We have a requirement to get a .csv files from a bucket which is a client location (They would provide the S3 bucket info and other information required). Every day we need to pull this data into our S3 bucket so we can process it further. Please suggest the best way/technology that we can use to achieve the result.
I am planning to do it by Python boto (or Pandas or Pyspark) or Spark; reason being, once we get this data it might be processed further.
You can try the S3 cross account object copy using the S3 copy option. This is more secure and the suggested one. Please go through the below link for more details. It also works for same account different buckets. After copying then you can trigger some lambda function with custom code(python) to do the processing of the .csv files.
How to copy Amazon S3 objects from one AWS account to another by using the S3 COPY operation
If your customer keeps the data in an s3 bucket to which your account has been granted access to it, then it should be possible to use the .csv files as a direct source of data for a spark job. Use the s3a://theirbucket/nightly/*.csv as the RDD source, and save it to s3a://mybucket/somewhere, ideally in a format other than CSV (Parquet, ORC, ...). This lets you do some basic transformation of the format into one easier to work with.
If you just want the raw CSV files, that S3 Copy operation is what you need, as it copies the data within S3 itself (6+MiB/s if in the same S3 location), and not needing any of your own VMs involved.
Related
I have data available in an S3 bucket we don't own, with a zipped folder containing files for each date.
We are using Snowflake as our data warehouse. Snowflake accepts gzip'd files, but does not ingest zip'd folders.
Is there a way to directly ingest the files into Snowflake that will be more efficient than copying them all into our own S3 bucket and unzipping them there, then pointing e.g. Snowpipe to that bucket? The data is on the order of 10GB per day, so copying is very doable, but would introduce (potentially) unnecessary latency and cost. We also don't have access to their IAM policies, so can't do something like S3 Sync.
I would be happy to write something myself, or use a product/platform like Meltano or Airbyte, but I can't find a suitable solution.
How about using SnowSQL to load the data into Snowflake, and using Snowflake stage table/user/named stage to hold files at stages.
https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage.html
I had a similar use case. I use an event based trigger that runs a Lambda function everytime there is a new zipped file in my S3 folder. The Lambda functions opens the zipped files, gzips each individual file and re-uploads them to a different S3 folder. Here's the full working code: https://betterprogramming.pub/unzip-and-gzip-incoming-s3-files-with-aws-lambda-f7bccf0099c9
I need to download a big number of files (around 400k) files from an S3 bucket. I have the paths stored in a csv file. Some of the paths may not exist.
The two options i see are:
Use the foreach activity and somehow pass the contents of the file there. But i think that this would flood my monitor pane with a huge number of runs, and it feels like it is meant to be for smaller pipelines.
Use the listOfFiles option which is supported in the S3 source. The problem with this approach is that the list must be in the S3 bucket and cannot be loaded from Azure Datalake Gen2 (anybody knows why, please let me know as well).
I have tried using the listOfFiles way, but the pipeline fails once it finds the first missing file. The fault tolerance options contain a "skip missing file" option but it is defined as "Skip the files if it is being deleted from source store during the data movement", so it is of no use to me.
I don't want to download more files than needed, so copying the bucket as-is is not an option. How can i approach this issue with ADF? I'm looking for a solution that uses the predefined transformations, ideally i would like to not involve Azure Batch or Azure Functions for such a simple task.
I have a project in AWS to insert data from some files, which will be in S3, to Redshift. The point is that the ETL has to be scheduled each day to find new files in S3 and then check if those files are correct. However, this has to be done with custom code as the files can have different formats depending of their kind, provider, etc.
I see that AWS Glue allows to schedule, crawl and do the ETL. However I'm lost at how to one can create its own code for the ETL and parse the files to check the correctness before ending up doing the copy instruction from S3 to Redshift. Do you know if that can be done and how?
Another issue is that if the correctness is OK then, the system should upload the data from S3 to a web via some API. But if it's not the file should be left into an ftp email. Here again, do you know if that can be done as well with the AWS Glue and how?
many thanks!
