Phenytech

Why Phenytech?

A close-knit team, where everyone  can grow at the rate of projects that are always different, often complex and structuring for  our customers.

It is to help human-sized companies, innovative companies, start-ups and all those who want to quickly conquer markets that Phenytech was founded 15 years ago by two experienced data engineers, joined since then by numerous talents, all specialists and at the forefront of their field.

We offer ingenious solutions, adapted to the needs of our customers and intelligently filling internal gaps, saving them time and money.

Who are our customers?

55%

ETI

34%

PMI

11%

Key accounts

35%

Startup

65%

Established businesses

Our expertise

We are committed to providing the best Data/Cloud/DevOps technical skills

For a comprehensive approach, supported by an excellent knowledge of solutions and tools  of the market, combining reliability and permanent innovation.

Data

Big data: Management and processing of massive data
Data Science: Use of scientific methods, processes, and algorithms to extract knowledge from structured and unstructured data.
Data Engineering: Construction and maintenance of data architectures, ETL pipelines, and data warehouses.
Data governance: Establishing policies and processes to ensure data quality, security, and compliance
Reporting/DataViz: Transforming data into actionable information for decision making.

Tools: PostgreSQL, Mysql, SQL Server, SQL Server, Oracle, MonetDB, Oracle, MonetDB, Vertica, MongoDB, Elastic Search/Kibana,
GCP : GCP: Bigtable, Pubsub, Cloud Functions, Cloud Run, BigQuery
AWS :AWS: RDS, Redshift, SNS, Glue Job, EMR
Divers :Various: Snowflake, Apache Kafka, RabbitMQ, RabbitMQ, Apache Spark, Apache Spark, Starlake, DBT, AWS Glue, Dataproc, Airflow, Talend, Talend, Pentaho Data Integration, Pentaho Data Integration, Pentaho Data Integration, Pentaho Data Integration, Tableau, Microsoft PowerBI

DevOps

CI/CD: Establishment of continuous integration and continuous deployment pipelines to automate and accelerate development cycles.
Infrastructure as Code (IaC): Using code to manage and provision IT infrastructure
Containerization: Deploy and orchestrate containers for isolated and repeatable environments.
Monitoring and logging: Implementation of monitoring and logging solutions to ensure the visibility and performance of systems.

Tools: Github Actions, Gitlab CI/CD, BitBucket, CI/CD, BitBucket, Jenkins, Terraform, Docker, Kubernetes, Prometheus, Grafana, Datadog

Cloud

Infrastructure as a Service (IaaS): Provision of virtualized computing resources on demand.
Scalability: Ability to increase or decrease IT resources as needed.
Cloud security: Implementation of security measures to protect data and applications in the cloud.
Cloud migration: Moving infrastructure, applications, and data from a local environment to a cloud environment.

Tools: Amazon Web Services (Lambda, Fargate, RDS, RDS, Redshift, EMR, Glue jobs), Microsoft Azure (Container Instances, Linux Webapp), Google Cloud Platform (GKE, Cloud run jobs, Cloud jobs, Cloud functions, Cloud functions, BigTable, BigQuery), Cloud functions, BigTable, BigQuery), Access Control and Identity Management (IAM), Data Encryption, Firewall, and Network Protection