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This type of repository has various benefits over traditional data storage techniques. Let’s take a look at what data lakes can do.
Data lakes offer many benefits to help organizations utilize their data assets better and improve their decision-making process.The way in which an organization uses a data lake also depends on the types of business insights it hopes to gain.
Bottomline: as organizations increasingly seek to gain insights from all their data, data lakes will become essential to their overall big data strategy.
In the past, compute and storage resources were combined on the same machines. This was due to the prevalence of on-premises systems and the nature of the Hadoop Distributed File System (HDFS). In contrast, data lakes enabled the separation of compute and storage, ensuring that each resource can be scaled as needed. This is often one of the main ways that data lakes reduce cost.
Data lakes are designed to be queryable, meaning that they can be easily analyzed using a variety of tools such as Hadoop, Trino, and Spark. This makes them ideal for extracting insights from large data sets. In addition, data lakes can be used for a variety of purposes, such as predictive analytics, machine learning, and data visualization.
Data lakes are a cost-effective type of storage for large amounts of data from various sources. Data lakes typically allow data of any structure, which reduces cost because data is more flexible and scalable as it doesn’t need to fit a specific schema.
Data lakes are typically both large and inexpensive. Because of this, they are well suited to the rapid increase in data volumes seen in recent years. In fact, they are often the most affordable data storage option, typically costing far less than data warehouses.
Data lakes are designed to store data from multiple sources and multiple data structures in the same repository. This includes structured, semi-structured, and unstructured data. Such a diverse approach would not be possible in a data warehouse.
To navigate different data structures, data lakes typically deploy intelligent search and retrieval systems like Starburst. This helps ensure that you can find the information you need, regardless of the original structure of the data involved.
Data lakes can handle large volumes of data without compromising performance. This is particularly important as organizations build large, expanding data repositories and need a reliable system capable of matching the growing size of their data. A data lake creates more options for expansion and helps ensure that a solution put in place today is still suitable in the future.
Data lakes are designed to be platform independent. This means that all data types can easily be analyzed together in the same data lake. This critical distinction makes data lakes ideal for business analysts as they extract insights from large, varied data sets.
New sources can be added and new data types incorporated at a later date, allowing organizations to harness all of their data for real-world insights. This versatility has traditionally driven the data lake’s adoption in cases where multiple sources and data structures are either required or an unavoidable by-product of the systems in question. This contrasts with data warehouse solutions which are much less flexible.
Data lakes store and process data differently from other database technologies. While data warehouses require all of the data to be structured according to a predefined schema before it is added to the system, data lakes store data in a raw format.
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