GGML GGUF File Format Vulnerabilities
The GGUF file format is a binary file format used for storing and loading model weights for the GGML library. The library documentation...
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Parameterized queries with PySpark
PySpark has always provided wonderful SQL and Python APIs for querying data. As of Databricks Runtime 12.1 and Apache Spark 3.4, parameterized queries...
Introducing Apache Spark™ 3.5
Today, we are happy to announce the availability of Apache Spark™ 3.5 on Databricks as part of Databricks Runtime 14.0. We extend our...
Shared Clusters in Unity Catalog for the win: Introducing Cluster Libraries, Python UDFs, Scala, Machine Learning and more
We are thrilled to announce that you can run even more workloads on Databricks’ highly efficient multi-user clusters thanks to new security and...
Introducing English as the New Programming Language for Apache Spark
Introduction We are thrilled to unveil the English SDK for Apache Spark, a transformative tool designed to enrich your Spark experience. Apache Spark™...
Databricks ❤️ Hugging Face
Generative AI has been taking the world by storm. As the data and AI company, we have been on this journey with the...
Spark Connect Available in Apache Spark 3.4
Last year Spark Connect was introduced at the Data and AI Summit. As part of the recently released Apache SparkTM 3.4, Spark Connect...
Pandas-Profiling Now Supports Apache Spark
Data profiling is the process of collecting statistics and summaries of data to assess its quality and other characteristics. It is an essential...
Announcing Ray support on Databricks and Apache Spark Clusters
Ray is a prominent compute framework for running scalable AI and Python workloads, offering a variety of distributed machine learning tools, large-scale hyperparameter...
Scalable Kubernetes Upgrade Using Operators
At Databricks, we run our compute infrastructure on AWS, Azure, and GCP. We orchestrate containerized services using Kubernetes clusters. We develop and manage...