bucket | basic containers | Continuous Deployment library

I have three .snappy.parquet files stored in an s3 bucket, I tried to use pandas.read_parquet() but it only work when I specify one single parquet file, e.g: df = pandas.read_parquet("s3://bucketname/xxx.snappy.parquet"), but if I don't specify the filename df = pandas.read_parquet("s3://bucketname"), this won't work and it gave me error: Seek before start of file.

I did a lot of reading, then I found this page

it suggests that we can use pyarrow to read multiple parquet files, so here's what I tried:

I have users in a Cognito user pool, some of whom are in an Administrators group. These administrators need to be allowed to read/write to a specific S3 bucket, and other users must not.

To achieve this, I assigned a role to the Administrators group which looked like this:

I have 2 buckets on the S3 service. I have a lambda function "create-thumbnail" that triggered when an object is created into an original bucket, if it is an image, then resize it and upload it into the resized bucket.

Everything is working fine, but the function doesn't trigger when I upload files more than 4MB on the original bucket.

Function configurations are as follow,

• Timeout Limit: 2mins
• Memory 10240
• Trigger Event type: ObjectCreated (that covers create, put, post, copy and multipart upload complete)

4 rows are stored here. Each new row is displayed when the LOAD MORE button is pressed. Each row is displayed as it should and the code works without problems. When the end is reached, a Go button should appear pointing to another page.

What's the best way to do this? I have included the code as an example.

I have a Python Apache Beam streaming pipeline running in Dataflow. It's reading from PubSub and writing to GCS. Sometimes I get errors like "Error in _start_upload while inserting file ...", which comes from:

I need to split my products into a total of 120 predefined price clusters/buckets. These clusters can overlap and look somewhat like that:

As I dont want to write down all of these strings manually: Is there a convenient way to do this in M or DAX directly using a bit of code?

Thanks in advance! Dave

I'm running gitlab-ce on-prem with min.io as a local S3 service. CI/CD caching is working, and basic connectivity with the S3-compatible minio is good. (Versions: gitlab-ce:13.9.2-ce.0, gitlab-runner:v13.9.0, and minio/minio:latest currently c253244b6fb0.)

Is there additional configuration to differentiate between job-artifacts and pipeline-artifacts and storing them in on-prem S3-compatible object storage?

In my test repo, the "build" stage builds a sparse R package. When I was using local in-gitlab job artifacts, it succeeds and moves on to the "test" and "deploy" stages, no problems. (And that works with S3-stored cache, though that configuration is solely within gitlab-runner.) Now that I've configured minio as a local S3-compatible object storage for artifacts, though, it fails.

I'm pretty new to AWS Lambda functions.

I'm trying to get a .xlsx file from a website and put it on a private Amazon S3 bucket.

The following code leads to a timeout when running the put_object function and I don't know how doing now ... What am I doing wrong? I'm so close...
This code works on our backend to write to a file.

I have a current solution for my s3 bucket where I store exe files with specific versions like:

• s3://my-bucket
  • /latest
    • my-exe-v1.xxx3.exe
  • /history
    • my-exe-v1.xxx2.exe
    • my-exe-v1.xxx1.exe
    • ...

Is it possible for versionned bucket to set the version name ? In my case it would allow to get the bucket like:

• s3://my-bucket
  • my-exe.exe -> contains versions (v1.xxx1,v1.xxx2,v1.xxx3, ...)