Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Data Lake - Building Scalable Data Architectures training are: Mastery of SQL, knowledge of Big Data (Hadoop, Spark), and basic Python skills. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Data Lake - Building Scalable Data Architectures training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Data Lake - Building Scalable Data Arch... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Data Lake - Building Scalable Data Architectures training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Data Lake - Building Scalable Data Architectures training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Data Lake - Building Scalable Data Architectures training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master Data Lake architectures for scalable professional projects

Develop pipelines for ingesting massive data volumes in the enterprise

Implement optimized storage and partitioning strategies

Design advanced data processing with Spark and query tools

Optimize governance and security of certified Data Lakes

Deploy Data Lakes in production to boost analytical skills

Training program

Module 1Data Lake Fundamentals: Architecting Scalable Data Environments (S3, Azure Data Lake, Schema-on-Read Principles)

Dive into the key concepts of Data Lakes by evaluating hybrid architectures versus traditional data warehouses, configure a test environment with AWS S3 or Azure Data Lake Storage Gen2, explore schema-on-read to ingest raw data without prior transformation, perform practical exercises on zonal modeling (raw, refined, curated), produce a personal architecture diagram and analyze real enterprise cases to identify common pitfalls, transforming your skills into immediate professional assets.

Module 2Data Lake Ingestion: High-Performance ETL/ELT Pipelines (Kafka, NiFi, Airflow)

Build batch and streaming ingestion flows using Apache Kafka for real-time data and NiFi for visual orchestration, integrate Airflow to schedule complex pipelines, test on large datasets from application logs and IoT sensors, manage connectivity errors and resilience with advanced retry patterns, develop a complete pipeline from scratch with integrated monitoring, apply to a concrete enterprise case to accelerate access to raw data and boost your team's analytical productivity.

Module 3Data Lake Storage: Advanced Organization and Partitioning (Parquet, Delta Lake)

Optimize storage by converting data into columnar formats like Parquet and ORC for ultra-fast queries, implement Delta Lake for ACID compliance and time travel on your tables, master Hive-style partitioning and Z-ordering to reduce unnecessary scans, migrate a legacy dataset to a refined zone with hands-on exercises, analyze performance via real benchmarks, create a structured data catalog that prepares the ground for scalable enterprise analyses, making your data skills immediately operational.

Module 4Data Lake Processing: Querying and Transformation (Spark, Athena, Presto)

Process terabytes of data with Apache Spark in SQL and PySpark for distributed transformations, query your Data Lake via Amazon Athena or Presto for ad-hoc analyses without heavy infrastructure, develop cleansing and feature engineering jobs on concrete business cases like fraud detection, optimize performance with caching and broadcast joins, integrate MLflow to track pipelines, produce actionable insights via a red thread dashboard, strengthening your professional skills for data-driven decisions in the enterprise.

Module 5Data Lake Governance: Security and Monitoring (Ranger, Atlas, CI/CD)

Secure your Data Lake with Apache Ranger for fine-grained ACLs and Kerberos for authentication, catalog metadata via Atlas for GDPR-compliant governance, implement monitoring with Prometheus and Grafana to detect anomalies in real time, deploy via CI/CD using GitHub Actions or Jenkins on hybrid cloud, conduct a full audit of your red thread project with an improvement plan, simulate incident scenarios for maximum resilience, conclude with an internal certification that highlights your professional and scalable data management skills.