A Data Lake, The Magic Data Source For Your Big Data Strategy

Big Data Strategy: They are everywhere and, when used well, offer incredible insights and even materialize in profit for companies. But precisely because they are scattered around, their use is challenging.

To become required in this market and help organizations deal with Big Data, the technology professional must know how to implement well-known solutions in the IT area. One is the data lake, which you will learn about in detail in the following paragraphs.

What Is A Data Lake?

A data lake is a non-relational database. It is a repository that does not require initial structuring of data, which can be stored in its original format. Here it is important to remember some concepts about the information available on the web.

The data lake can store all three types of data, which are classified as:

• Structured data: Formatted and organized in relational schemes, following specific parameters. Prime examples in this category are Excel, CSV, SQL, and JSON files.
• Semi-structured data: the information has already been organized in some way, but it still needs to be fully structured. HTML, XML and OWL files are three examples of the category.
• Unstructured data: Information is not organized or has an explicit internal hierarchy. The category encompasses most of the data available on the internet, such as text files, images, videos and data from social networks.

The data that make up a data lake also goes through the ETL process, one of the most used in integrating digital information. ETL is an acronym, and each letter represents a step in the process:

• Extract: a collection of data from different systems to be taken to the staging area. In this temporary space, they will be converted into the same format.
• Transform: Data is organized according to the needs of the business. They are structured to be stored in an ideal space at this stage.
• Load: the already structured data is sent to a specific repository, which is available for internal consultation.

Data does not go through the transformation step (T) to integrate a data lake, jumping from step E to L. This allows the repository to store a massive volume of data of any type and at any scale.

For this reason, it is also customary to define a data lake as a repository that stores a large volume of raw data in native format. This definition is inspired by the idea of ​​a lake, a metaphor first used in 2010 by James Dixon, CTO of Pentaho. He coined the phrase “data lake” when referring to the challenges of collecting, using, and storing data.

Data lakes are generally managed by data scientists, who design the structure’s architecture and integrate it into the general flow of data. The professional is also responsible for curating the stored information.

The Benefits Of A Data Lake

• Data storage in full;
• Economic and dynamic data management;
• On-demand processing, scalable. Data is transformed only when necessary, a process called “read schema”;
• Greater flexibility in the use of data, as they have not yet been structured in pre-defined schemes;
• Ease of using data in automating processes and creating deep learning algorithms.

Data Lake vs. Data Warehouse: What’s The Difference?

The main difference between a data lake and a data warehouse is the type of data contained in each. While the data lake allows for storing the three categories of data, the data warehouse is intended for structured data.

As the name implies, data warehouses serve as “data warehouses.” Information is classified into semantic blocks, called relations, to provide reports. Unlike the data lake, they are a relational database, generally used by Big Data and Business Intelligence analysts.

Another critical difference between a data lake and a data warehouse is the size available for storage. The first requires a larger space, often in terabytes and petabytes, since it has the purpose of storing all kinds of data. The second can be smaller, as it has the objective of storing only relevant data for analysis.

The data lake and warehouse can use on-premise, cloud, or hybrid storage models. The cloud has become increasingly popular due to its flexibility and ease of access to information.

A company cannot choose between a data lake and a data warehouse. It can maintain both types of databases, depending on its business objectives and its Big Data strategy.

How To Make A Data Lake Architecture

The data lake architecture design is simple, involving native data collection and storage. However, its planning must involve different company sectors, not just the IT area, as different sectors will access the information.

The most common data lake tool is Hadoop, an open-source software structure focused on data storage, but many are on the market. The choice will depend on the objectives, the technology team and how much the company plans to invest in data lake architecture.

The main steps that must be foreseen in the data lake architecture project are:

Landing Zone

The first step is to create a virtual data capture environment, which must be detached from the company’s central IT systems. There, the information is stored in its raw state.

Data Science Environment

Data scientists, who perform experiments and tests, access the virtual environment. At this stage, the IT team makes sure that the data lake meets the company’s demands.

Offload For Data Warehouses

The data in the data lake is integrated into the company’s data warehouses. They can be structured at different stages of the process.

A Critical Component Of Data Operations

The data lake can replace small-scale data repositories, which are part of the company’s data warehouse. This allows the creation of data scanning systems to extract information as if it were an internal search engine.

Remember that data lakes require ongoing governance and maintenance so that the sheer volume of stored information does not become a data swamp. This “data swamp” refers to lakes that have become inaccessible, cumbersome, expensive, and useless.

Also Read: How Big Data Can Contribute To Project Management