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With Delta Lake on Databricks, you have access to a vast open source ecosystem and avoid data lock-in from proprietary formats. Simplify data engineering with Delta Live Tables – an easy way to build and manage data pipelines for fresh, high-quality data on Delta Lake.How do I create a delta table in azure Databricks?
For Azure Databricks notebooks that demonstrate these features, see Introductory notebooks. To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, json, and so on, to delta. For all file types, you read the files into a DataFrame and write out in delta format:What format is data stored in Delta Lake?
All data in Delta Lake is stored in open Apache Parquet format, allowing data to be read by any compatible reader. APIs are open and compatible with Apache Spark. With Delta Lake on Databricks, you have access to a vast open source ecosystem and avoid data lock-in from proprietary formats.How do I partition data in Delta Lake?
To speed up queries that have predicates involving the partition columns, you can partition data. To partition data when you create a Delta table using SQL, specify PARTITIONED BY columns. Delta Lake supports a rich set of operations to modify tables. You can write data into a Delta table using Structured Streaming.