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CAREER PATH
Bootcamp (11 weeks)
Part-time (11 months)

DevOps Engineer Course

By attending this training course, you have the chance to earn a qualitative certification with ongoing training and career support in order to fulfill an increasing demand in the job market of the tech world!

OUR NEXT ENTRIES ARE:
02 April 2024
06 May 2024

Attend an officially certified AWS course:

  • Access 120+ hours of high-quality content available on our platform
  • Benefit from qualitative content being delivered by our dedicated in-house AWS-Authorized program instructors 
  • Be prepared to take the official AWS-exam to receive the certification yourself (exam fees included in the DataScientest course)

Training course content: a glimpse on our curriculum

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Application development with Python (35h)

  • Basic and advanced Python
  • FastAPI
  • Linux & Bash
  • Virtualization & Vagrant
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Linux system and agility (35h)

  • Linux system administration
  • NGINX
  • AGile project management
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Databases (40h)

  • SQL language
  • PostgreSQL
  • MongoDB
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CI/CD (55h)

  • Git/Github
  • Gitlab
  • Docker
  • Kubernetes
  • Jenkins
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AWS Cloud (70h)

  • AWS Cloud Practitioner
  • AWS Solutions Architect
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Automatisation (25h)

  • Terraform
  • Ansible
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Monitoring (30h)

  • Prometheus
  • Grafana
  • Datadog
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Cloud DevOps (60h)

  • Advanced cloud concepts

Educational Partnership: AWS – Amazon Cloud Services

As we are an official AWS partner, you will be able to gain the skills to fulfil the certification of the Solutions Architect Associate (SAA) – RS5611! This will give you a great benefit in the job market.

Professional Project: Put your learning into doing

Throughout your training course, you will do an individual project that requires around 100 hours of work.
You will be able to apply what you have learned in a project with the topic of your choice, with which our teachers will assist you throughout the course.
This method gives you the opportunity to create a first professional proof for your new career portfolio, while benefiting from applying your learned theory to a real-life problem!

Academic Partnership: Université Paris 1 – Panthéon Sorbonne

DatsScientest is an official partner of one of the most acknowledged universities in France and Europe, the Panthéon Sorbonne in Paris.
You are benefitting from being awarded with an additional official certification of this university once you finished your DevOps training course with us. 

This partnership does not only attest to the quality of our institution and our courses, but gives you a highly recognized academic recognition!

A hybrid learning format

By combining flexible individual learning on our self-designed platform with the attendance of masterclasses led by our in-house data scientists and professionals, you will benefit from a high quality training course. As of now, DataScientest has helped over 10,000 learners to emerge into the tech world, and there are many more in the coming. Become part of the movement, and attend our courses that have a completion rate of over 98%

Our method is based on a learning by doing approach:

  • Practical application: For each of the training modules you will receive access to online exercises and theoretical information, enabling you to apply any concepts you have learned about. 

 

  • Masterclasses: To support your individual learning journey, we organize supportive online classes led by experienced in-house instructors. During these classes, you can ask any theoretical questions that might have come up during your individual learning time, while our teachers will address different issues concerning upcoming technology developments, methods, and tools in the field.

A DevOps Engineer's missions

Automate

The DevOps Engineer is responsible for automating the deployment of a cloud infrastructure. This task refers to the processes and tools that reduce or eliminate manual efforts used to provision and manage cloud computing workloads and services.

Deploy

The task of continuous application deployment refers to a strategy in software deployment, where code changes to an application are released automatically into the production environment driven by a series of predefined tests. 

Supervise

The DevOps Engineer is responsible for not only being in charge of these changes, but supervising the different teams connected to certain processes and tasks. Therefore, your communication skills are asked for, on top of your technical expertise! 

Discover Learn, our learning platform

A user-friendly, comprehensive interface for a tailor-made learning experience. An enhanced platform and premium coaching.

How to finance the training?

Our goal is to make our courses affordable and open to everyone – regardless of one’s current situation. This means we do our best to offer as many options as possible.

Total course costs: 6.500 €

Accordion Content

If you live in France, you can benefit from several financing options:

  • CPF: If you have already worked in France, you may have accumulated a budget allocated for training, which allows you to finance your training via your CPF account
  • Personal financing: It is possible to spread out your payment in several instalments in order to finance your training.
  • Company financing: If you are an employee, you can have your training financed by your company.
  • Pôle Emploi: If you are a job seeker and registered with Pôle Emploi, it is possible to benefit from total or partial financing via Pôle Emploi.
  • Transitions Pro: Do you want to retrain while keeping your job? You can use the system via Transitions Pro.
  • Region: If you are registered with Pôle Emploi, you can also benefit from funding from your region! Several schemes exist that allow you to finance your training.


Don’t hesitate to make an appointment with one of our consultants to find the funding that best suits you!

If you are living in Germany you have multiple ways to finance your training courses depending on your professional situation.

Employees:

  • Funding from your employee: You can check with your employer to see if there is a possibility of having your training paid for (totally or partially paying for your training).
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

Unemployed, job seekers, self-employed or students:

  • Bildungsgutschein: If you are looking for work, threatened by unemployment, self-employed or even a student, you have a good chance of receiving an education voucher (Bildungsgutschein). Contact your advisor at the employment agency or the job center and check whether there is a possibility of funding your further education .
  • Self-financing: If you have no chance of receiving the education voucher, you can pay the remaining amount by bank transfer, direct debit or credit car.
  • Payment by installments: If you are unable to pay the entire amount at once, you may be interested in our installment plan (pay the costs over a period of up to 12 months).

The DataScientest team will help you find the best funding for your personal circumstances.

If you currently live in Spain, there are different types of financing that can be applied depending on your current situation:

  • Fundae : Thanks to our close links with companies and our high employment rate, you can subsidize our courses with Fundae.
  • Pledg : Finance our courses in up to 12 months.
  • Quotanda: Finance the course with Quotanda interest-free (+12 months).
  • Student Finance : You pay nothing until you find a job.

 

For further information, please check this page and book an appointment with our team.

Do you have any questions about the training or DataScientest as a company?

Do not hesitate to download our detailed program sheet to find out more about the programme, the company behind it, and more!

What our alumni say about our DataScientest training courses!

Patricia Jan, Data Scientist and alumni of DataScientest, tells you today in a video about her experience of further training and how data plays a role in her everyday life!

