What is Data Lake? It is a centralized repository that allows you to store large amounts of data in their native format, coming from many diverse and non-homogeneous sources. What is it in detail? What are the differences from Data Warehouse and what are the advantages? How does Big Data Analytics affect you?

Definition of Data Lake

Data Lake’s best definition is a place for storing, analyzing and correlating structured and unstructured data (from CRM data to social media posts, from ERP data to production machine info), in native format. Its distinctiveness is to allow the recovery and organization of the data according to the type of analysis to be performed.

This new feature, compared to traditional Big Data Analytics systems, represents a simplification and a significant enhancement of the tool. The Data Warehouse is, in fact, a method that requires the modeling of data before it can be stored, thus not allowing to fully exploit its value.

What are the differences between Data Lake and Data Warehouse?

A closer look at the differences in Data Lake and Data Warehouse features can only help us better understand the nature of the so-called “Data Lake”.

• Data collection. Unlike the Data Warehouse, Data Lake does not require an ex-ante structuring of the data. Indeed, its strength lies in its capacity to receive structured, semi-structured and unstructured data.
• Data processing. In the Data Warehouse, the database structure is defined as a priority, the data is written in the predefined structure and then read in the desired format (Schema-on-write). In Data Lake, instead, they are acquired in the native format and each element receives an identifier and a set of metadata supplied (Schema-on-read).
• Agility and Flexibility. Being a highly structured repository, changing the structure of Data Warehouse can be very time-consuming. Data Lake, instead, allows you to easily configure and reconfigure models, queries and live apps and proceed to Data Analytics in a more flexible way.

What are the advantages of Data Lake?

The adoption of a Data Lake system represents a turning point for the company in terms of:

1. Considerable expansion of information to which one has access

This expansion is because of a potentially infinite set of data types. In fact, since the analysis question determines the selection of data from which to draw information, in Data Lake the search accesses all the available information, regardless of the source that generated it.

2. Unlimited ways to query data and the possibility of applying a wide variety of different tools to them

It is important to keep in mind that the advantages of this new methodology are actually realized through the use of advanced Modern BI software. Only these tools, among other things, are able to manage various types of data from different sources and provide a usable and shared Visual Analytics interface between users and can give maximum value to Data Lake potential. Our advice? Tableau Software.

3. Reduction of storage costs and infinite space

With a traditional system, it is necessary to anticipate all the uses of data that will be needed. But, as business needs change, the analysis requirements also change. In addition, different professionals in the company need different data sets. In Data Warehouse systems, increasing the volume and structure of the database is costly and takes a lot of time. With Data Lake, we avoid the problem of the database structure by its nature and we have infinite space available thanks to data storage methods on distributed file systems (HDFS in the cloud).

4. Reduction of data consolidation costs

Distributed file systems take Data Lake to a potentially infinite scale-out storage system for data consolidation.

5. Time-to-market reduction

Not having to deal with data expansion and consolidation projects, access to information is always immediate and real-time.

6. Sharing and democratizing access to information

Data Lake provides all the insights obtained. It makes them accessible to anyone with permissions through a unified view of the data within the organization.

Given the increasing variety and volume of data with which companies must approach, Data Lake is certainly an extremely powerful approach. This is truer considering the changes that increasingly bring companies to mobile, cloud-based applications and the Internet of Things (IoT).