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....
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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
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.
I'm using Marklogic 8.0.6 and we also have JSON documents in it. I need to extract a lot of data from Marklogic and store them in AWS S3. We tried to run "mlcp" locally and them upload the data to AWS S3 but it's very slow because it's generating a lot of files.
Our Marklogic platform is already connected to S3 to perform backup. Is there a way to extract a specific database in aws s3 ?
It can be OK for me if I have one big file with one JSON document per line
Thanks,
Romain.
I don't know about getting it to s3, but you can use CORB2 to extract MarkLogic documents to one big file with one JSON document per line.
S3:// is a native file type in MarkLogic. So you can also iterate through all your docs and export them with xdmp:save("s3://...).
If you want to make agrigates, then You may want to marry this idea into Sam's suggestion of CORB2 to control the process and assist in grouping your whole database into multiple manageable aggregate documents. Then use a post-back task to run xdmp-save
Thanks guys for your answers. I do not know about CORB2, this is a great solution! But unfortunately, due to bad I/O I prefer a solution to write directly on s3.
I can use a basic Ml query and dump to s3:// with native connector but I always face memory error even launching with the "spawn" function to generate a background process.
Do you have any xquey example to extract each document on s3 one by one without memory permission?
Thanks
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..
We are experiencing problems with files produced by Java code which are written locally and then copied by the Data Pipeline to S3. The error mentions file size.
I would have thought that if multipart uploads is required, then the Pipeline would figure that out. I wonder if there is a way of configuring the Pipeline so that it indeed uses multipart uploading. Because otherwise the current Java code which is agnostic about S3 has to write directly to S3 or has to do what it used to and then use multipart uploading -- in fact, I would think the code would just directly write to S3 and not worry about uploading.
Can anyone tell me if Pipelines can use multipart uploading and if not, can you suggest whether the correct approach is to have the program write directly to S3 or to continue to write to local storage and then perhaps have a separate program be invoked within the same Pipeline which will do the multipart uploading?
The answer, based on AWS support, is that indeed 5 gig files can't be uploaded directly to S3. And there is no way currently for a Data Pipeline to say, "You are trying to upload a large file, so I will do something special to handle this." It simply fails.
This may change in the future.
Data Pipeline CopyActivity does not support files larger than 4GB. http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-copyactivity.html
This is below the 5GB limit imposed by S3 for each file-part put.
You need to write your own script wrapping AWS CLI or S3cmd (older). This script may be executed as a shell activity.
Writing directly to S3 may be an issue as S3 does not support append operations - unless you can somehow write multiple smaller objects in a folder.