🎉 Would you also like to get started with one of our courses? New courses start every month! Just book a meeting with our team and get more information.

Do you have any questions? We have the answers!

DataScientest is eligible for the French Compte Professionnel de Formation or CPF and the German Bildungsgutschein of the Agentur für Arbeit. You can also finance your training independently by spreading your payments over 3, 6, 10, or 12 monthly instalments, either to cover the full training cost or the remainder of your governmental funding. The company may also be willing to pay for the training for you if you are looking to train while working for them.
Once you’ve made contact with the team via our website, we’ll contact you for an initial consultation conversation of what we can offer you based on your professional background and your personal wishes. The idea is to align our offer with your needs. An application form and a test will be used to assess your level. Once this stage has been completed, a member of the admissions team will contact you to discuss your results and validate your professional project, your motivations, and finally the relevance of your educational project. Once your project has been confirmed, you’ll go straight on to the enrolment phase with our teams. The registration deadline depends on the financing method you choose: Personal or company financing: you have until the day before the start date to register (subject to availability). Governmental financing: it might take a few days to receive the confirmation of funding during your application phase, therefore we suggest you apply a few weeks before the course start.

DataScientest is the only organization to offer hybrid online training. With that being said, 85% of the learning effort is performed on the company’s self-made, coached platform, and 15% masterclass sessions via videoconferencing, to combine flexibility and rigor without compromising on either.

Our training comes in 2 formats:

  • Bootcamp (11 weeks): full-time intensive format
  • Continuous (11 months): 10-12 hours per week, suitable for working learners;

Absolutely! We have a team of dedicated teachers who not only designed the program but are also available to offer comprehensive support. Whether you have theoretical or practical questions, they are here to assist you and provide the help you need.

You can receive assistance every day of the week, from 9:00 am to 5:00 pm. Our instructors take turns on a dedicated forum, ensuring personalized technical support for all learners. Additionally, we offer pedagogical support through the Slack communication network.

Furthermore, our teachers closely track your progress to ensure completion and commitment. If you happen to go offline for an extended period, your cohort manager will reach out to you promptly.

Our teams are committed to accommodating your constraints and assisting you in finalizing your application as efficiently as possible. We will make every effort to ensure that the deadlines are kept within one week. If you are highly motivated and confident in your project, you can even complete your registration within a day!

Accordion Content

DevOps is short for “development” and “operations”. It’s a collaborative approach to software development or IT operations. It’s a methodology based on communication and collaboration within an organization. It relies in particular on the use of an iterative software development method, on automation, and on the deployment of programmable infrastructures. To adopt this approach, an organization may have to change its entire software delivery chain, services, roles and IT tools. A DevOps environment is generally based on continuous integration (CI) and continuous deployment (CD) tools. Task automation is also a key component. Real-time monitoring and incident management systems and collaboration platforms are also widely used. The same applies to cloud computing, microservices and software container technologies. The aim of DevOps is to improve the software development cycle. The process consists of successive steps: plan, code, build, test, relax, deploy, operate, monitor. These steps are repeated in an infinite loop. The software is continually updated to meet expectations. Updates are tested in containers, then deployed in production. In the event of a problem, traceability ensures rapid intervention.

Software projects within a company often run into the same problems. Development takes too long and the software doesn’t live up to expectations. DevOps can remedy these problems, saving considerable time and ensuring that negative feedback is taken into account immediately. The communication between different IT teams is also improved. Since its creation in 2009 by Patrick Debois, DevOps has enjoyed a boom worldwide. Thanks to its many advantages, this methodology is being massively adopted by companies. DevOps is particularly useful for Data Science projects.

The DevOps Engineer has a dual role: he or she is a developer and a system administrator.

They work with developers and IT staff to oversee different versions of code, combining an understanding of engineering and coding. From creating and implementing new software to analyzing data to improve existing software, the DevOps Engineer aims to increase the productivity of his company. They deal with the software development lifecycle and are familiar with the various automation tools used to develop digital pipelines.

A DevOps Engineer, Machine Learning Engineer, Data Engineer or any other role using the DevOps method needs to have software development skills, but also operations skills such as infrastructure configuration.

To adopt this development method, you need to master Cloud Computing, software containers and continuous development/deployment technologies.

It is imperative to master code repository platforms such as GitHub and GitLab. These platforms enable multiple developers to collaborate on code, with the ability to revert to previous versions when needed.

This is a major component of a CI/CD pipeline, as validated code changes activate the next steps in the process, such as static code analysis or testing.

There are also CI/CD engines such as Jenkins, GitLab and CircleCI. These tools enable DevOps teams to validate and deliver applications to end-users in an automated way as development progresses.

Containers enable software to be tested in an isolated environment. They are very useful for working on code changes. The main containerization tools are Docker and Kubernetes.

Finally, Cloud environments are widely used for DevOps, particularly for scaling and deployment. AWS and Microsoft Azure are the most popular Cloud providers, and also offer CI/CD services.

The DevOps system administrator automates the deployment of infrastructures on a private, public or hybrid cloud.

When working for a software publisher or in an IT department with teams of developers, the DevOps system administrator deploys applications on a continuous basis.

They supervise the services deployed and deal with any alerts raised.

To automate the deployment of cloud infrastructures, the DevOps system administrator automates the creation of servers using scripts, configures and connects them together, and then uses an Ansible-type platform to configure and monitor deployment.

When in charge of deploying an application on a continuous basis, in conjunction with the development teams, he prepares test and pre-production environments.

The DevOps Engineer prepares the various data servers and associated storage, as well as the containers for the application. He oder she then migrates the data and deploys the application in the pre-production environment.

The DevOps Engineer is in constant contact with the development team to correct any malfunctions discovered during the various test phases. Using a Kubernetes-type platform, he deploys the application and its successive updates on the production environment.

The DevOps system administrator supervises the infrastructures and applications he has deployed. To do this, he defines the indicators to be monitored and installs and configures a supervision solution. When an anomaly is detected or an alert is raised, the problem is corrected or rectified.

In order to solve a configuration problem, understand the cause of a malfunction or install a new tool, he exchanges information on professional community forums, possibly in English.

Most technical documentation is written in English, so DevOps system administrators need to be able to read it to find the information they are looking for, and interpret the advice given correctly. This corresponds to level B2 of the European framework for reading comprehension.

He oder she will sometimes have to ask questions or provide answers on user forums in English; level B1 of the European framework for written expression is sufficient.

