The term Data Lake comes across by James Dixon, the chief technology officer, which means the ad hoc nature of data. He argued that data marts have inherent data silos problems that the data lake could end while promoting data lake. The term data lake is often deprecating as a marketing label for those products that support-oriented object storage-Hadoop.
A data lake is a system or a network where a considerable amount of raw data from many sources is stored in a raw or granular, or native format. It flexibly stores data for future use as it includes data in semi-structured or unstructured data or binary data. When held, a data lake is associated with the metadata tags, which helped in faster retrieval. A data lake is established within the business or organization’s data center or by using the Google and Amazon cloud services. These are generally composed of a bundle or clump of scalable and economic commodity hardware. It is essential for all those companies or organization which warns to take full advantage of their data.
Some examples of a data lake are that many companies like Amazon S3 use cloud storage or a distributed file system.
Difference between data warehouse and data lake:
Data lakes and data warehouses are much different from their database, and data warehouse is both the various strategies to store data. The difference between them could easily be understood as that lake is liquid, mostly unstructured, and created or fed or dependent on the other unfiltered water sources like rivers and streams. On the other hand, the warehouse is created by humans. It has shelves destined places for the things inside it, and it is prestructured while data lakes are not.
A data warehouse allows the strategic use of data. It collects data from internal and external sources, and then it optimizes it for business purposes. In a data warehouse, the schema of data is preset, which means a plan for data into the database upon its entry. It handles only structured data and has a preplanned schema, whereas it is not necessary for the data lake and can house structured and unstructured data. Even it does not have a preplanned schema for the data it houses.
Data lakes are less secure than the data warehouses as data warehouses existed for a more extended period, and therefore security methods have time to get matured.
Data lakes generally receive relational and nonrelational data from mobile apps and social media. Data warehouse receives from on; line transactions processing applications to support business sales and inventory teams.
A data lake is typically used when an organization needs a repository of data and can afford to apply schema on its access. In contrast, data warehouses are more useful when a massive amount of data needs to be readily available from the operational systems.
A data lake is highly agile, while data warehouses are less so.
The data lake isn’t a single or specific technology. There are many more technologies that delegate them, and some of the traders that offer these technologies are Amazon that offers AmazonS3 with limitless accessibility; podium which offers easy and suite management features, Oracle which offers Big Data Cloud, Apache that offers Hadoop-the open source ecosystem which is one the most used data lake services.
ADVANTAGES OF DATA LAKE:
It is cheap and stores raw data for low cost to implement as the technologies used to monitor the data lake are installed on low-cost hardware and are all open sources like Hadoop.
-There is no inherent data structure in the data lake, which means that any user can easily access the data lake data.
-It is flexible as it allows the data to be in its native format
-It supports the various level of investment users as access is possible for all the users.
-The data lake is scalable as it lacks the structure, and it is highly agile, too, which allows different types of methods to interpret data, SQL queries, etc.
Why data lake projects fail?
Data lake projects fail because the data lake might turn into a data graveyard as if any organization, which due to some reasons, practices poor data management, can lose data track in the lake no matter how much it is poured in. The other drawback is that the data lake may not provide accessibility in practical use to the organization; hence it is essential to maximize the investment and reduce the risk of failed deployment in the data lake.