Data lake: definition, operation and interest

Data lake: definition, operation and interest

the data lake is a system for storing data. Appeared in 2010 in a context of big data, this storage medium is intended to resolve the limitations of other systems, in particular the phenomenon of data silotage.

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Data lake for data lake: the lake is a natural space supplied by various sources, the individual has free access to it to take samples to be analyzed; the data lake, in the same way, is supplied by various sources, raw data is collected there for use. It is to optimize the management of their data that companies consider the data lake when choosing their storage system, and particularly when it comes to taking advantage of opportunities linked to the “Internet of Things” and the Internet. machine learning.

What is a data lake?

The data lake is a fast and large storage space for heterogeneous company data. The system is distinguished by the following features:

  • The data is stored in its native format, without preprocessing: the system thus stores data in all formats. Structured data, semi-structured data and unstructured data coexist in the storage space, which centralizes raw data as well as transformed data.
  • The storage is for an indefinite period, and for indefinite use: the company stores information there systematically, regardless of its usefulness.
  • The data lake does not categorize information, the system centralizes all data in one place.
  • This means of storage allows the massive collection of a considerable volume of information.
  • The data lake is an inexpensive solution in the era of big data.
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Like any storage system, a data lake keeps information for later analysis and exploitation. The main difference between the data lake system and other systems is its capacity and performance: the company quickly collects a large volume of information. A very useful process for developing “Internet of Things” applications, and for exploiting machine learning technology.

How does a data lake work?

The operation of the data lake is similar to the operation of any other storage system, with the difference that the user does not need to process the data beforehand to conform to an imposed format. E-mails, video files or even CSV: so many of the company’s digital assets are found together immersed in the lake of data.

Concretely :

  1. A data lake is implemented in a company, on site or in the cloud. Most systems are based on Hadoop technology. Most companies prefer cloud solutions, such as Microsoft Azure or Amazon S3 among other examples.
  2. The data lake system operates on the basis of the read scheme. The data is imported by the user in its original format, no processing is carried out on the incoming flows.
  3. The data is accessible from the data lake: the user searches for information there for processing and analysis. As a rule, the tasks are entrusted to a data scientist. Indeed, the exploration of a data lake requires advanced expertise, since the formats are not standardized and the system is not optimized for SQL queries.

What are the advantages and disadvantages of data lakes?

When they first appeared, data lakes were very popular with businesses. The advantages of this type of storage solution are indeed very attractive. Be careful, however, to know the limits of the system, which is not necessarily the best suited to all uses.

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The advantages of data lakes

  • Flexibility: Because data lakes store data as is, this system is the most flexible. No pre-treatment process necessary, information is stored in all formats, regardless of its source.
  • Agility: the system is fast. Since the user does not need to prepare the data before storage, the company gains in agility and saves time.
  • Price: the storage cost is reduced compared to other systems. The company thus offers itself a cheap solution, all the less expensive as the data lake makes it possible to keep a colossal volume of data.
  • Completeness: the company stores all of its data in a data lake for an indefinite period. This makes it possible to have an exhaustive history over a long period, to make optimal use of all the information collected. Completeness further mitigates the risk of data silos.
  • Capacity: The massive and scalable capacity of data lakes is adapted in the context of increasing data volume. The company has flexible storage space, and thus rationalizes its costs.

The disadvantages of data lakes

  • Data lakes store all data, not just the data you need. The risk of clutter is great when an inordinate amount of various information gravitates in the storage space, the company must be careful not to lose control of the information. The term “data swamp”, for swamp of data, illustrates the degradation of the system, which is found to be abandoned because the data has become inaccessible, without any value.
  • Although the storage capacity is considerable, it seems absurd to store unnecessary data, a strong temptation for the use of data lakes. Collecting and storing too much information is also risky in the GDPR context: regulations gradually limit data processing; a company that gets lost in a massive volume of information can find itself in violation of the law, unwittingly.
  • Finding, processing and analyzing raw data is time consuming. The company must call on experts to use its data stored in a data lake, data that is neither processed nor prioritized upstream.
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What is the difference between data lake and datawarehouse?

Big data requires companies to have a data storage system. Several systems to choose from, including the data lake and the datawarehouse. How to arbitrate?

The data warehouse, also called a data warehouse, is also a data storage system. Unlike the data lake system, the data warehouse uses the write-on scheme: the data is processed before being stored. A piece of data which is also stored for a specific purpose. Information in the company is better organized, and therefore easier to use. One nuance, however: the preprocessing makes the data warehouse a specialized database, intended for a limited number of employees. Result: the risk of under-exploiting the data is proven. Other differences between data lake and datawarehouse: the price, lower with a data lake type solution; storage capacity, greater in a data lake.

In any case, each system has advantages and limitations. This is why it is often necessary for the company to implement the 2 solutions in a complementary manner, to cover all of its needs and resolve the respective issues of the data lake and the datawarehouse.

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