The DevOps Engineer also uses a logical approach to diagnose the cause of a malfunction and to remedy it, and keeps an active watch to keep his or her skills up to date.

This job requires mastery of a wide range of tools and languages, as well as an understanding of abstract concepts.

The DevOps system administrator is in contact with teams of developers, his or her technical manager, network and security teams, hosting solution providers, and professional communities for the tools he or she uses.

The DevOps system administrator works in a digital services company (ESN), a Cloud operator, a software publisher or in the IT department of a large company.

They work as part of a team, reporting to their company’s technical manager or IT director. In some cases, this activity is carried out entirely remotely.

A DevOps training course teaches you to master the various DevOps tools: expertise that is much in demand in the enterprise. In the face of digital transformation, software development projects have to meet increasingly stringent constraints. To meet the demands of end-users, companies are adopting the DevOps methodology for application development and data science. DevOps training is becoming increasingly important.

The DevOps methodology has come a long way since its inception, and continues to expand. New tools and technologies have emerged to meet demand. Companies are adopting this philosophy to stay competitive. In addition, the Covid pandemic triggered a surge in digital transformation and led many organizations to turn to DevOps. Many companies of all sizes and sectors are adopting DevOps worldwide. As a result, DevOps skills are in high demand. The job of DevOps Engineer is currently one of the most sought-after in IT. Organizations need experts to implement best practices.

To become a DevOps expert, opt for the DevOps training course from DataScientest.
The program covers the code repository platforms GitHub and GitLab, as well as the containerization tools Docker and Kubernetes.
At the end of the course, you’ll have all the skills you need to become a DevOps Engineer.
Our course can be taken as an intensive Bootcamp (11 weeks), or as a Continuing Education (11 months). It consists of 85% individual coaching on our Cloud platform, and 15% Masterclasses.

A DevOps engineer, Machine Learning engineer, Data Engineer or any other role using the DevOps method needs to have software development skills, but also operations skills such as infrastructure configuration.

To adopt this development method, you need to master Cloud Computing, software containers and continuous development/deployment technologies.

It is imperative to master code repository platforms such as GitHub and GitLab. These platforms enable multiple developers to collaborate on code, with the ability to revert to previous versions when needed.

This is a major component of a CI/CD pipeline, as validated code changes activate the next steps in the process, such as static code analysis or testing.

There are also CI/CD engines such as Jenkins, GitLab and CircleCI. These tools enable DevOps teams to validate and deliver applications to end-users in an automated way as development progresses.

Containers enable software to be tested in an isolated environment. They are very useful for working on code changes. The main containerization tools are Docker and Kubernetes.

Finally, Cloud environments are widely used for DevOps, particularly for scaling and deployment. AWS and Microsoft Azure are the most popular Cloud providers, and also offer CI/CD services.

To enroll in the DevOps training program, you need to have a diploma or RNCP qualification at Bac +2 level in IT (European level 5), or a diploma or RNCP qualification at Bac +3 level (level 6) in a scientific field. Applicants are also required to demonstrate an understanding of the Python language and Linux systems.

Applicants who do not have the required level of qualification may be granted an exemption based on their application and a written test.

To follow the course, learners must have a computer with an Internet connection and a webcam.

The training leads to the French Ministry of Labor’s level 6 professional title (Bac+¾ equivalent): “DevOps System Administrator”. The title is registered with the French RNCP under number RNCP36061. The title is made up of 3 blocks of skills to be acquired in order to obtain professional certification:
  • Automate the deployment of a cloud infrastructure
  • Continuous application deployment
  • Supervise deployed services

It is possible to validate one or more of the skill blocks. Each block can be acquired individually. See RNCP36061 for details.

The curriculum is based on sequences that are themselves divided into modules enabling you to master the skills deemed necessary for the DevOps System Administrator profession. For a total hourly volume of 350 hours of training and a project lasting an estimated 100 hours, 85% of training time is spent on a personalized coaching platform, while the remaining 15% is spent in masterclasses where an experienced teacher leads a course and answers all your questions. In addition to the platform and masterclasses, you’ll be working on a “red thread” project that will confirm the skills you’ve acquired and enable you to get straight to work.

Results are assessed by means of an evaluation procedure to determine whether the learner has acquired the skills required for the role of DevOps engineer.
There are two aspects assessed by the DataScientest teaching team: Practical work situations, including the development of a project lasting an estimated 100 hours and online case studies. The course leads to the level 6 (bac+¾) “DevOps System Administrator” RNCP36061 professional qualification. The assessments carried out during the training are therefore to be distinguished from the certifying assessments aimed at obtaining the title.
Once you’ve completed the DevOps System Administrator professional title, you can continue your training by aiming for an RNCP title or a level 7 diploma (bac +5 equivalent) in IT engineering, or you can aim for mastery of editorial tools. It all depends on you! Some examples of certifications :
  • Implement DevOps for AWS Cloud RS5849
  • Implement DevOps for Microsoft Azure Cloud RS5343

Validation of the assessments will enable you to obtain the “DevOps System Administrator” RNCP36061 professional qualification at bac +3 level (European level 6) (Click here to access the certification description).

The DevOps System Administrator professional qualification is divided into 3 skill blocks (or CCP)

  • Block 1 : Automate the deployment of a cloud infrastructure
  • Block 2: Continuously deploy an application
  • Block 3 : Supervise deployed services

According to Hays, a DevOps employee can earn between €520 per day (with less than 3 years of experience) and €760 per day (with more than 8 years of experience). For those starting their career, DevOps engineers can earn up to €3,000 gross per month. As they gain more experience (5 to 10 years), a seasoned DevOps engineer can earn over €60,000 per year, and after 15 years of experience, they can earn more than €75,000 per year (Source: Monde Informatique).

