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Data lake vs data warehouse: state of data storage and management, six key differences, and features

Data has become extremely important for every company aiming to achieve digital transformation. However, one of the challenges they face in this path of digital transformation is storing big data. The most common solutions are data lakes and data warehouses but they are not interchangeable terms.

Even though these two types of data storage are often confused, they are much more different than they are alike. The only similarity between a data lake and a data warehouse is their high-level purpose of storing data. The distinction between the two is important since they serve different purposes. Here is a look at data lake, data warehouse, and how they differ from each other.

What is a data lake and a data warehouse?

A data lake is a repository capable of storing all of your organisation’s data, including the structured and unstructured data. In other words, a vast pool of raw data stored with no purpose defined yet will result in a data lake. A data lake can handle huge volumes of data that most organisations produce without the need to structure it first. The data stored in a data lake is used to build data pipelines necessary for data analytics tools.

Data warehouse, on the other hand, is a repository for structured, filtered data that has already been processed for a specific purpose. A data warehouse is designed specifically to support business intelligence and analytics needs of an organisation. Data from a warehouse is used to support historical analysis and reporting to inform decision making across an organisation’s lines of business.

An emerging data management architecture combines the flexibility of a data lake with the data management capabilities of a data warehouse. Called data lakehouse, it is an all in one data platform that is beneficial to data scientists, thanks to its support for machine learning and business intelligence.

What is the benefit of a data lake?

A data lake hosts a large volume of data that is not structured before being stored. This kind of data management can assist skilled data scientists to gain access to a broader range of data far faster than in a data warehouse. Some of the benefits are below:

  1. Massive volumes of structured and unstructured data like ERP transactions and call logs can be stored cost effectively.
  2. The data stored in a data lake is available for use faster since it is kept in a raw state.
  3. A data lake allows for a broader range of data to be analysed in new ways to gain previously unavailable insights.

What is the benefit of a data warehouse?

A data warehouse offers huge benefits to organisations, especially to those working in business intelligence and analytics. Data is stored in a data warehouse only after it is cleansed and processed. In other words, data stored in a data warehouse can be considered as a consistent “single source of truth,” which is invaluable to business data analysis, collaboration, and better insights.

Here are the three major advantages of a data warehouse include:

  1. Since no data prep is needed, a data warehouse is easier for data analysts and business users to access and analyse this data.
  2. A data warehouse is home to accurate, complete data and this data is more easily available. This makes it easier for businesses to turn information into insight faster.
  3. A data warehouse allows those working with data to build trust in data insights and decision making across business lines.

Data Lake vs Data Warehouse: Key differences

Most organisations use both a data lake and a data warehouse to support their data storage needs. Here is a look at six key differences between a data lake and a data warehouse.

  • Data storage: A data lake contains an organisation’s data in a raw, unstructured form, able to store data indefinitely. A data warehouse contains structured data that is cleansed and processed, ready for strategic warehouses.
  • Users: Data from a data lake has been found to be used by data scientists and engineers looking to study data in its raw form. Data from a data warehouse is typically accessed by managers and business-end users looking to gain insights that will help their business KPIs.
  • Analysis: Data lake supports predictive analytics, machine learning, data visualisation, BI, and big data analytics. A data warehouse supports data visualisation, BI, and data analytics.
  • Schema: The schema of a data lake is defined after the data is stored, which makes the process of capturing and storing the data faster. In a data warehouse, the schema is defined before the data is stored and this lengthens the time it takes to process the data.
  • Extract, Load, Transform (ELT): For a data lake, the processing is done by extracting the data from its source for storage and structured only when needed. The ELT sees data extracted from its source (s), scrubbed, then structured before it is stored in the data warehouse.
  • Cost: The biggest advantage of a data lake over a data warehouse is that it is fairly inexpensive. Data lakes are also less time-consuming and hence have a lower operating cost. Data warehouses cost more than data lakes and also require more time to manage, which results in additional operating costs.
2048 1152 Editorial Staff
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