Currently, my company uses SSIS and BCP to export data from SQL Server to CSV files. However, we are only able to create a single file per SQL table (due to the limitations of BCP). Most of these files are quite large; if I am correct, they are too large to get the best performance when loading them into Snowflake. On their website, they state that we should be working with multiple gzip files to offer the best performance.
I am wondering how other people made this work? Splitting up the CSV to multiple files and zipping them? Any good tools that can do this during export from SSIS?
I'd keep the current process that exports the large .csv files using SSIS, then run 7zip via command line to create a split gzip set for each text file, either within the SSIS package or via Powershell.
The -v switch is used to specify the volume size.
https://sevenzip.osdn.jp/chm/cmdline/switches/volume.htm
You may be able to start importing/uploading the completed chunks before the later ones are finished to pick up some additional time savings, but I've not tested that.
Related
I'm trying to connect to and run queries on two large, locally-stored SQL databases with file extensions like so:
filename.sql.zstd.part
filename2.sql.zstd
My preference is to use the RMySQL package- however i am finding it hard to find documentation of a) how to access locally stored SQL files, and b) how to deal with the zstd extension.
This may be very basic but help is appreciated!
Seems like you have problems understanding the file extensions.
filename.sql.zstd.part
.part usually means you are downloading a file from the internet, but the download isn't complete yet (so downloads that are in progress or have been stopped)
So to get from filename.sql.zstd.part to filename.sql.zstd you need to complete your download
.zstd means it is a compressed file (to save disk space). You need a decompression program to get from filename.sql.zstd to filename.sql
The compression algorithm used is called Zstandard so you need a decompressor specifically for this program. Look here https://facebook.github.io/zstd/ for such a program.
There was also once an R package for this - but it has been archived. But you could also download an older version
(https://cran.r-project.org/web/packages/zstdr/index.html)
In filename.sql is actually not a database. In an .sql file are usually SQL statements for creating / modifying database structures. You'd have to install a database e.g. MariaDB and then import this .sql file to actually really have the files in a database on your computer. And then you would access this database via R.
I’m using mosaic decisions data flow feature to read a file from Azure blob, do a few transformations and write that data back to Azure. It worked fine except that in the output file path I have given, it created a folder and I can see many files with some strange “part-000” etc in their names. What I need is a single file in that output location – Not many. Is there a way around this?
Mosaic-Decisions uses apache spark as its backend execution engine. In Spark, the dataframe read is split into multiple partitions and these partitions are written to the output location in parallel. That's the reason it creates multiple files at the target location with "part-0000", "part-0001" etc. (part here represents partition).
The workaround on this is to check "combine-output-files-into-one" in writer node. This will combine all of the part files into one big file. But use this with caution and only if you really need a single file - as this will come with a performance tradeoff.
I am having 220 millions of raw files in AWS s3 which I considering to merge all into a single file which estimate around 10 terabyte. The merge file will be serve as a fact table but in file format for reporting purposes for the audit.
The raw files are source data from an application. If there is any new data changes to the application, the contain of the file will be change.
I would like to ask is anybody come across this end to end process for this user case?
s3--> ETL (file merging)--> s3 --> reporting (tableau)
I haven't personally tried it, but this is kind of what Athena is made for... Skipping your ETL process, and querying directly from the files. Is there a reason you are dumping this all into a single file instead of keeping it dispersed? Rewriting a 10TB file over and over again is very expensive and time consuming... I'd personally at least investigate keeping the files 1-1 with the source files.
Create a s3 trigger that fires when a file is rewritten on s3
Create a Lambda that creates your "audit ready" report files on s3
Use AWS Athena to query those report files
Tableau connector to Athena for your reports
We have several large CSV files in Azure Data Lake Store that were created using the Append method of the .NET API. Recently, we switched over to ConcurrentAppend for performance reasons. Since ConcurrentAppend and Append cannot be used interchangeably, the switch required us to create a new folder structure for the files, to make sure that the ConcurrentAppend would never hit any files created using Append.
However, our downstream application needs to load all data, both from before and after the switch. Instead of changing our application, we wanted to join the files (using the PowerShell SDK Join-AzureRmDataLakeStoreItem cmdlet), but the documentation does not specify whether files joined this way can be written to by ConcurrentAppend after the join. I suspect that we will face issues, since we are going to join files created by both methods (maybe it's not even possible to do the join?)
So my questions are as follows:
Can ConcurrentAppend write to a file that has been joined using Join-AzureRmDataLakeStoreItem, even if one or more of the source files have been created using Append?
If not, we will use U-SQL to combine the files, but can ConcurrentAppend write to a file that has been outputted from a U-SQL job?
If not, do we have any other options than executing a local script (using the .NET API for example), which will read all files, and write a new set of files back to the lake using only ConcurrentAppend?
Cost is a concern, which is why we prefer to use the PowerShell cmdlet if possible, and would like to avoid the last option.
At present after the join operation, no append operations can be executed on the file. We are currently working on a feature to remove this limitation. However, at present after concatenating files, the appends will not work.
I've been looking around for a lightweight, scaleable solution to enrich a CSV file with additional metadata from a database. Each line in the CSV represents a data item and the columns the metadata belonging to that item.
Basically I have a CSV extract and I need to add additional metadata from a database. The metadata can be accessed via ODBC or REST API call.
I have a number of options in my head but I'm looking for other ideas. My options are as follows:
Import the CSV into a database table, apply the additional metadata with sql UPDATE statements by finding the necessary metadata with SELECT statements, and then export the data back into CSV format. For this solution I was thinking to use an ETL tool which may be a bit heavyweight to tackle this problem.
I also thought about a NodeJS based solution where I read the CSV in, call web service to get the metadata and write back the data into the CSV file. The CSV can be however quite large with potentially tens of thousands of rows so this could be heavy on memory or in case of line-by-line processing not very performant.
If you have a better solution in mind, please post. Many thanks.
I think you've come up with a couple of pretty good ideas here already.
Running with your first suggestion using an ETL tool to enrich your CSV files, you should check out https://github.com/streamsets/datacollector
It's a continuous ingestion approach, so you could even monitor a directory of CSV files to load as you get them. While there's no specific functionality yet for doing lookups in a database, its certainly possible in a number of ways (including writing your own custom logic in Java, or a script in python or JavaScript).
*Full disclosure I work on this project.