Data Lake stores large amounts of raw and heterogeneous information. This “universal memory” allows to better understand its environment by crossing a considerable number of data. In the key, actions which will gain in relevance.
Distinguish Data Lake from Data Warehouse
A “Data Lake” represents an essential link in the knowledge of the customer and his sector of activity. In this sense, it brings complementary functions to those of a Data Warehouse which is a simple warehouse of data organized by themes, time-stamped and structured. It is therefore perfectly suited to repetitive analyzes.
Conversely, a Data Lake analyzes the data according to the needs expressed by a department of the company. It is indeed possible to load raw information and to give them a form and a structure only when it is time to exploit them.
Objective: Anticipate market developments
The volume of information stored is very important and the sources are multiple: logs of a website, logs of production systems, cash receipts, orders, comments from Internet users, emails, telemetry (Internet of Things) … They are preserved in their state within an unfixed structure.
But storing a lot of data is not efficient; you have to extract value! By leveraging Business Intelligence and Big Data applications, Data Scientists can more accurately predict the market conditions on which their business is located.
The Data Lake / Big Data binomial addresses four major objectives:
optimize the marketing stimulus by personalizing the content;
anticipate sales in stores or online and refine its cross-country strategy;
measure the contribution of the web to store activity;
reduce costs, especially those related to inventory, by improving processes.
By keeping unstructured data, this warehouse can reveal amazing results. Thanks to Data Lake, it is possible to link the internal data of the company with external information such as weather, pollution, traffic, the number of bicycles circulating in Paris, etc. This powerful behavior prediction tool allows the company to adapt its production lines and stocks.
It also analyzes the data that has the greatest impact on productivity and profitability, such as manufacturing defects. For an industrialist, this method reduces the rejects while improving its product.
Object storage optimizes the Datalake
Object Storage simplifies the creation of a Datalake by providing easily scalable storage systems.
Cost is an important parameter to take into account, especially in Datalake systems where the goal is to store a maximum of data. Object storage systems make it easy to leverage servers at the lower cost to manage petabytes.
Erasure Coding or replication technologies included in object storage systems provide better protection against RAID-based systems and therefore better fault tolerance.
Finally, the ability to interact with the storage system by API allows system administrators to automate data management.
Almost twenty years after the appearance of this term, more and more companies have a Data Lake. This integration into the digital strategy is mainly driven by lower storage costs and the maturity of Big Data tools.
It can be deployed either on an on-premise infrastructure, that is, in the company’s data center, or in the cloud (hybrid mode). The latter makes it possible in particular to adapt the infrastructure and the analytical capacities according to its needs, without making heavy investments.
The major advantage of the cloud: the company pays only what it consumes, without limits of size or duration.
And you, how is your datalake?