The rise of Big Data and connected objects has led to a sharp increase in the volume of data worldwide. This is creating new capacity requirements for companies. It is therefore imperative for companies to use new methods to store and analyse data efficiently. Here is what you need to know about data storage and the different methods and tools.
Data storage: what is it?
Data warehousing is the set of methods and technologies that help store and preserve digital data. These methods take into account all media. Examples of file storage media include floppy disks, flash or USB media, hard disks or SSDs. However, the cloud is increasingly favoured by companies because of the security of data storage.
These media can be used by individuals to store files such as photos, documents or videos. They can also be used by companies to collect and generate huge volumes of data. However, with the development of connected objects and Big Data, companies are increasingly using artificial intelligence and machine learning. This allows them to collect, store and analyse data.
High density scalable computing systems have been created using real-time database analysis to store data. These are converged infrastructures and backup platforms. These are one of the best ways to store data securely on company servers.
Data storage training
In order to master the methods and tools of data storage, Jedha offers training courses tailored to the needs of individuals and companies. The courses offered include Data Scientist, Data Analyst, Data Engineer and Cybersecurity. The courses offered by Jedha help learners to better understand the concept of data warehousing. This solution is a great asset for a company undergoing a digital transformation.
Data storage methods
Between the Data Lake, the Data Warehouse and the Data ManagementThere are several methods and tools for storing data.
A data lake is a repository for data. Data Lake refers to a data repository. It offers the possibility of storing a very large quantity of raw or highly refined data for a given period of time. The data lake is one of the backup solutions that facilitate the cohabitation of structural forms of data and different schemas. In other words, this storage method promotes cohabitation between copies of the source system data, the raw data and the transformed data.
In short, all the data of a company can be stored in a single data lake. The stored data is then used for reporting or data analysis for Machine Learning. The data lake contains structured data such as logs, CSVs, JSONs and XMLs. It also includes structured files that come mainly from relational databases.
Among the information that can be stored on a data lake are unstructured data such as documents, e-mails, PDFs, etc. Even binary data such as images, video files or audio files can be stored on the data lake.
Today, information is an important part of any business, regardless of the sector. Among other things, it helps them to make the right decisions to improve their marketing strategy, reduce their production costs or optimise processes. Data management is therefore an important element that offers the company the possibility to achieve its objectives.
In order to be used effectively by companies, data first needs to be properly organised. It is therefore essential to use storage and analysis solutions such as data management. This backup method encompasses all the processes, tools and techniques that ensure the consistency, quality and security of the data set in order to use it effectively.
It is a process that aims to store, but also to integrate, organise and maintain all the files collected or created by a company. Data Management is a broad combination of functions that aim to make corporate data accurate, consistent, available, secure and accessible. This method of storing and managing data helps to achieve many benefits, such as eliminating duplicate data and standardising its format.
Also known as Not Only SQL Database, the NoSQL database is a storage tool whose particularity is to be non-relational. This approach to database design and administration promotes the storage and analysis of Big Data. In other words, it is possible to store data in unstructured form on a NoSQL database, without following specific schemas. Moreover, the NoSQL database can also be used for real-time web applications.
In addition to not following the relational model, one of the specificities of the NoSQL database is that it does not present tables as fixed columns. It therefore does not require relational mapping or data normalisation. Another feature of NoSQL databases is the absence or flexibility of schemas.
There are four main NoSQL databases:
- the key/value pair database,
- the column-oriented database,
- the graph-oriented database,
- the document-oriented database.
However, not every NoSQL database is capable of solving every problem. The NoSQL database must therefore be chosen according to its use and the needs of the company.
Hadoop is an open source Java framework that is often used for Big Data storage and processing. Developed by Michael J. Cafarella and Doug Cutting, this framework uses the MapReduce programming model for fast storage and retrieval of data in its nodes. Hadoop has many advantages for users.
Indeed, the Hadoop open source Java framework is capable of storing large volumes of data. This is, among other things, due to the fact that Hadoop's core servers are built with very simple hardware configurations. This means that they can be easily scaled to keep up with changing data volumes. Hadoop is also appreciated for its speed in processing information.
In addition to these many advantages, the Hadoop data storage and processing system has several qualities. Among these is its incredible scalability and resilience. Indeed, unlike traditional systems that have limited storage capacity, Hadoop is scalable because it operates in a distributed environment. As far as its configuration is concerned, it can be extended by installing additional servers. At the same time, its storage capacity can be increased to several petabytes.
The Data WarehouseData Warehouse is a term used to describe databases. They offer the possibility to store and manage non-volatile structured historical data from one or more sources for exploratory analysis. In other words, the data warehouse is a relational database made up of a combination of technological components. This technological device is in fact made up of :
- a Cloud database (Amazon Redshift, Snowflake...),
- an ETL (Extract, Transform, Load) tool that helps manage data flows,
- a BI tool that enables data analysis.
These are the three building blocks that make up the architecture of a data warehouse. This technological device thus fulfils four main functions. Thanks to the ETL tool to which it is connected, the data warehouse offers the possibility of extracting files from all the data sources it deems useful. The Data Warehouse also cleans the data it integrates and then performs the necessary de-duplications and reformatting to organise the stored data in a structured and coherent way.
The other main function of a data warehouse is transformation. The ETL system performs the necessary transformations to adapt the data models to the target use cases of the data warehouse. Finally, the data stored in this technological device is continuously updated thanks to the data sources to which it is connected.
There are many other equally effective data storage methods and tools. The method a company chooses will depend primarily on its needs, but also on the amount of data to be stored. To get a better understanding of this concept, it is advisable to follow a training course offered by Jedha. With this module, data storage will no longer hold any secrets for the learners.