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lakeFS enabled us to streamline and run 200+ dbt models in production, increase data deployment velocity, efficiently reproduce ML experiments, increase productivity of the data teams and adhere to FDA compliance requirements
This blog discusses advanced topics within lakeFS. If you are new to lakeFS, or would like to expand your knowledge of how lakeFS works, make sure to check out our documents section. In the Beginning There Was Postgres Up until recently, lakeFS was using a strongly consistent SQL DB, namely PostgreSQL, where all metadata was …
lakeFS with DynamoDB – How Key Value Store is Used by lakeFS Read More »
As the Covid-19 pandemic loosens its grip on the world, we’re all eager to start travelling and meeting in person again. The great news is that in persone conferences are back, so it’s time to make up for lost time! Attending conferences is a great way to learn, network, and engage with like-minded people. Not …
Top Data Engineering Conferences & Events You Don’t Want to Miss in the Fall of 2022! Read More »
Organizations have practically always needed data analytics, and they jumped on the analytics bandwagon as soon as the first computers appeared on the scene. In the 80s, businesses built data warehouses using relational databases as their decision-support systems (DSS). However, as companies generated more diverse data at high velocity, relational databases showed their limitations. This …
Data Mesh: What is it and What Does it Mean for Data Engineers? Read More »
A year has passed since we shared the State of Data Engineering 2021. And since we released that article last May, not much has changed in the data landscape. In fact, we had discussions internally about whether we should even do an update for 2022. We kid. It was another year worthy of its own …
The last decade saw an unprecedented rise in the number of organizations that base their decisions and operations on data. The number of digital products that collect and process data and use it to fuel decision-making algorithms for enhancing future services is also growing at a very fast pace. That’s why data and data quality …
Clearing the mess – How to ensure data quality with versioning Read More »
Modern data engineering practices lead more and more organizations to a broader use of object stores. This happens due to the rising scale and complexity of the data that they manage – along with the growing variety of use cases that these data warehouses need to cater: from machine learning and algorithm development, to analytics …
5 Painful mistakes data engineers make, and how to avoid them Read More »
Introduction It may seem strange at first, but increasingly we cannot be sure when putting or getting data from an object store that the data is being sent around is coming from the service we think it is. Imitation is the highest form of flattery, and given cloud object storages’ popularity, they have naturally attracted …
Overview of Hive’s Metastore Let’s get right into it. This is not an objective recap of every topic covered at the Future of Metadata After Hive Roundtable last week. But it is a summary of what I found most interesting from the discussion between panelists Lior Ebel, Ryan Blue, Seshu Adunuthula and host Oz Katz. Watch the full talk below! …
Takeaways From the Future of Metadata After Hive Metastore Roundtable Read More »
This is the first post of a series (well, at least 2) about Data Products. In Part I, we’ll cover what data products are, how they differ from traditional services built by product teams, and conclude with a few requirements that help create successful data products. What Are Data Products? Data products are carefully curated …
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