Temberton Insights

Passion For Data

Bens' Blog

What is the Difference Between a Data Lake and Data Warehouse?

Searching for the ideal data storage infrastructure for your business needs?

When doing so, you’d be forgiven for confusing the difference between a data lake and data warehouse.

Data lakes and data warehouses are both powerful data infrastructure that store vast quantities of data for organizations.

The similarities between the two, however, tend to end there.

Let’s compare two key differences between a data lake and a data warehouse – their structure and purpose. In doing so, you’ll have a better understanding of which data storage infrastructure will provide the most value for your organization and its business intelligence (BI) needs.

The Structure of a Data Lake vs. Data Warehouse

One key difference between a data lake and data warehouse is the structure of the data they pull in and hold.

A data lake stores any and all data, structured or unstructured, in its raw format. This raw data comes from a variety of company data sources.

There are a number of benefits to storing data in its native format. With the right expertise, raw data can be easily pulled from the data lake and analyzed for any purpose.

Data lakes are especially useful when applying machine learning (ML) techniques, for example.

There is, however, a potential downside to storing any and all raw data in a data lake.

If you’re not careful, a data lake will quickly become a data swamp! To avoid drowning in data, always be sure to use data quality best practices, like the 6C’s of data quality.

If your organization has a specific use case for the data it wishes to store, a data warehouse may be a better solution.

A data warehouse is much more selective in both the data it stores and the purpose of storing it. Specifically, a data warehouse pulls in historical data that’s structured to fit a relational database schema.

The Purpose of a Data Lake vs. Data Warehouse

Let’s consider the purpose of raw, unprocessed data in a data lake compared to processed data held in a data warehouse.

When data is in its raw format, it can be pulled and analyzed from a data lake for any future use.

Raw data is generally lest costly to store than processed data. As such, data lakes are cost-effective solution when storing vast quantities of data.

Data lakes are also cost-effective because they can be easily scaled up or down to match the volume of storage needed.

With a data warehouse, the data is already processed for a specific purpose, so only the data that is needed for said purpose is stored.

The benefit of storing only structured data for a specific purpose is that structured data is easier to analyze. This means the data can be more readily used by various business functions that understand the nature of the data.

The same cannot be said of data lakes. Vast quantities of raw data may require a data scientist to pull and analyze to glean the necessary insights. This is changing, however, with the advent of tools that can help business professionals in accessing and analyzing raw data.

Data Lake or Data Warehouse – Which is best for my business?

The answer? It can be difficult to say without knowing your exact business needs.

A data lake is a highly flexible and cost-efficient data storage solution – if you have the right manpower to gain insights and make predictions from the data.

If your business needs are more specific and you don’t have the expertise on hand to play with raw data, a data warehouse may be the solution.

Thankfully, you don’t need to find the right data solution for your business alone. Temberton Analytics will guide you in the process and find the right solution for you.

At Temberton Analytics, we specialize in consulting, building, and managing data infrastructure, including data lake and data warehouse, for business intelligence in the financial services, insurance, banking and healthcare industries.

Contact us today to schedule a free consultation with our data experts.

Be sure to subscribe to our newsletter for more monthly data-driven insights.

Temberton Analytics, Inc.