Understand The Key Differences Between a Data Lake And a Data Warehouse

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December 7, 2021 by Updated March 18th, 2024

Although both data lakes and data warehouses are used for storing big data, they are not synonymous with each other. One would agree that both terms have some elements in common.

  • Both help organizations in better decision-making
  • Both store huge volumes of enterprise data
  • Both are of interest to data analysts and data scientists

However, both are conceptually different in terms of their design and implementation. Let’s take a look at the differences between a Data Lake and a Data Warehouse.

What is a Data Lake?

Data lake architectures store volumes of data in their original form – structured, unstructured, and semi-structured. Ideal for machine learning use cases, data lakes provide SQL-based access to data and provide support for programmatic distributed data processing frameworks like Apache Spark and Tensorflow via languages such as Python, Scala, Java, and more. Data lakes support native streaming, where streams of data are processed and made available for analytics as they arrive. The data pipelines transform the data as it is received from the data stream and trigger computations required for analytics. Data lakes make organizational data accessible to data scientists, data analysts, data engineers, business analysts, etc. from different sources to improve business performance in a cost-effective manner.

What is a Data Warehouse?

In contrast, data warehouses are structured data used for a specific business purpose. As an enterprise data technology, data warehouses store current and historical data in a single place, where it can be stored, consolidated, and analyzed, making it easily accessible to the organization to make informed decisions on key initiatives. Data warehouses support sequential ETL operations, where data flows in a waterfall model from the raw data format to a fully transformed set. The data warehouse architecture relies on the structure of the data to support highly performant SQL (Structured Query Language) operations. Some newer data warehouses support semi-structured data such as JSON, Parquet, and XML files. Businesses are relying more on data warehouses since structured data helps them make decisions confidently. Data warehouses are becoming more vital for businesses wanting to make informed decisions.

Data Lake vs. Data Warehouse

Data structure: Raw Data vs. Processed Data

Data lakes primarily store raw, unprocessed data. Raw data is data that has been unprocessed for a purpose. Ideal for machine learning, raw data is easy to analyze. On the other hand, data warehouses store processed data. Unlike raw data, this processed data can be easily understood by a large number of people. It also saves on expensive storage space. The risk involved is that a large amount of data can sometimes turn into data swamps where some of the data may never be used.

Purpose: Undetermined vs. Operational

Data lakes have less organization and less filtration of data than data warehouses. When the raw data is put to a specific use, it is called processed data. This means that the data that is stored is not wasted and that it will be used for some purpose within the organization.

Users: Data scientists vs. business professionals

The unprocessed, raw data makes it very difficult to navigate through data lakes. Such unstructured data needs a data scientist or a specialized tool to understand the raw data and utilize it for a specific business purpose. The processed data can be used in charts, spreadsheets, tables, and more so that the employees at a company can read it. Processed data only requires that the user be familiar with the topic represented.

Accessibility: Flexible vs. secure

Data lake architecture has no structure, making it flexible to access and easy to change. Given the very few limitations of data lakes, any changes made to the data can be done quickly. Data warehouses are, by design, more structured, making them difficult and expensive to manipulate. One major advantage of data warehouse architecture is that the processing and structure of data make the data easier to decode.

Data Lake and Data Warehouse

Organizations often need a mix of both a data lake and a data warehouse. The need to harness big data and benefit from the raw, unstructured data gave birth to data lakes but one still needs structured data for analytics that organizations can use.

Healthcare: Data lakes store unstructured information

Healthcare sectors need data lakes since the data is unstructured (such as physicians’ notes, clinical data, etc.). The data warehouse does not have much utility in the healthcare sector and hence isn’t an ideal model. With a combination of structured and unstructured data, data lakes are a better option for healthcare companies.

Education: Data Lakes Offer Flexible Solutions

The education sector deals with a lot of unstructured data – attendance records, academic records, student details, fees, and more. This data is very raw and vast, making data lakes the perfect fit in the education sector.

Finance: Data Warehouses Appeal to the Masses

A data warehouse is an ideal storage model in the finance sector and other business settings because it has structured data that can be accessed by any employee of a company, rather than data scientists. Since the data is structured, the model is more cost-effective for financial services companies.

Transportation: Data Lakes Help Make Predictions

Much of the benefit of a data lake’s insights lies in the ability to make predictions. Flexible data helps make predictions in the field of transportation, especially in supply chain management. This can have huge benefits in cutting down the cost by examining data from the transport pipeline.

Data Lake vs. Data Warehouse: Which One Do You Need?

Data lakes should be considered when:

  • Data are not known in advance
  • Data types do not easily fit a tabular or relational model
  • Datasets are either very large or are growing fast
  • Relationships between data elements aren’t understood in advance
  • Applications include data exploration, predictive analytics, and machine learning where it is valuable to have complete, raw datasets

A data warehouse should be considered when:

  • Organizations are confident of what data they need to use and are comfortable discarding the rest of it
  • Data formats are not expected to change with time
  • Organizations run standard sets of reports requiring fast, efficient queries
  • Results need to be drawn from accurate, carefully curated data
  • Regulatory or business requirements dictate the special handling of data

Parameters to consider when comparing Data lake and Data warehouse

The table below shows the key differences in structure, process, users, and overall agility that make each model unique. Depending on your company’s needs, one can choose a data lake or data warehouse, and this in turn will become instrumental in the organization’s growth.

Data LakeData Warehouse
Data StructureRawStructured, Filtered
UsersData scientists, Data architectsBusiness analysts and operational users
AccessibilityHighly accessible and quick to updateMore complicated to make changes
Data CurationStores EverythingSelective in terms of storing data
CostLow cost per GBGenerally expensive
Data FormatDiverseProprietary

Data Lake vs Data Warehouse: In Conclusion

To conclude, in a market where data is available in huge volumes, leveraging it in ways that could benefit your organization is what needs to be understood. It is important to realize the complementary functions that both data lake and data warehouse platforms offer and work towards a modern architecture that brings the best out of both platforms.

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