Orchestrate Technologies and Run Pipelines

Make the most of the open-source, commercial and cloud-native worlds with smooth integration and orchestration of data technologies through Saagie, powered by Kubernetes. Mix and match technologies, libraries and their versions to change and adapt your technology strategy at low integration cost. 

Choose Your Technology

Start now and leverage our smooth orchestration of ready-to-use frameworks from the open-source, commercial and cloud-native worlds and save yourself from months of architecture meeting deliberations. Our DataOps Platform offers multi-context preset technologies coupled with their libraries to allow you to be agile and adapt your technology strategy at low cost.

Saagie's technologies

Make Your Jobs Reproducible

Do not stop at the Proof Of Concept and meet production criteria with Saagie as a DevOps enabler. Let your data teams experiment but ensure that their processing jobs will work as properly in production as they did in Lab through our jobs promotion capabilities. Every job is reproducible whatever the environment and prevents you from having to struggle with Python environments or customize it by hand on Linux servers.

devops

Run Workflows Into Pipelines

After choosing your technologies and creating jobs, run them into pipelines to manage the entire data lifecycle. Mix and match frameworks to allow your data engineers and data scientists to collaborate on the same project. Monitor and iterate as every change and run instance are recorded to help you keep track of their work.

pipelines

Our Supported Technologies

With off-the-shelf frameworks, start now and orchestrate ETL (Extract, Transform, Load) workloads as well as preprocessing and processing technologies. 

Python

Category:
Data Engineering, Machine Learning
Common use case:
KPI & Machine Learning on limited size datasets
Supported versions:
2.7, 3.5, 3.6, 3.7

R

Category:
Data Engineering, Machine Learning
Common use case:
Statistics
Supported versions:
3.4.4, 3.5.3, 3.6.3

Java

Category:
Data Engineering
Common use case:
Streaming
Supported versions:
7, 8, 11

Talend

Category:
Data Engineering
Common use case:
Batch
Supported versions:
8.121, 8.131

Spark

Category:
Data Engineering, Machine Learning
Common use case:
KPI & Machine learning on large datasets, Streaming
Supported versions:
2.4.5 (Java: 8,11 – Python: 2.7, 3.5, 3.5, 3.7)

Sqoop

Category:
Data Engineering
Common use case:
Extraction
Supported versions:
1.4.6

Bash

Category:
Data Engineering
Common use case:
Extraction, Integration
Supported versions:
Debian 9, CLI AWS, CLI Azure, CLI GKE

Docker

Category:
Data Engineering, Machine Learning
Common use case:
App containerization

Our Apps Catalog

Leverage a selection of pre-set Docker images to integrate apps with embedded libraries. 

R Studio

Category:
Data Engineering, Machine Learning
Supported versions:
3.4.4, 3.6.2

Jupyter

Category:
Data Engineering, Machine Learning
Supported versions:
5.2.1

Zeppelin

Category:
Data Engineering, Machine Learning
Supported versions:
0.7.3

Kibana

Category:
Data Visualization
Supported versions:
5.6.3, 5.6.16, 6.4.1, 6.4.3, 6.8.1, 6.8.4, 7.4.0, 7.4.1, 7.5.1, 7.6.2

Nifi

Category:
Data Engineering
Supported versions:
1.9.2

Grafana

Category:
Data Visualization
Supported versions:
6.6.2