![]() ![]() The new whenNotMatchedBySourceUpdate is critical to allow Jim to be updated to have a status of inactive. Here’s the existing customer table.Ĭopy DeltaTable.forPath(spark, "tmp/customers").toDF().show() Imagine you’re running this analysis on April 12th, 2023. A customer will be considered active if they’ve gone to your store in the last month. Suppose you have the following target table with customers, their ages, the last time they were seen in your store, and an active flag. “When not matched by source” enables you to UPDATE or DELETE rows in the target table that do not have corresponding records in the source table. Previously only two clauses were supported “when matched” which allows updating or deleting a target row, and “when not matched” which allows inserting a source row. The merge command enables updating an existing Delta table (target) with information in a source table. “when not matched by source” clauses for the Merge command Convert to Delta is best used for one-time conversions when you don’t plan to update the source table in the source format anymore. Note that any subsequent Delta operations could corrupt the Iceberg source table and will not update the Iceberg metadata. You can convert it to a Delta table with the following command:Īfter performing CONVERT TO DELTA the table is now a Delta table and can take advantage of all the awesome Delta Lake features. Suppose you have an Iceberg table named some_table that’s stored at /some/path/some_table. It performs a one-time conversion to the Delta Lake format and also supports converting Parquet tables. In Delta 2.3 you can easily convert Iceberg tables to the Delta Lake format with CONVERT TO DELTA. ![]() Convert Iceberg to Delta LakeĬompanies are looking to migrate from Iceberg to Delta Lake to get access to better performance and reliability, as explained in the Analyzing and Comparing Lakehouse Storage Systems paper. We’ll dive into the details of cloning in a future blog post and how it can be used along with CONVERT TO DELTA. Creating a shallow clone means you can run arbitrary operations on the cloned table without corrupting the production table or disrupting any production workloads. For example, shallow clone can be used to experiment and test on a production table. Shallow clone is useful when you want to continue updating the source table but want to read or write to an independent copy of it. Delta tables are normally constructed of Parquet files and a colocated transaction log, as illustrated in the following diagram: Let’s look at the architecture of a Delta table to get a better understanding of how shallow clones work. You can create a shallow clone of a Parquet table, a Delta table, or even an Iceberg table. The Delta Lake SHALLOW CLONE command creates a Delta table in a new location while leaving the existing data files in their current location. This post will show you why you should upgrade to Delta 2.3 and how to take advantage of these amazing features. The features outlined below are just a few of the many features included in the release - see the Delta Lake 2.3 Release Notes for more. This new release makes it easier to transition from Iceberg to Delta Lake, write advanced MERGE statement logic, query the change data feed, and much more. With the release of 2.3, Delta now has even more capabilities that makes deploying Lakehouses super easy. The Delta 2.0 release has been wildly successful with widespread adoption and we continue to build upon this success. Delta Lake continues to be the best open source storage format for the lakehouse.
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