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 »
What is the Basic Data Lake? A data lake is primarily two things: an object store and the objects being stored. It might look something like this: Even with this basic setup, your data is in a good position to support all three of the main use cases for data: 1. BI Analytics 2. Data-Intensive APIs …
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 »
Recently, we’ve heard from several community members experimenting with new development workflows using lakeFS and dbt. The timing isn’t surprising given dbt’s more recent support of big data compute tools like Spark and Trino that are some of the most commonly-used technologies by lakeFS users managing a data lake over an object store. The combination …
dbt Tests – Create Staging Environments for Flawless Data CI/CD Read More »
This week I’m pleased to share “Tardy Data”, a chapter I wrote for the new book 97 Things Every Data Engineer Should Know, adapted to blog format. It’s about what “late” means in data engineering. This content is licensed under the CC BY 4.0. Tardy Data When collecting time-based data: some are born late, some …
Databricks has come a long way since growing out of a Berkeley Lab in 2013 with an open-source distributed computing framework called Spark. Fast forward eight years and in addition to the core Spark product, there are a dizzying number of new features in various stages of public preview within the Databricks platform. In case …
Thoughts on the Future of the Databricks Ecosystem Read More »
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