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 with the Saagie, DataOps platform powered by Kubernetes. Mix and match technologies, libraries and their versions to change and adapt your technology strategy while optimizing costs. 

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 deliberations on architecture. Our DataOps Platform offers multi-context preset technologies coupled with their libraries so you can stay agile and continuously adapt your technology strategy.

Saagie's technologies

Make your jobs reproducible

With Saagie as a DevOps enabler, you don’t have to stop at proof of concept and can meet all your production criteria. Let your data teams experiment but ensure that their processing jobs work as well in production as they did in lab through our job promotion capabilities. Every job is reproducible regardless of the environment, which means you don’t have to struggle with Python environments or customize them 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 so your data engineers and data scientists can collaborate on the same project. You can also monitor and iterate since every change and run instance are recorded to help you keep track of their work.

pipelines

Our supported technologies

You can use our off-the-shelf frameworks to immediately start orchestrating extract, transform, load (ETL) workloads and preprocess and process 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

Choose from our selection of preset 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