We are here to support you in your career path, with a singular goal in mind: to enhance your employability.
The sectors we primarily focus on include:
  • Digital services companies (ESNs)
  • Specialized ESNs offering Cloud hosting services (Cloud Providers)
  • Software publishers
  • IT departments of companies with dedicated IT development departments

The available job positions in these sectors are as follows:
  • DevOps Engineer
  • SysOps DevOps
  • DevOps Systems Engineer
  • Cloud Engineer
  • Cloud Developer
After analyzing comparable certifications, no equivalents to the professional title of “DevOps System Administrator” registered with France Compétences’s RNCP have been identified. However, there are several RSCH-registered certifications that can help develop skills in the DevOps field:
  • Implement DevOps for AWS Cloud (RS5849)
  • Implementing DevOps for the Microsoft Azure cloud (RS5343)
  • Use DevOps chain development tools (RS5043)
  • Apply the DevOps method to optimize the application lifecycle (RS5044)
  • Deploy DevOps infrastructure with microservices architecture (RS5363)
  • Use DevOps methods and tools in infrastructure administration (RS5234)

In terms of career progression, a DevOps System Administrator can transition into cybersecurity professions such as analyst or security coordinator. Additionally, they can explore the field of DevSecOps. Opportunities for advancement can include roles such as systems engineer, developer, or product owner at the p-1 level, and roles such as Cloud and DevOps manager, network and infrastructure engineer, or Chief Technical Officer (CTO) at the p+1 level.
On the first day of your training, you will be presented with a dedicated career services platform containing all the workshops essential to your job search. You can access it at any time, even after your course has finished. Mathilde and Morgane, our career managers, are dedicated to you throughout your training. You can make an individual appointment with one of them to support you and answer any questions you may have about your career plans.
In addition, career workshops are organized every month:
  • A workshop to help you write a good data-driven CV, which can also be used for LinkedIn
  • A workshop to help you strategize your job search with various topics on presentation, career change, salary negotiation and technical test practice. These topics are supplemented by other workshops to be defined according to individual needs. Please note that these events are mainly offered in French, and we are continuously working on growing this service for english-speakers as well.

In addition, concrete actions are put in place to support you in your job search: recruitment fairs organized by DataScientest with its partner companies, organization of Webinars with data expert speakers, communication actions to boost your visibility (CV Competition, DataDays, project articles published on the blog and external reference media). To find out more about DataScientest’s career support initiatives, click here.

Mathilde and Morgane, our career managers, will be available throughout the course to support you in your professional (re)integration project. You can schedule an individual appointment with either of them to receive personalized support and have your career-related questions answered.

Additionally, we organize monthly career workshops to provide you with the best advice for success, including workshops on CV and LinkedIn optimization, job search assistance, and mock interviews.

Furthermore, we have various initiatives in place to support your job search, such as recruitment fairs organized by DataScientest to introduce you to our partner companies, webinars with data experts, and communication initiatives to boost your visibility, including CV competitions, DataDays events, and the publication of project articles on our blog and other external reference media.

Newsletters produced by our Data Scientists are sent out regularly and are a reliable source of specialized Data Science information. DataScientest also organizes monthly webinars and Data Ateliers to help you improve your general knowledge of Data.

At the same time, the DataScientest community continues to grow, and with it all its alumni.

To keep in touch and enable alumni to communicate with each other, DataScientest has set up a group of alumni on LinkedIn who share and exchange views on various Data Science topics.

The DatAlumni community is a LinkedIn community of DataScientest alumni. On this page, questions, advice and technology news are shared for the benefit of all. You’ll be invited to join at the start of your training. Also part of the program: business opportunities, networking and events (afterworks, trade shows, Data Challenges…).

DataScientest alumni can also meet up on our Facebook group, where they can get in contact and help each other out.

At the same time, our program life department organizes monthly activities such as ice breakers, “Who Wants to Win a Million Dollars in Data”, afterwork events, etc., to further strengthen the bond between learners and alumni.

Initially, DataScientest helped companies with their Data transition. This enabled us to forge strong links with the major groups that have ensured the growth of our structure.

Building on our experience and these privileged relationships, we regularly organize recruitment fairs with our partner companies, aimed at all our students and alumni. Recent participants include: Mano Mano, OnePoint, JellySmack, Crédit Agricole, Little Big Code, Job Teaser, among many others…

Throughout the year, our careers department also relays offers from our partners via our promotional channels, where you can apply directly.

In order to compensate for your handicap and enable you to follow your training in the best possible conditions, we have set up a special program for disabled people. If you have any queries concerning your situation, please contact our disability advisor Mathilde Venchiarutti: mathilde.v@datascientest.com.

Read the testimonial of a disabled learner and her support by the DataScientest team on the webinar: “Disability & employment: seize the opportunity of a career in tech”.

Key Informations

A data lake is a platform for storing and analyzing data, with no constraints on type or structure. Find out all you need to know about this indispensable tool for Data Scientists: definition, operation, use cases, training...

A Data Lake is a storage repository that can hold vast amounts of structured, unstructured, or semi-structured data in its native format. Like a real lake, data flows into it from various sources in real-time.

This platform imposes no constraints in terms of file size or data category, allowing high-performance data analysis and native integration.

Different types of data analysis can be performed, such as Big Data processing, real-time analytics, Machine Learning, as well as creating dashboards and data visualizations.

Within the Data Lake, each data element receives a unique identifier and is associated with a set of metadata. The architecture is not hierarchical, unlike that of a Data Warehouse.

Why use a Data Lake?

A Data Lake enables the cost-effective storage of various types of data for later analysis, providing an initial overview for Data Scientists.

Data can be stored without a predefined model, regardless of their structure. The Data Lake delivers agility for organizations.

Artificial intelligence and Machine Learning allow for highly advanced predictive analysis. It is possible to analyze data from new sources such as log files, clickstreams, social media, or IoT devices.

With a Data Lake, a company can identify and seize opportunities. For example, it is possible to attract and retain new customers, increase productivity, perform predictive maintenance, or make better decisions.

By implementing it, the company gains a competitive advantage. According to a survey conducted by Aberdeen, companies that have implemented a Data Lake outperform similar organizations in terms of revenue growth by 9%.

Data Lake architecture and operation

Initially, data is ingested from various sources such as databases, web servers, or IoT devices using connectors. It can be loaded in batches or in real-time.

The storage provided by a Data Lake is scalable and allows for quick access for data exploration. Once the data is stored, it can be transformed into a structured form to facilitate analysis. Data can be tagged to associate metadata with it.

SQL or NoSQL queries, or even software like Excel, can then be used to analyze the data. Whenever a question arises within the company, it is possible to query the Data Lake by analyzing only a subset of relevant data. The Data Lake also enables data management and governance.

Advantages and disadvantages of the Data Lake

A Data Lake allows for storing and analyzing data while offering cost-effective flexibility. It enables extracting value from any type of data. The main strength of a Data Lake is the ability to centralize content from various sources. All users within a company can access it, even if they are geographically separated.

