Data lakes are a hot topic in the big data world, but what are they, and what benefits do they offer? A data lake is a system or repository of data stored in its natural or raw format, usually object blobs or files. There are many benefits of using a data lake. Keep reading to learn more about data lakes and how they can benefit your organization.
What is a data lake?
Before we explain the benefits of a data lake, let’s first understand the meaning of a data lake. A data lake is a storage repository that holds large volumes of raw data in its native format until it is needed. The raw data in a data lake can come from many different sources, including internal company systems, social media, and the Internet of Things (IoT). The raw data in the data lake can improve decision-making and business processes. The data in the data lake can also create new products and services.
Internal company systems can provide data on customer demographics, preferences, and buying habits. Social media data can understand customer sentiment and identify new marketing opportunities. Data from the IoT can include information on weather, traffic, and other real-time conditions that can be used to improve business processes. All of this data can be brought together in a data lake, where it can be analyzed to find trends and correlations.
What are the benefits of a data lake?
There are many benefits of a data lake. The first benefit is increased flexibility and agility. Because the data in a data lake is in its original format, it can be used. The second benefit is improved decision-making. By having all of the relevant data available in one place, decision-makers can get a complete picture of what is happening and make more informed decisions. The third benefit is reduced costs. A data lake eliminates the need to purchase or build multiple specialized systems to store different data types.
Another benefit is enhanced insights and analytics. The combination of big data technologies and self-service analytics makes it possible to gain insights into business operations that were not possible before. Lastly, a data lake can store different types of data. This includes both structured and unstructured data. Structured data is organized in tables and columns, while unstructured data is not contained in any specific format.
What should you do before installing a data lake?
Before installing the data lake, you should preprocess and cleanse your data before loading it into the lake. Preprocessing and cleansing your data ensures that the data is ready for analysis. You will also be able to get the most value from your data lake. The preprocessing and cleansing steps might include:
- Removing Duplicates. Remove duplicate records from your data set to reduce the size and improve the performance of your data lake. Removing duplicates can be done in several ways, such as using a unique key to identify each record or eliminating duplicate rows based on specific criteria.
- Filtering Data. Filter your data to remove irrelevant data and improve performance. Filtering data can be completed by identifying and removing columns that are not needed or by filtering data based on specific criteria.
- Normalizing Data. Normalize your data to ensure that all data is in the same format and has the same range of values. Normalizing data can be done using a standard algorithm or by applying a set of rules to all columns in your data set.
- Transforming Data. Transform your data to prepare it for analysis.
A data lake is a great way to store all of your data in one place so that you can access it quickly and analyze it to find trends. Data lakes can help you make better business decisions.