You can write your glue/spark code, upload it to s3 and create a glue job referring to this script/library. Anything you want to write in python can be done in glue. its just a wrapper around spark which in turn uses python....
The documentation for the Redshift COPY command specifies two ways to choose files to load from S3, you either provide a base path and it loads all the files under that path, or you specify a manifest file with specific files to load.
However in our case, which I imagine is pretty common, the S3 bucket periodically receives new files with more recent data. We'd like to be able to load only the files that haven't already been loaded.
Given that there is a table stl_file_scan that logs all the files that have been loaded from S3, it would be nice to somehow exclude those that have successfully been loaded. This seems like a fairly obvious feature, but I can't find anything in the docs or online about how to do this.
Even the Redshift S3 loading template in AWS Data Pipeline appears to manage this scenario by loading all the data -- new and old -- to a staging table, and then comparing/upserting to the target table. This seems like an insane amount of overhead when we can tell up front from the filenames that a file has already been loaded.
I know we could probably move the files that have already been loaded out of the bucket, however we can't do that, this bucket is the final storage place for another process which is not our own.
The only alternative I can think of is to have some other process running that tracks files that have been successfully loaded to redshift, and then periodically compares that to the s3 bucket to determine the differences, and then writes the manifest file somewhere before triggering the copy process. But what a pain! We'd need a separate ec2 instance to run the process which would have it's own management and operational overhead.
There must be a better way!
This is how I solved the problem,
S3 -- (Lambda Trigger on newly created Logs) -- Lambda -- Firehose -- Redshift
It works at any scale. With more load, more calls to Lambda, more data to firehose and everything taken care automatically.
If there are issues with the format of the file, you can configure dead letter queues, events will be sent there and you can reprocess once you fix lambda.
Here I would like to mention some steps that includes process that how to load data in redshift.
Export local RDBMS data to flat files (Make sure you remove invalid
characters, apply escape sequence during export).
Split files into 10-15 MB each to get optimal performance during
upload and final Data load.
Compress files to *.gz format so you don’t end up with $1000
surprise bill :) .. In my case Text files were compressed 10-20
times.
List all file names to manifest file so when you issue COPY command
to Redshift its treated as one unit of load.
Upload manifest file to Amazon S3 bucket.
Upload local *.gz files to Amazon S3 bucket.
Issue Redshift COPY command with different options.
Schedule file archiving from on-premises and S3 Staging area on AWS.
Capturing Errors, setting up restart ability if something fails
Doing it easy way you can follow this link.
In general compare of loaded files to existing on S3 files is a bad but possible practice. The common "industrial" practice is to use message queue between data producer and data consumer that actually loads the data. Take a look on RabbitMQ vs Amazon SQS and etc..
Use Case:
Upload multiple files into a cloud storage bucket, and then use that data as a source to a bigquery import. Use the name of the bucket as the metadata to drive which sharded table the data should go into.
Question:
In order to prevent partial import to the bigquery table, ideally, I would like to do the following,
Upload the files into a staging bucket
Verify all files have been uploaded correctly
Rename the staging bucket to its final name (for example, gs://20130112)
Trigger the bigquery import to load the bucket into a sharded table
Since gsutil does not seem to support bucket rename, what are the alternative ways to accomplish this?
Google Cloud Storage does not support renaming buckets, or more generally an atomic way to operate on more than one object at a time.
If your main concern is that all objects were uploaded correctly (as opposed to needing to ensure the bucket content is only visible once all objects are uploaded), gsutil cp supports that -- if any object fails to upload, it will report the number that failed to upload and exit with a non-zero status.
So, a possible implementation would be a script that runs gsutil cp to upload all your files, and then checks the gsutil exit status before creating the BigQuery table load job.
Mike Schwartz, Google Cloud Storage team
Object names are actually flat in Google Cloud Storage; from the service's perspective, '/' is just another character in the name. The folder abstraction is provided by clients, like gsutil and various GUI tools. Renaming a folder requires clients to request a sequence of copy and delete operations on each object in the folder. There is no atomic way to rename a folder.
Mike Schwartz, Google Cloud Storage team