However, the Data Lake also has drawbacks. It is a platform that can be challenging to manage and may lose relevance over time. Storing unstructured data can quickly lead to chaos if not properly managed.

The use of such a platform can also be costly and pose cybersecurity risks if not designed methodically. Data stored without precautions can also lead to privacy or compliance issues.

Data Lake vs. Data Warehouse: what are the differences?

Data Lakes and Data Warehouses serve the purpose of storing and processing data but have significant differences.

One of the key distinctions of a Data Lake is its retention of all data. A Data Warehouse retains only data that can be used to answer specific queries or generate reports, simplifying storage and saving space.

On the other hand, a Data Lake retains all data, even if it’s not immediately useful. This is made possible by the hardware used, which is typically quite different from that of a Data Warehouse and more cost-effective.

Another difference is that a Data Lake supports all types of data without exception, regardless of their source and structure. Data is stored in its raw form and transformed when needed.

In contrast, Data Warehouses typically focus on data extracted from transactional systems, such as quantitative metrics and attributes describing them. Non-traditional sources like web server logs, sensor data, social media, text, and images are often ignored because they are expensive and challenging to store.

Data Lakes also offer greater adaptability to change. Developing and configuring a Data Warehouse can be time-consuming, and any changes may require significant time and resources.

This is not the case with Data Lakes, as all data is stored in its raw form. This allows for innovative data exploration and the automation of schemas if they prove to be relevant.

Lastly, Data Lakes tend to provide faster analysis results. Users can access all types of data before it has been transformed, cleaned, or structured.

The downside is that analyzing data on a Data Lake requires more technical skills. These platforms are not as accessible to non-technical business users as Data Warehouses, making them more suitable for Data Scientists.

Data warehouses in the cloud

Data Lakes can be deployed on-premises or in the cloud. Opting for cloud computing offers superior performance, scalability, and increased reliability.

Users can also benefit from various analytical engines. Security is enhanced, deployment is accelerated, and feature updates are more frequent. Costs are typically proportional to actual usage.

The importance of data lakes in business

Companies striving to align with Big Data are always looking for new ways to efficiently manage data. However, large datasets are not always easy to analyze. Adopting a Data Lakes approach can address these issues and help with other aspects of their business, such as improving customer relations, research and development activities, and operational efficiency.

To do this, a company can effectively implement Data Lakes by following these steps:

Understanding the benefits of data lakes

A data lake provides key features that will enable a company to discover new ways to enhance analysis and inform executive decision-making. A significant amount and variety of data need to be managed. Data governance is crucial to standardize information from various sources, ensure their accuracy and transparency, and prevent misuse.

Harnessing data lakes for business intelligence

La Business Intelligence est une approche efficace qui permet aux experts dans une entreprise d’utiliser des méthodologies avancées pour travailler avec de grands volumes de données brutes. Cela permet d’obtenir des informations pertinentes qui peuvent améliorer la prise de décision et faire découvrir de nouvelles opportunités de croissance.

Un lac de données peut améliorer une solution de BI en offrant un plus grand potentiel de traitement des données. Il peut servir de source de données centralisée pour construire un Data Warehouse et fonctionner comme une source directe de données pour la BI.

Les lacs de données ont des applications dans la science des données et l’ingénierie de l’apprentissage automatique où les ensembles de données massives constituent l’épine dorsale des solutions techniques.

Ajouter une structure​

Pour donner un sens aux grandes quantités de données non structurées stockées dans un Data Lake, une entreprise doit créer une certaine structure comme les métadonnées d’un fichier, le comptage des mots, etc. Un lac de données offre une plateforme unique où l’entreprise à la possibilité d’appliquer une structure sur une variété d’ensembles de données, ce qui lui permet de traiter les données combinées dans des scénarios analytiques avancés.

Comment se former à utiliser un Data Lake

Un Data Lake constitue un précieux atout pour les entreprises de tous les secteurs. Par conséquent, apprendre à maîtriser cet outil permet de trouver facilement du travail dans n’importe quelle industrie.

Pour devenir expert en la matière, vous pouvez vous tourner vers les formations DataScientest. Le Data Lake est incontournable en Data Science, et vous apprendrez donc à l’utiliser à travers nos différents cursus : Data Scientist, Data Engineer, Data Analyst, Data Management ou encore Machine Learning Engineer.

Toutes nos formations proposent une approche innovante de Blended Learning à mi-chemin entre présentiel et distanciel, et peuvent être effectuées en BootCamp intensif ou en Formation Continue. À l’issue du parcours, les apprenants reçoivent un diplôme certifié par l’Université de la Sorbonne.

Vous savez tout sur le Data Lake. Découvrez notre dossier complet sur les bases de données, et notre introduction à la Data Science.

The Job
Accordion Content

DevOps is short for “development” and “operations”. It’s a collaborative approach to software development or IT operations. It’s a methodology based on communication and collaboration within an organization. It relies in particular on the use of an iterative software development method, on automation, and on the deployment of programmable infrastructures. To adopt this approach, an organization may have to change its entire software delivery chain, services, roles and IT tools. A DevOps environment is generally based on continuous integration (CI) and continuous deployment (CD) tools. Task automation is also a key component. Real-time monitoring and incident management systems and collaboration platforms are also widely used. The same applies to cloud computing, microservices and software container technologies. The aim of DevOps is to improve the software development cycle. The process consists of successive steps: plan, code, build, test, relax, deploy, operate, monitor. These steps are repeated in an infinite loop. The software is continually updated to meet expectations. Updates are tested in containers, then deployed in production. In the event of a problem, traceability ensures rapid intervention.

Software projects within a company often run into the same problems. Development takes too long and the software doesn’t live up to expectations. DevOps can remedy these problems, saving considerable time and ensuring that negative feedback is taken into account immediately. The communication between different IT teams is also improved. Since its creation in 2009 by Patrick Debois, DevOps has enjoyed a boom worldwide. Thanks to its many advantages, this methodology is being massively adopted by companies. DevOps is particularly useful for Data Science projects.

The DevOps Engineer has a dual role: he or she is a developer and a system administrator.

They work with developers and IT staff to oversee different versions of code, combining an understanding of engineering and coding. From creating and implementing new software to analyzing data to improve existing software, the DevOps Engineer aims to increase the productivity of his company. They deal with the software development lifecycle and are familiar with the various automation tools used to develop digital pipelines.

