Delta Lake
Delta Lake is a storage layer on top of existing data lake. It is compatible with Apache Spark. It helps tackling data reliability issues and manage data lifecycle. Underlying storage format is Parquet, a columnar open-source format. Delta Lake enables ACID transactions, scalable metadata handling, data versioning, schema enforcement and schema evolution. It also supports updates and deletes. It is available in open-source. version or managed version on Databricks.
Related articles
Internship in Data Engineering
Categories: Front End, Learning | Tags: Hive, Elasticsearch, Metrics, Monitoring, Kafka, Delta Lake, IaC, Kubernetes, Streaming
Job Description Data is a valuable business asset. Some call it the new oil. The data engineer collects, transform and refine raw data into information that can be used by business analysts and data…
By David WORMS
Oct 25, 2021
Self-Paced training from Databricks: a guide to self-enablement on Big Data & AI
Categories: Data Engineering, Learning | Tags: Cloud, Data Lake, Databricks, Delta Lake, MLflow
Self-paced trainings are proposed by Databricks inside their Academy program. The price is $ 2000 USD for unlimited access to the training courses for a period of 1 year, but also free for customers…
May 26, 2021
Data versioning and reproducible ML with DVC and MLflow
Categories: Data Science, DevOps & SRE, Events | Tags: Data Engineering, Git, Databricks, Delta Lake, Machine Learning, MLflow, Storage
Our talk on data versioning and reproducible Machine Learning proposed to the Data + AI Summit (formerly known as Spark+AI) is accepted. The summit will take place online the 17-19th November…
Sep 30, 2020
Experiment tracking with MLflow on Databricks Community Edition
Categories: Data Engineering, Data Science, Learning | Tags: Deep Learning, Spark, Databricks, Delta Lake, Machine Learning, MLflow, Notebook, Python, Scikit-learn
Introduction to Databricks Community Edition and MLflow Every day the number of tools helping Data Scientists to build models faster increases. Consequently, the need to manage the results and the…
Sep 10, 2020
Importing data to Databricks: external tables and Delta Lake
Categories: Data Engineering, Data Science, Learning | Tags: Parquet, AWS, Amazon S3, Azure Data Lake Storage (ADLS), Databricks, Delta Lake, Python
During a Machine Learning project we need to keep track of the training data we are using. This is important for audit purposes and for assessing the performance of the models, developed at a later…
May 21, 2020