I know how to write and read from a file in S3 using boto. I'm wondering if there is a way to append to a file without having to download the file and re-upload an edited version?
There is no way to append data to an existing object in S3. You would have to grab the data locally, add the extra data, and then write it back to S3.
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I am trying to bulk copy tables from SnowFlake to postgreSQL. From SnowFlake, I was able to extract tables in CSV format using COPY. The COPY compresses the extract in gz format in aws s3.
Now the second step is to load these files in postgreSQL. I am planning to use postgreSQL COPY utility to ingest the data. However, I don't want to unzip the files. I would rather like to buffer the data directly from gz files and give the buffer file as input to the psycopg2 copy_from function.
Is there a way to parse gz files in AWS S3 using python? Thanks in advance!
Is there any way to read a parquet file schema from Node.JS?
If yes, how?
I saw that there is a lib, parquetjs but as I saw it from the documentation it can only read and write the contents of the file.
After some investigation, I've found that the parquetjs-lite can do that. It does not read the whole file, just the footer and then it extracts the schema from it.
It works with a cursor and the way I saw it there is two s3.getobject calls, one for the size and one for the given data.
I have copied the files from remote server to Azure Blob Storage using Azure Data Factory Copy Activity (Binary file copy). Those files are json files and txt files. I would like to change the encoding of the files to UTF-16.
I know its possible to change the encoding while copying the text files from remote server by just mentioning the encoding as UTF-16 in sink side in Copy Activity.I have implemented the copy activity which takes every files as txt file and it was working file. Sometimes, i get some error related to row delimiter and i changed the implementation to binary copy.Now, i would like to change the encoding of those files from UTF-8 to UTF-16. I couldn't find any way to do it.
Any help/suggestions would be appreciated.
If a file is stored in blob storage, you cannot directly change it's content-encoding, even if you set the blob property of content-encoding.
The way(via code or manually) to do this is that you should download it -> encode it with UTF-16 -> then upload it again.
Is there any way one can append two files in GCS, suppose file one is a full
load and second file is an incremental load. Then what's the way we can append
the two?
Secondly, using gsutil compose will append the two files including the attributes
names as well. So, in the final file I want the data of the two files.
You can append two separate files using compose in the Google Cloud Shell and rename the output file as the first file, like this:
gsutil compose gs://bucket/obj1 [gs://bucket/obj2 ...] gs://bucket/obj1
This command is meant for parallel uploads in which you divide a large object file in smaller objects. They get uploaded to Google Cloud Storage and then you can append them to get the original file. You can find more information on Composite Objects and Parallel Uploads.
I've come up with two possible solutions:
Google Cloud Function solution
The option I would go for is using a Cloud Function. Doing something like the following:
Create an empty bucket like append_bucket.
Upload the first file.
Create a Cloud Function to be triggered by new uploaded files on the
bucket.
Upload the second file.
Read the first and the second file (you will have to download them as string first).
Make the append operation.
Upload the result to the bucket.
Google Dataflow solution
You can also do it with Dataflow for BigQuery (keep in mind it’s still in beta).
Create a BigQuery dataset and table.
Create a Dataflow instance, from the template Cloud Storage Text to BigQuery.
Create a Javascript file with the logic to transform the text.
Upload your files in Json format to the bucket.
Dataflow will read the Json file, execute the Javascript code and append the new data to the BigQuery dataset.
At last, export the BigQuery query result to Cloud Storage.
I need a microcontroller to read a data file that's been stored in a Zip file (actually a custom OPC-based file format, but that's irrelevant). The data file has a known name, path, and size, and is guaranteed to be stored uncompressed. What is the simplest & fastest way to find the start of the file data within the Zip file without writing a complete Zip parser?
NOTE: I'm aware of the Zip comment section, but the data file in question is too big to fit in it.
I ended up writing a simple parser that finds the file data in question using the central directory.