lakeFS transforms object storage buckets into data lake repositories that expose a Git-like interface. By design, it works with data of any size.

The Git-like interface means users of lakeFS can use the same development workflows for code and data. Git workflows greatly improved software development practices; we designed lakeFS to bring the same benefits to data.

In this way, lakeFS brings a unique combination of performance and manageability to data lakes

The move to data lakes, with their infinite scale and low costs, also introduced a new challenge in maintaining and ensuring data resilience and reliability within the data lake as time goes by. Naturally, the quality of the data we introduce determines the overall reliability of our data lake.

Despite the scalability and performance advantages of running a data lake on top of object stores, enforcing best practices, ensuring high data quality and recovering quickly from errors remains extremely challenging. Specifically, the data ingestion stage is critical for ensuring the soundness of our service and data.

What are the lakeFS use cases?

When considering it, data engineers should continuously test newly ingested data while ensuring they meet data quality requirements, much like software engineers applying automatic new code testing. So that when a mistake happened and 'bad data' was ingested into the lake, they can have a feasible way to reproduce the ingestion error at the time of failure, and roll back to the previous high quality snapshot of their data. Sounds right, doesn't it?

Through its versioning engine, lakeFS enables the following built-in operations familiar from Git, to enable these best practices that are coming from the world of code into the world of data engineering:

* branch: a consistent copy of a repository, isolated from other branches and their changes. Initial creation of a branch is a metadata operation that does not duplicate objects.

* commit: an immutable checkpoint containing a complete snapshot of a repository.

* merge: performed between two branches - merges atomically update one branch with the changes from another.

* revert: return a repo to the exact state of a previous commit.

* tag: a pointer to a single immutable commit with a readable, meaningful name.

Incorporating these operations into your data lake pipelines provides the same collaboration and organizational benefits you get when managing application code with source control.

What are the benefits of using lakeFS with data lakes?

When using lakeFS on your object store, you improve the entire process of data management within your organization and enjoy the following benefits:

* Data teams efficiency - lakeFS enables automation of many of the repetitive manual labor-heavy tasks that data engineers deal with on a daily basis. lakeFS eliminates manual tasks such as manual rollback of production data (have you ever tried to restore data that was accidentally deleted by some retention algorithm?), or trying to debug issues in production without a solid version of the data at the time of failure. When your data engineers are free from these tasks, they can focus on what they really know and love to do: develop more and more rich & efficient data sources and algorithms for your organization.

* High quality data products - lakeFS enables validating the data coming into the data lake before it is exposed to external users. Being able to prevent inconsistencies and errors before they happen is one of the strongest capabilities of lakeFS. It enables organizations to gain more trust in their ever-growing and ever more complex data estates, and this is a great value for many organizations that rely on their data.

* Data resilience - At lakeFS, we believe that data resilience means that even when mistakes and inconsistencies happen, we can quickly recover from them. One of the core capabilities of lakeFS is the ability to rollback the entire data lake to its previous consistent state. This is a valuable feature which enables organizations to eliminate data downtimes. In addition, keeping versions of the data and being able to time travel between them enables data resilience, as data engineers can automatically check the data as it was at the time of failure and reduce dramatically the time they invest in investigating and fixing bugs, errors and inconsistencies.