Delta Lake Blogs
Building a more efficient data infrastructure for machine learning with Open Source using Delta Lake, Amazon SageMaker, and EMR
By Vedant Jain , Denny Lee
In this blog, we’ll explore how connecting Delta Lake, Amazon SageMaker Studio, and Amazon EMR can simplify the end-to-end workflow required to support data engineering and data science projects.
Data Sharing across Government Agencies using Delta Sharing
By Li Yu , Mubashir Kazia , Jon D. Ceanfaglione , Prabha Rajendran , Purushotam Shrestha , Shawn A. Benjamin
This post shows how government agencies are sharing data with Delta Sharing.
How to Delete Rows from a Delta Lake Table
This post teaches you how to delete rows from a Delta Lake table and how the operation is implemented under the hood.
Delta Lake Constraints and Checks
This post shows how to add constraints to your Delta table to avoid certain types of values from getting appended.
Delta Lake Schema Enforcement
This post teaches you about schema enforcement in Delta Lake and why it's better than what's offered by data lakes
Why PySpark append and overwrite write operations are safer in Delta Lake than Parquet tables
This post shows you why PySpark overwrite operations are safer with Delta Lake and how the different save mode operations are implemented under the hood.
How to Create Delta Lake Tables
This post shows you how to create Delta Lake tables with Python, SQL, and PySpark.
How to Version Your Data with pandas and Delta Lake
This post shows you how to version your pandas datasets and the benefits you'll enjoy with versioned data.
Sharing a Delta Table’s Change Data Feed with Delta Sharing 0.5.0
By Will Girten
We are excited to announce the release of Delta Sharing 0.5.0.
How to Rollback a Delta Lake Table to a Previous Version with Restore
This post shows you how to rollback Delta Lake tables to previous versions with restore.
Converting from Parquet to Delta Lake
This post shows how to convert a Parquet table to a Delta Lake.
Why we migrated to a Data Lakehouse on Delta Lake for T-Mobile Data Science and Analytics Team
By Robert Thompson , Geoff Freeman
In this post, we will discuss the how and why we migrated from databases and data lakes to a data lakehouse on Delta Lake. Our lakehouse architecture allows reading and writing of data without blocking and scales out linearly. Business partners can easily adopt advanced analytics and derive new insights. These new insights promote innovation across disparate workstreams and solidify the decentralized approach to analytics taken by T-Mobile.