A Data Lake stores large amounts of raw and heterogeneous information. This “universal memory” makes it possible to better understand its environment by crossing a considerable amount of data. Key actions include which will gain relevance.
Distinguishing Data Lake from Data Warehouse
A “Data Lake” represents an essential link in the knowledge of the client and his sector of activity. In this sense, it provides functions complementary to those of a Data Warehouse which is a simple data warehouse organized by themes, time-stamped and structured. It is therefore perfectly suited to repetitive analysis.
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 it a form and a structure only when the time has come to exploit them.
The difference is:
- Data Lake has a flat architecture.
- Data Warehouse (data warehouse) scales the data.
Objective: To anticipate market developments
The volume of information stored is very important and the sources are multiple: logs of a website, logs of the production systems, receipts, orders, comments of Internet users, emails, telemetry (Internet of Things) preserved in their state within an unfrozen structure.
But storing a lot of data is not effective; it is necessary to extract value. Based on Business Intelligence and Big Data applications, Data scientists can predict more precisely the evolutions of the market on which their company is located.
The Data Lake/Big Data binomial can meet four major objectives:
- Optimize the marketing boost by personalizing the content;
- Anticipate in-store or online sales and refine its cross-channel strategy;
- Measure the contribution of the web to in-store activity;
- Reduce costs, especially those related to inventories, by improving processes.
By retaining unstructured data, this warehouse can reveal amazing results. Thanks to the Data Lake, it is possible to couple the internal data of the company with external information such as weather, pollution, traffic, the number of bikes circulating in Paris, etc. This powerful behaviour prediction tool allows the company to adapt its production lines and stocks.
It can also analyze data that has the greatest impact on productivity and profitability, such as manufacturing defects. For an industrialist, this method reduces waste while improving its production.
Object Storage to optimize Data Lake
A storage architecture is often complicated by the coexistence of several file systems, several proprietary technologies or several generations of hardware. The Object Storage simplifies the creation of a Data Lake by providing easily scalable storage systems.
Cost is an important parameter to take into account, especially in Data Lake systems where the objective is to store a maximum of data. Object Storage systems make it possible to easily increase the number of servers at low cost to manage petabytes of data.
Erasure Coding or replication technologies included in object storage systems provide better protection over RAID-based systems and therefore better fault tolerance.
Finally, the ability to interact with the API storage system allows system administrators to automate data management.
Almost twenty years after the emergence of this term, more and more companies have a Data Lake. This integration into the digital strategy is favoured mainly by lower storage costs and the maturity of Big Data tools.
It can be deployed either on an on-premise infrastructure, in the company data centre 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 incurring heavy investments.
The Cloud’s major advantage is that the company only pays for what it consumes, without any size or duration limitations.