’s an established technology, its ecosystem is open and its returns are significant for most companies.

The data lakehouse is right in the middle of the future of modern data architecture: open, unified, and intelligent.

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