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lakeFS Community

Data Engineering

Data Engineering Thought Leadership

Why Data Versioning as an Infrastructure Matters

Einat Orr, PhD

The demand for infrastructure that contributes to the collection, storage, and analysis of data is growing with the increasing amounts of data managed by organizations. Every organization that manages data pipelines to extract insights from data encounters the need for reproducibility, safe experimentation, and means to ensure data quality. The path to answering these needs …

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Best Practices Data Engineering

Loosely Coupled Monolith vs Tightly Coupled Microservices

Barak Amar

TL;DR With some thoughtful engineering, we can achieve a lot of the benefits that come with a microservice oriented architecture, while retaining the simplicity and low operating cost of being a monolith. What is lakeFS? lakeFS is an open source tool that delivers resilience and manageability to object-storage based data lakes. lakeFS provides Git-like capabilities …

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Best Practices Data Engineering

Data Mesh Applied: How to Move Beyond the Data Lake with lakeFS

Einat Orr, PhD

The data mesh paradigm The Data Mesh paradigm was first introduced by Zhamak Dehghani in her article How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh.  Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, …

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Data Engineering

Object Storage: Everything You Need to Know

Yael Rivkind

While Object Storage is not novel technology, it can still be overwhelming when getting started. Here’s a definitive guide to object-based storage with everything you need to know.   What is object storage? At its core, object storage or object-based storage represents a data storage architecture that allows you to store large amounts of unstructured …

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Data Engineering

Chaos Data Engineering

Oz Katz

Modern Data Lakes are a complexity tar pit. They involve many moving parts: distributed computation engines, running on virtualized servers connected by a software defined network, running on top of distributed object stores, orchestrated by a distributed stream processor or pipeline execution engine. These moving parts fail. All the time. Handling these failures is not …

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Best Practices Data Engineering

System Tests: Lessons Learned From Developing For OSS Project

Itai Admi

Overview In this article, I will try to cover some do’s and don’ts for system testing from the perspective of an open-source project. To keep things simple, it all boils down to running the system as our customers would: think of the different use-cases of your system, the environment where it runs, the configuration options, …

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Best Practices Data Engineering Tutorials

Building A Data Development Environment with lakeFS

Barak Amar

Overview As part of our routine work with data we develop code, choose and upgrade compute infrastructure, and test new data. Usually, this requires running parts of our production pipelines in parallel to production, testing the changes we wish to apply. Every data engineer knows that this convoluted process requires copying data, manually updating configuration, …

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Best Practices Data Engineering

How to Manage Your Data the Way You Manage Your Code

Einat Orr, PhD

50 years ago it was very hard to collaborate over code. When developing large scale software projects it was difficult to manage changes to source code over time, as revision control tools were only starting to enter mainstream computing. The adoption of version control tools, first centralized and then distributed, changed all that, and now …

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Git for Data – lakeFS

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