Introducing Kubeflow and lakeFS Kubeflow is a cloud-native ML platform that simplifies the training and deployment of machine learning pipelines on Kubernetes. An ML project using Kubeflow will consist of isolated components for each stage of the ML lifecycle. And each component of a Kubeflow pipeline is packaged as a Docker image and executed in a …
Build Reproducible Experiments with Kubeflow and lakeFS Read More »
How to use lakeFS to keep multiple Delta tables consistent and guarantee data quality.
Today we are excited to announce the official release of the lakeFS Airflow provider! What this package does is allow you to easily integrate lakeFS functionality to your Airflow DAGs. The library is published on PyPI so it can easily be installed in your project via the command: pip install airflow-provider-lakefs Once installed, you are …
Update (May 26th, 2021): We officially released the lakeFS Airflow provider. Read all about it in the latest blog post. In this post, we’ll see how easy it is to use lakeFS with an existing Airflow DAG, to make every step in a pipeline completely reproducible in both code and data. This is done without …
Building Reproducible Data Pipelines with Airflow and lakeFS Read More »
lakeFS is an open source tool that delivers resilience and manageability to object-storage based data lakes. lakeFS provides Git-like operations over your MinIO storage environment and works seamlessly with all modern data frameworks such as Spark, Hive, Presto, Kafka, R and Native Python etc. Common use-cases include creating a development environment without copying or mocking …
