2020年12月16日 · Some optimization on the parquet format though will not be very effective this way. I would use delta, just for the advanced features. It is very handy if there is a scenario where the data is updating over time, not just appending. Specially nice feature that you can read the delta tables as of a given point in time they existed. SQL as of syntax

2023年10月12日 · Delta and Parquet are indeed two different file formats. Delta format uses Parquet as its underlying storage format. So, Delta-Parquet means that data is stored using Delta Lake, which organizes the data into Parquet files while adding features like ACID transactions, versioning, and schema enforcement on top.

2022年10月23日 · Delta Lake is the default storage format.I understand how to convert a parquet to Delta. My question is is there any way to revert it back to parquet.Any options ? What I need is I want single parquet file while writing .Do not need the extra log file !

2023年7月24日 · The code reads Parquet files from Azure Data Lake Storage (ADLS) into Spark DataFrames, and then register those DataFrames as Delta tables. Delta is the extension of Parquet files that provides additional features like ACID transactions, schema evolution, and more. The DataFrames are written back to the store in Delta format using the write method.

2024年3月17日 · I am using Azure SQL with files in Delta-Parquet format stored in a Azure Data Lake Storage and also in a regular Storage account. Does Azure SQL support Delta external tables? I looked at Fabric, Azure Data Explorer documentation and they are both supporting it.

2022年2月17日 · Delta, also stores the file in parquet format, along with some other metadata and version history, thus needs to store some history data too. So you already answered your question, that if you need to optimize your storage, and always overwrite the entire dataset, I would suggest to go ahead with normal parquet.

2024年1月29日 · For parquet format, we have multiple .parquet files, all of them together representing the whole dataset. For delta, we have multiple "versions" of parquets. Does each of them representing the whole dataset? Or more correctly, each of them contains different state of the dataset (e.g. snapshot). How Vacuum deals with these .parquet files in ...

2021年1月27日 · I have data written in Delta on HDFS. From what I understand, Delta is storing the data as parquet, just has an additional layer over it with advanced features. But when reading data with Pyspark, I get a different result if dataframe is read with spark.read.parquet() or spark.read.format('delta').load()

2024年5月28日 · Currently spark write version 1.0 of parquet when creating delta table. Background-delta writes parquet 1.0 version files which do not support nanoseconds precision timestamps. In short, I currently face data loss when creating delta table from my source files. The newer parquet format version- >2.0 supports that. github link

I was trying to write a parquet file with delta encoding. This page, states that parquet supports three types of delta encoding: (DELTA_BINARY_PACKED, DELTA_LENGTH_BYTE_ARRAY, DELTA_BYTE_ARRAY). Since spark, pyspark or pyarrow do not allow us to specify the encoding method, I was curious how one can write a file with delta encoding enabled?