Data Lakes are used by businesses of all sizes and industries to understand their data better. While the term may be new, the concept is not. Data lakes have been around for many years.

Data lake technology has evolved, but it all comes down to one key concept: a data lake stores, manages and processes massive amounts of unstructured data stored in an online system.

A data lake is a collection of structured and unstructured data, which can be used for analysis, trending, or real-time decision-making. It's a repository of all the potentially useful information that may be collected from our business systems.

The term "data lake" is often used interchangeably with "big data" or "cloud-based big data," but there are some differences between these terms:

• Big Data: Big data refers to the growing size and volume of digital information generated by consumer devices, social media and other sources. It also includes collecting, analysing and storing this information to derive insights that help businesses make better decisions about their operations.
• Cloud-based Big Data: Cloud-based big data refers to the use of cloud computing resources to help run large-scale applications such as Hadoop clusters and other systems designed to process large amounts of unstructured and semi-structured data quickly.
  

Data lakes often contain several subsets of different types of data:

• Raw data: This is unprocessed, unstructured data that has not yet been analysed or cleaned up. This may include files on disk, sensors and devices throughout the company, social media posts, etc.
• Analyzed data: Raw data may be cleaned up to make it ready for analysis. For example, suppose you have an image file with a lot of noise in the background or graininess in the foreground. In that case, you might want to distinguish between those two things before considering whether it's worth further processing for actionable insights.
• Consolidated results: Sometimes raw data does not include enough information for accurate analysis; it doesn't provide enough context, or context isn't easily accessible; or there are too many variables to consider when analysing it at scale e.g., too many pixels per pixel.

In these cases, consolidated results can help reduce time and cost while still providing valuable insights into decisions being made by your business.

Data lakes are the next step in data management. In a data lake, you have all your data in one place, accessible by anyone. You can analyse the data any time, whether a month ago or tomorrow.

Data lakes provide many benefits for businesses seeking to gain new insights from their data sets:

• Make better use of existing resources: Managing large amounts of data can be time-consuming and expensive. The cost savings associated with using a data lake instead of traditional file storage could make it worthwhile for organisations to invest in one.
• Improve decision-making capabilities: An organisation's ability to make decisions based on its knowledge is crucial for its success. The more information an organisation has about its customers, products or services, the better it can serve and provide them with value. A data lake allows access to all information that can help an organisation make better decisions faster than ever.
• Business intelligence: Through analysis of this information, companies gain access to valuable insights that can help them make better business decisions.
• Big data analytics: Data lakes can also be used for big data analytics projects, allowing organisations to analyse large amounts of data quickly and easily.