A DevOps Engineer, Machine Learning Engineer, Data Engineer or any other role using the DevOps method needs to have software development skills, but also operations skills such as infrastructure configuration.

To adopt this development method, you need to master Cloud Computing, software containers and continuous development/deployment technologies.

It is imperative to master code repository platforms such as GitHub and GitLab. These platforms enable multiple developers to collaborate on code, with the ability to revert to previous versions when needed.

This is a major component of a CI/CD pipeline, as validated code changes activate the next steps in the process, such as static code analysis or testing.

There are also CI/CD engines such as Jenkins, GitLab and CircleCI. These tools enable DevOps teams to validate and deliver applications to end-users in an automated way as development progresses.

Containers enable software to be tested in an isolated environment. They are very useful for working on code changes. The main containerization tools are Docker and Kubernetes.

Finally, Cloud environments are widely used for DevOps, particularly for scaling and deployment. AWS and Microsoft Azure are the most popular Cloud providers, and also offer CI/CD services.

The DevOps system administrator automates the deployment of infrastructures on a private, public or hybrid cloud.

When working for a software publisher or in an IT department with teams of developers, the DevOps system administrator deploys applications on a continuous basis.

They supervise the services deployed and deal with any alerts raised.

To automate the deployment of cloud infrastructures, the DevOps system administrator automates the creation of servers using scripts, configures and connects them together, and then uses an Ansible-type platform to configure and monitor deployment.

When in charge of deploying an application on a continuous basis, in conjunction with the development teams, he prepares test and pre-production environments.

The DevOps Engineer prepares the various data servers and associated storage, as well as the containers for the application. He oder she then migrates the data and deploys the application in the pre-production environment.

The DevOps Engineer is in constant contact with the development team to correct any malfunctions discovered during the various test phases. Using a Kubernetes-type platform, he deploys the application and its successive updates on the production environment.

The DevOps system administrator supervises the infrastructures and applications he has deployed. To do this, he defines the indicators to be monitored and installs and configures a supervision solution. When an anomaly is detected or an alert is raised, the problem is corrected or rectified.

In order to solve a configuration problem, understand the cause of a malfunction or install a new tool, he exchanges information on professional community forums, possibly in English.

Most technical documentation is written in English, so DevOps system administrators need to be able to read it to find the information they are looking for, and interpret the advice given correctly. This corresponds to level B2 of the European framework for reading comprehension.

He oder she will sometimes have to ask questions or provide answers on user forums in English; level B1 of the European framework for written expression is sufficient.

The DevOps Engineer also uses a logical approach to diagnose the cause of a malfunction and to remedy it, and keeps an active watch to keep his or her skills up to date.

This job requires mastery of a wide range of tools and languages, as well as an understanding of abstract concepts.

The DevOps system administrator is in contact with teams of developers, his or her technical manager, network and security teams, hosting solution providers, and professional communities for the tools he or she uses.

The DevOps system administrator works in a digital services company (ESN), a Cloud operator, a software publisher or in the IT department of a large company.

They work as part of a team, reporting to their company’s technical manager or IT director. In some cases, this activity is carried out entirely remotely.

The Course

A data lake is a platform for storing and analyzing data, with no constraints on type or structure. Find out all you need to know about this indispensable tool for Data Scientists: definition, operation, use cases, training...

A Data Lake is a storage repository that can hold vast amounts of structured, unstructured, or semi-structured data in its native format. Like a real lake, data flows into it from various sources in real-time.

This platform imposes no constraints in terms of file size or data category, allowing high-performance data analysis and native integration.

Different types of data analysis can be performed, such as Big Data processing, real-time analytics, Machine Learning, as well as creating dashboards and data visualizations.

Within the Data Lake, each data element receives a unique identifier and is associated with a set of metadata. The architecture is not hierarchical, unlike that of a Data Warehouse.

Why use a Data Lake?

A Data Lake enables the cost-effective storage of various types of data for later analysis, providing an initial overview for Data Scientists.

Data can be stored without a predefined model, regardless of their structure. The Data Lake delivers agility for organizations.

Artificial intelligence and Machine Learning allow for highly advanced predictive analysis. It is possible to analyze data from new sources such as log files, clickstreams, social media, or IoT devices.

With a Data Lake, a company can identify and seize opportunities. For example, it is possible to attract and retain new customers, increase productivity, perform predictive maintenance, or make better decisions.

By implementing it, the company gains a competitive advantage. According to a survey conducted by Aberdeen, companies that have implemented a Data Lake outperform similar organizations in terms of revenue growth by 9%.

Data Lake architecture and operation

Initially, data is ingested from various sources such as databases, web servers, or IoT devices using connectors. It can be loaded in batches or in real-time.

The storage provided by a Data Lake is scalable and allows for quick access for data exploration. Once the data is stored, it can be transformed into a structured form to facilitate analysis. Data can be tagged to associate metadata with it.

SQL or NoSQL queries, or even software like Excel, can then be used to analyze the data. Whenever a question arises within the company, it is possible to query the Data Lake by analyzing only a subset of relevant data. The Data Lake also enables data management and governance.

Advantages and disadvantages of the Data Lake

A Data Lake allows for storing and analyzing data while offering cost-effective flexibility. It enables extracting value from any type of data. The main strength of a Data Lake is the ability to centralize content from various sources. All users within a company can access it, even if they are geographically separated.

However, the Data Lake also has drawbacks. It is a platform that can be challenging to manage and may lose relevance over time. Storing unstructured data can quickly lead to chaos if not properly managed.

The use of such a platform can also be costly and pose cybersecurity risks if not designed methodically. Data stored without precautions can also lead to privacy or compliance issues.

Data Lake vs. Data Warehouse: what are the differences?

Data Lakes and Data Warehouses serve the purpose of storing and processing data but have significant differences.

One of the key distinctions of a Data Lake is its retention of all data. A Data Warehouse retains only data that can be used to answer specific queries or generate reports, simplifying storage and saving space.

On the other hand, a Data Lake retains all data, even if it’s not immediately useful. This is made possible by the hardware used, which is typically quite different from that of a Data Warehouse and more cost-effective.

Another difference is that a Data Lake supports all types of data without exception, regardless of their source and structure. Data is stored in its raw form and transformed when needed.

