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Data warehouse vs. data lake: Key differences
Data warehouses and data lakes are both data repositories common in the enterprise, but what are the main differences between the two and which is best for your data?
The vast amount of data organizations collect from various sources goes beyond what traditional relational databases can handle, creating the need for additional systems and tools to manage the data. This leads to the data warehouse vs. data lake question -- when to use which one and how they compare to each other.
All of these data repositories have a similar core function: housing data for business reporting and analysis. But they differ in their purpose, their structure, the types of data they store, where the data comes from and who has access to it.
In general, data comes into these repositories from systems that generate data -- CRM, ERP, HR, financial applications and other sources. The data records created from those systems are applied according to business rules and then sent to a data warehouse, data lake or other data storage area.
Once all the data from the disparate business applications is collated onto one data platform, it can be used in data analytics tools to identify trends or deliver insights to help make business decisions.
What is a data lake?
A data lake is a vast repository that stores raw data in its native format. One benefit to a data lake is that it can store data of varying structures. Each stored data element is tagged with a unique identifier and metadata so it can be queried more easily when needed. Data lakes have no predefined schema, and analysts can apply the schema after the ingestion process is complete.
Data lakes are most commonly associated with a Hadoop framework, but data lakes are a supported architecture to many vendors as the influx of data continues to grow. Many vendors also support data lakes in the cloud.
What is a data warehouse?
A data warehouse is a repository for data collected and generated by business applications for a predetermined purpose. Data warehouses apply a predefined schema to data before storage, and data must be cleaned and organized before being stored in this repository.
Because data stored in a data warehouse is already processed, it is easier for high-level analysis. BI tools can easily access and use the processed data from a data warehouse, making it simpler for non-data professionals to use data warehouses.
Data warehouse vs. data lake
Organizations typically opt for a data warehouse vs. a data lake when they have a massive amount of data from operational systems that needs to be readily available for analysis. Data warehouses often serve as the single source of truth because these platforms store historical data that has been cleansed and categorized.
While data warehouses retain massive amounts of data from operational systems, a data lake stores data from more sources. A data lake platform is essentially a collection of various raw data assets that come from an organization's operational systems and other sources, often including both internal and external ones.
Because the data within data lakes may be uncurated and can originate from sources outside of the company's operational systems, it isn't a good fit for the average business analytics user; rather, data lakes are the playground of data scientists and other data analytics experts.
To remember the difference between a data warehouse and data lake, picture actual warehouses and lakes: Warehouses store curated goods from specific sources, whereas a lake is fed from rivers, streams and other unfiltered sources of water.
Data warehouse vendors include AWS, Cloudera, IBM, Google, Microsoft, Oracle, Teradata, SAP, SnapLogic and Snowflake, to name some of the many options. Data lakes are available from AWS, Google, Informatica, Microsoft, Teradata and other data management providers.
Which is right for me?
Deciding on a data warehouse vs. a data lake depends mostly on how you plan to use your data.
Because data warehouses use historical data that has already been processed and is ready to be used for analytics, it is well-suited for employees with less technical knowledge to use for analysis. Not only is it easier for business and data analysts to input data into BI and analytics tools, the design of data warehouses makes it easy for different teams and departments to access the data from the repository. This is why data warehouse architecture is key to breaking down data siloes across enterprise teams.
A data lake approach is popular for organizations that ingest vast amounts of data in a constant stream from high-volume sources. Ingestion to a data lake is relatively uncomplicated because it can store raw data. The unstructured data is more difficult to navigate than the processed data of a data warehouse. Data lakes are typically accessed by data scientists for deep analysis and predictive modeling because of the flexibility available to configure different models and queries. This flexibility also makes data lakes popular for enterprises that have data on hand for future analysis.
There are also some cases where combining a data lake and data warehouse may be best. Enterprises may have data scientists explore the potential of elements in a data lake for changing marketing strategies and to improve industry-specific services and products for future progress.
