Data Lake vs Data Warehouse: Which One Does Your Business Actually Need?

All expanding businesses hit the same problem at some point: data is becoming excessive for anyone to make sense of. Logs, transactions, customer history, feeds from various sensors, spreadsheets — all must have a home, and crucially, a home where they can be transformed into decisions. This is when the data lake vs data warehouse dilemma appears, typically at a time when a team is attempting to choose the best way to store data, and doesn’t want to do it all over again in a year.

One of the most frequently asked questions that we receive from teams when they’re developing their analytics stack is this one. So let’s get to the bottom of it, what exactly are these systems, what is the difference between them and which one (or both) should you use in your architecture.

What Is a Data Lake?

A data lake is a single storage location where data can be stored in its native format, structured, semi-structured, and unstructured. Try to imagine all your logs in JSON, images, video, PDFs, CSVs, and database dumps all in the same place, without having to predefine an objective schema.

The essence of a data lake is “store now, structure later. It doesn’t need to be known at the point of ingestion how the data will be used. Data lakes are particularly favored by data science and machine learning teams, which frequently need to access raw data that hasn’t been filtered.The ability to access unfiltered raw data is a key advantage of data lakes, especially for data science and machine learning teams that rely on raw data for model training or exploratory analysis.

Examples of common data lake technologies are Amazon S3, Azure Data Lake Storage, Google Cloud Storage, and open table formats such as Delta Lake or Apache Iceberg on top of them.

What Is a Data Warehouse?

A data warehouse, on the other hand, contains data that has been cleaned, transformed and structured – typically, rows and columns that are built for rapid query performance. As data warehouses are designed for analytics and reporting—thinking dashboards, BI tools, and SQL queries that need to run in seconds, not minutes—these systems come in a variety of forms.

Data usually goes through an ETL or ELT pipeline before it arrives in a warehouse, transforming its format, deduplicating it and providing business logic. This results in a warehouse capable of being relied upon and quick for a business user, but not as flexible for raw or unusual data types.

Common data warehouse products include Snowflake, Google BigQuery, Amazon Redshift and Microsoft Fabric/Synapse.

Data Lake vs Data Warehouse: The Key Differences

Let’s get to the meat and potatoes of the data warehouse vs data lake comparison. There are a couple of points that are most important:

People tend to ask a cost-vs-structure question when they ask the data lake vs data warehouse question. Lakes optimize to store everything at a low price, and determine what is important at the end. Warehouses need to be built to speed up answering business questions, but must be designed in advance.

When to Use a Data Lake

A data lake is a good idea when:

• You have a huge amount of data that is raw or unstructured, such as logs, images, IoT streams, and you don’t know what you’ll use it for yet.
• You don’t want pre-aggregated summaries because those aren’t full-fidelity data for your data science team to use in building models.
• If query speed is an important concern for this layer of your stack, it’s not me.If query speed is an important concern for this layer of your stack, it’s not me.
• Before determining how to process data, you want to have one landing zone for data from many different sources.

When to Use a Data Warehouse

A data warehouse would be more appropriate when:

• Business teams require speedy and dependable dashboards and reports.
• Data should be consistent, well-governed and have a well-defined schema.
• You are running repeatable SQL analytics as opposed to exploratory data science.
• Performing queries and handling concurrency (users accessing the system concurrently) is important.

Moving From Data Lake to Data Warehouse

Most organizations don’t choose only one, but data flow through both in turn. The most common data lake to data warehouse scenario is:

1. Load data in to a data lake from apps, APIs and third-party sources.
2. Clean, Deduplicate and apply business rules to that data (typically using dbt, Spark, or a managed ELT pipeline).
3. Ingest the clean, well-formed output to the data warehouse.
4. Deliver that warehouse data to BI tools, dashboards and downstream applications.

This approach is commonly referred to as ELT (extract, load, transform) and is the approach for modern data stacks in general, to ensure that a full, raw copy of everything is stored in the lake while still providing a speedy and reliable warehouse for business users to query. You can reprocess from the lake as the logic for your transformation changes later, and will not lose historical raw data.

Do You Need Both? The Case for Data Lake and Data Warehouse Together

This is the essence of the question — it’s a not-or-other-than-or-this-and-this situation most of the time. Mid-size and large data teams are using a combination of a data lake and a data warehouse, referred to as a “lake-first” or hybrid architecture:

• The lake serves as the system of record — cheap, durable and in its original form.
• The Warehouse is the system of insight, that is fast, structured and tuned to the business’ actual queries of each day.

Newer architectures attempt to make this distinction less distinct, with some referred to as “data lakehouses” (the Delta Lake from Databricks and the Iceberg from Snowflake are just two examples). It sounds like a good idea, but for the majority of teams in existence these days, the more feasible, reliable alternative is a clear lake plus a warehouse pipeline.

How Datalance Approaches This

A typical initial step at Datalance is to determine the nature of the data a client has – the amount of raw and exploratory data versus structured and reportable data. A basic configuration using just a warehouse is good for starting teams. A lake-plus-warehouse pipeline is typically a fast return on investment for teams that have a high volume of ML workloads, multiple sources of unstructured data or multiple disparate sources.

Final Thoughts

There is no real competition between the data lake and the data warehouse, it’s choosing the right tool at the right time in the evolution of your data. Lakes are used for flexibility, scaling and raw storage. Warehouses are about fast, organized and succinct business responses. Most ‘mature’ data teams end up with both and then a good transformation pipeline between the two.

When you don’t know where your organization is now, or where it should be going, that’s a perfect opportunity where Datalance can help.