In contrast, Data Warehouses typically focus on data extracted from transactional systems, such as quantitative metrics and attributes describing them. Non-traditional sources like web server logs, sensor data, social media, text, and images are often ignored because they are expensive and challenging to store.

Data Lakes also offer greater adaptability to change. Developing and configuring a Data Warehouse can be time-consuming, and any changes may require significant time and resources.

This is not the case with Data Lakes, as all data is stored in its raw form. This allows for innovative data exploration and the automation of schemas if they prove to be relevant.

Lastly, Data Lakes tend to provide faster analysis results. Users can access all types of data before it has been transformed, cleaned, or structured.

The downside is that analyzing data on a Data Lake requires more technical skills. These platforms are not as accessible to non-technical business users as Data Warehouses, making them more suitable for Data Scientists.

Data warehouses in the cloud

Data Lakes can be deployed on-premises or in the cloud. Opting for cloud computing offers superior performance, scalability, and increased reliability.

Users can also benefit from various analytical engines. Security is enhanced, deployment is accelerated, and feature updates are more frequent. Costs are typically proportional to actual usage.

The importance of data lakes in business

Companies striving to align with Big Data are always looking for new ways to efficiently manage data. However, large datasets are not always easy to analyze. Adopting a Data Lakes approach can address these issues and help with other aspects of their business, such as improving customer relations, research and development activities, and operational efficiency.

To do this, a company can effectively implement Data Lakes by following these steps:

Understanding the benefits of data lakes

A data lake provides key features that will enable a company to discover new ways to enhance analysis and inform executive decision-making. A significant amount and variety of data need to be managed. Data governance is crucial to standardize information from various sources, ensure their accuracy and transparency, and prevent misuse.

Harnessing data lakes for business intelligence

Business Intelligence is an effective approach that allows experts in a company to use advanced methodologies to work with large volumes of raw data. This enables them to gain relevant insights that can improve decision-making and uncover new growth opportunities.

A data lake can enhance a BI solution by providing greater data processing potential. It can serve as a centralized data source for building a Data Warehouse and function as a direct data source for BI.

Data lakes have applications in data science and machine learning engineering where massive datasets are the backbone of technical solutions.

Add a structure

To make sense of the vast amounts of unstructured data stored in a data lake, a company needs to create some structure, such as file metadata, word counts, etc. A data lake provides a single platform where the company can apply structure to a variety of datasets, enabling it to process combined data in advanced analytical scenarios.

How to learn how to use a Data Lake

A data lake is a valuable asset for businesses in all industries. Therefore, mastering this tool can easily lead to job opportunities in any field.

To become an expert in this area, you can turn to DataScientest’s training programs. Data Lake is essential in Data Science, and you will learn how to use it through our various courses: Data Scientist, Data Engineer, Data Analyst, Data Management, or Machine Learning Engineer.

All our training programs offer an innovative approach to Blended Learning, halfway between in-person and distance learning, and can be completed in an intensive BootCamp or Continuing Education format. At the end of the program, learners receive a diploma certified by the University of Sorbonne.

You know all about the Data Lake. Discover our complete dossier on databases, and our introduction to Data Science.

Career

According to Hays, a DevOps employee can earn between €520 per day (with less than 3 years of experience) and €760 per day (with more than 8 years of experience). For those starting their career, DevOps engineers can earn up to €3,000 gross per month. As they gain more experience (5 to 10 years), a seasoned DevOps engineer can earn over €60,000 per year, and after 15 years of experience, they can earn more than €75,000 per year (Source: Monde Informatique).

We are here to support you in your career path, with a singular goal in mind: to enhance your employability.
The sectors we primarily focus on include:
  • Digital services companies (ESNs)
  • Specialized ESNs offering Cloud hosting services (Cloud Providers)
  • Software publishers
  • IT departments of companies with dedicated IT development departments

The available job positions in these sectors are as follows:
  • DevOps Engineer
  • SysOps DevOps
  • DevOps Systems Engineer
  • Cloud Engineer
  • Cloud Developer
After analyzing comparable certifications, no equivalents to the professional title of “DevOps System Administrator” registered with France Compétences’s RNCP have been identified. However, there are several RSCH-registered certifications that can help develop skills in the DevOps field:
  • Implement DevOps for AWS Cloud (RS5849)
  • Implementing DevOps for the Microsoft Azure cloud (RS5343)
  • Use DevOps chain development tools (RS5043)
  • Apply the DevOps method to optimize the application lifecycle (RS5044)
  • Deploy DevOps infrastructure with microservices architecture (RS5363)
  • Use DevOps methods and tools in infrastructure administration (RS5234)

In terms of career progression, a DevOps System Administrator can transition into cybersecurity professions such as analyst or security coordinator. Additionally, they can explore the field of DevSecOps. Opportunities for advancement can include roles such as systems engineer, developer, or product owner at the p-1 level, and roles such as Cloud and DevOps manager, network and infrastructure engineer, or Chief Technical Officer (CTO) at the p+1 level.
On the first day of your training, you will be presented with a dedicated career services platform containing all the workshops essential to your job search. You can access it at any time, even after your course has finished. Mathilde and Morgane, our career managers, are dedicated to you throughout your training. You can make an individual appointment with one of them to support you and answer any questions you may have about your career plans.
In addition, career workshops are organized every month:
  • A workshop to help you write a good data-driven CV, which can also be used for LinkedIn
  • A workshop to help you strategize your job search with various topics on presentation, career change, salary negotiation and technical test practice. These topics are supplemented by other workshops to be defined according to individual needs. Please note that these events are mainly offered in French, and we are continuously working on growing this service for english-speakers as well.

In addition, concrete actions are put in place to support you in your job search: recruitment fairs organized by DataScientest with its partner companies, organization of Webinars with data expert speakers, communication actions to boost your visibility (CV Competition, DataDays, project articles published on the blog and external reference media). To find out more about DataScientest’s career support initiatives, click here.

Mathilde and Morgane, our career managers, will be available throughout the course to support you in your professional (re)integration project. You can schedule an individual appointment with either of them to receive personalized support and have your career-related questions answered.

Additionally, we organize monthly career workshops to provide you with the best advice for success, including workshops on CV and LinkedIn optimization, job search assistance, and mock interviews.

Furthermore, we have various initiatives in place to support your job search, such as recruitment fairs organized by DataScientest to introduce you to our partner companies, webinars with data experts, and communication initiatives to boost your visibility, including CV competitions, DataDays events, and the publication of project articles on our blog and other external reference media.

