How load delta table in Synapse using delta table path?
Use Synapse Optimize command optimize {tablename}
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now that we can query delta lake tables from Athena without having to generate manifest files, does it also take care of automatically syncing the underlying partitions? I see MSCK REPAIR is not supported for delta tables. Or do I need to use a Glue crawler for this?
I am confused because of these two statements from Athena documentation
You can use Amazon Athena to read Delta Lake tables stored in Amazon S3 directly without having to generate manifest files or run the MSCK REPAIR statement.
Athena synchronizes table metadata, including schema, partition columns, and table properties, to AWS Glue if you use Athena to create your Delta Lake table. As time passes, this metadata can lose its synchronization with the underlying table metadata in the transaction log. To keep your table up to date, you can use the AWS Glue crawler for Delta Lake tables.
I am struggling to understand something here and im sure the answer is simple....when I run this command in a Databrick note book:
(df.write
.format("delta")
.option("path", "/file/path/location")
.saveAsTable("MyTable"))
It creates a delta table. Okay Great!
but if I run the same command in azure synapse spark notebook....it creates a table...
Does Synapse now support delta tables? According to this stack overflow post it does not.
So my question is...whats the difference?
Thanks in advance!
Azure Synapse Analytics has a number of engines such as Spark and SQL. Synapse Spark pools support Delta Lake. Synapse Serverless SQL pools recently supports reading from Delta Lake. Synapse Dedicated SQL Pools do not support Delta Lake at this moment.
That other StackOverflow answer is out of date. It was only discussing Synapse Serverless SQL Pools. I added a comment asking him to update it.
I have files loaded into an azure storage account gen2, and am using Azure Synapse Analytics to query them. Following the documentation here: https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/develop-storage-files-spark-tables, I should be able to create a spark sql table to query the partitioned data, and thus subsequently use the metadata from spark sql in my sql on demand query to given the line in the doc: When a table is partitioned in Spark, files in storage are organized by folders. Serverless SQL pool will use partition metadata and only target relevant folders and files for your query
My data is partitioned in ADLS gen2 as:
Running the query in a spark notebook in Synapse Analytics returns in just over 4 seconds, as it should given the partitioning:
However, now running the same query in the sql on demand sql side script never completes:
This result and extreme reduction in performance compared to spark pool is completely counter to what the documentation notes. Is there something I am missing in the query to make sql-on demand use the partitions?
Filepath() and filename() functions can be used in the WHERE clause to filter the files to be read. Which that you can achieve the prunning you have been looking for.
I am trying to move large amounts of reference data from on-prem SQL server to Delta lake to be used in databricks processing. To move this data, I am trying use Azure Data Factory via simple Copy data activity. but as soon as I start the pipeline I get the below error. I googled this error but could not find any matches.
Note that sink delta table is not present in the delta lake ? does this error mean that I have create tables manually before moving data to delta lake ?
Operation on target Copy data1 failed: ErrorCode=AzureDatabricksTableIsNotDeltaFormat,The table benefit is not delta format.
Resolved this using data flow rather than copy tasks.
I've created a DataFrame which I would like to write / export next to my Azure DataLake Gen2 in Tables (need to create new Table for this).
In the future I will also need to update this Azure DL Gen2 Table with new DataFrames.
In Azure Databricks I've created a connection Azure Databricks -> Azure DataLake to see my my files:
Appreciate help how to write it in spark / pyspark.
Thank you!
I would suggest instead of writing data in parquet format, go for Delta format which internally uses Parquet format but provide other features like ACID transaction.The syntax would be
df.write.format("delta").save(path)