Our Services

A data lake is a platform for storing and analyzing data, with no constraints on type or structure. Find out all you need to know about this indispensable tool for Data Scientists: definition, operation, use cases, training...

A Data Lake is a storage repository that can hold vast amounts of structured, unstructured, or semi-structured data in its native format. Like a real lake, data flows into it from various sources in real-time.

This platform imposes no constraints in terms of file size or data category, allowing high-performance data analysis and native integration.

Different types of data analysis can be performed, such as Big Data processing, real-time analytics, Machine Learning, as well as creating dashboards and data visualizations.

Within the Data Lake, each data element receives a unique identifier and is associated with a set of metadata. The architecture is not hierarchical, unlike that of a Data Warehouse.

Why use a Data Lake?

A Data Lake enables the cost-effective storage of various types of data for later analysis, providing an initial overview for Data Scientists.

Data can be stored without a predefined model, regardless of their structure. The Data Lake delivers agility for organizations.

Artificial intelligence and Machine Learning allow for highly advanced predictive analysis. It is possible to analyze data from new sources such as log files, clickstreams, social media, or IoT devices.

With a Data Lake, a company can identify and seize opportunities. For example, it is possible to attract and retain new customers, increase productivity, perform predictive maintenance, or make better decisions.

By implementing it, the company gains a competitive advantage. According to a survey conducted by Aberdeen, companies that have implemented a Data Lake outperform similar organizations in terms of revenue growth by 9%.

Data Lake architecture and operation

Initially, data is ingested from various sources such as databases, web servers, or IoT devices using connectors. It can be loaded in batches or in real-time.

The storage provided by a Data Lake is scalable and allows for quick access for data exploration. Once the data is stored, it can be transformed into a structured form to facilitate analysis. Data can be tagged to associate metadata with it.

SQL or NoSQL queries, or even software like Excel, can then be used to analyze the data. Whenever a question arises within the company, it is possible to query the Data Lake by analyzing only a subset of relevant data. The Data Lake also enables data management and governance.

Advantages and disadvantages of the Data Lake

A Data Lake allows for storing and analyzing data while offering cost-effective flexibility. It enables extracting value from any type of data. The main strength of a Data Lake is the ability to centralize content from various sources. All users within a company can access it, even if they are geographically separated.

However, the Data Lake also has drawbacks. It is a platform that can be challenging to manage and may lose relevance over time. Storing unstructured data can quickly lead to chaos if not properly managed.

The use of such a platform can also be costly and pose cybersecurity risks if not designed methodically. Data stored without precautions can also lead to privacy or compliance issues.

Data Lake vs. Data Warehouse: what are the differences?

Data Lakes and Data Warehouses serve the purpose of storing and processing data but have significant differences.

One of the key distinctions of a Data Lake is its retention of all data. A Data Warehouse retains only data that can be used to answer specific queries or generate reports, simplifying storage and saving space.

On the other hand, a Data Lake retains all data, even if it’s not immediately useful. This is made possible by the hardware used, which is typically quite different from that of a Data Warehouse and more cost-effective.

Another difference is that a Data Lake supports all types of data without exception, regardless of their source and structure. Data is stored in its raw form and transformed when needed.

In contrast, Data Warehouses typically focus on data extracted from transactional systems, such as quantitative metrics and attributes describing them. Non-traditional sources like web server logs, sensor data, social media, text, and images are often ignored because they are expensive and challenging to store.

Data Lakes also offer greater adaptability to change. Developing and configuring a Data Warehouse can be time-consuming, and any changes may require significant time and resources.

This is not the case with Data Lakes, as all data is stored in its raw form. This allows for innovative data exploration and the automation of schemas if they prove to be relevant.

Lastly, Data Lakes tend to provide faster analysis results. Users can access all types of data before it has been transformed, cleaned, or structured.

The downside is that analyzing data on a Data Lake requires more technical skills. These platforms are not as accessible to non-technical business users as Data Warehouses, making them more suitable for Data Scientists.

Data warehouses in the cloud

Data Lakes can be deployed on-premises or in the cloud. Opting for cloud computing offers superior performance, scalability, and increased reliability.

Users can also benefit from various analytical engines. Security is enhanced, deployment is accelerated, and feature updates are more frequent. Costs are typically proportional to actual usage.

The importance of data lakes in business

Companies striving to align with Big Data are always looking for new ways to efficiently manage data. However, large datasets are not always easy to analyze. Adopting a Data Lakes approach can address these issues and help with other aspects of their business, such as improving customer relations, research and development activities, and operational efficiency.

To do this, a company can effectively implement Data Lakes by following these steps:

Understanding the benefits of data lakes

A data lake provides key features that will enable a company to discover new ways to enhance analysis and inform executive decision-making. A significant amount and variety of data need to be managed. Data governance is crucial to standardize information from various sources, ensure their accuracy and transparency, and prevent misuse.

Harnessing data lakes for business intelligence

Business Intelligence is an effective approach that allows experts in a company to use advanced methodologies to work with large volumes of raw data. This enables them to gain relevant insights that can improve decision-making and uncover new growth opportunities.

A data lake can enhance a BI solution by providing greater data processing potential. It can serve as a centralized data source for building a Data Warehouse and function as a direct data source for BI.

Data lakes have applications in data science and machine learning engineering where massive datasets are the backbone of technical solutions.

Add a structure

To make sense of the vast amounts of unstructured data stored in a data lake, a company needs to create some structure, such as file metadata, word counts, etc. A data lake provides a single platform where the company can apply structure to a variety of datasets, enabling it to process combined data in advanced analytical scenarios.

How to learn how to use a Data Lake

A data lake is a valuable asset for businesses in all industries. Therefore, mastering this tool can easily lead to job opportunities in any field.

To become an expert in this area, you can turn to DataScientest’s training programs. Data Lake is essential in Data Science, and you will learn how to use it through our various courses: Data Scientist, Data Engineer, Data Analyst, Data Management, or Machine Learning Engineer.

All our training programs offer an innovative approach to Blended Learning, halfway between in-person and distance learning, and can be completed in an intensive BootCamp or Continuing Education format. At the end of the program, learners receive a diploma certified by the University of Sorbonne.

You know all about the Data Lake. Discover our complete dossier on databases, and our introduction to DataScience.

Join the DevOps journey!