I am planning to do continuous migration of RDS to Redshift using DMS. As per the docs, it states if the target is redshift , DMS uses a S3 bucket to temporarily store the data before copying to redshift. I could not find any document confirming if this S3 bucket is temporary (used only for initial copying) and is deleted once the copying is done. (https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.Redshift.html)
Any thoughts on this?
Probably you've figured out the answer already. If not, yes it does create a bucket and the contents are deleted but the bucket itself is not deleted. Generally the name of the bucket starts with dms-.
Also there is an option to provide a custom bucket.
Related
I need to copy large files ( may be even greater than 50 GB) from one S3 bucket to other S3 bucket ( event based). I am planning to use s3.Object.copy_from to do this inside Lambda ( using boto3).
I wanted to see if anyone has tried this? will this have any performance issue for larger files (100 GB etc.) causing Lambda timeout?
If yes, is there any alternate option ? ( I am trying to use code since I might need to do some other additional logic like rename file, move source file to archive etc.).
Note- I am also exploring AWS S3 Replication options, but looking for other solutions in parallel.
You can use AWS S3 replication feature.
It supports key prefix and API filtering as well.
I am using an AWS EMR compute cluster (version 5.27.0) , which uses S3 for data persistence.
This cluster both reads and writes to S3.
S3 has an issue of eventual consistency, because of which after writing data, it cannot be immediately listed. Due to this I use EMRFS with DynamoDB to store newly written paths for immediate listing.
Problem now is that I have to set a retention policy on S3, because of which data more than a month old will get deleted from S3. However, in doing so , the data does not get deleted from EMRFS DynamoDB table, leading to consistency issues.
My question is , how can I ensure that on setting the retention policy in S3, the same paths get deleted from the DynamoDB table?
One naive solution I have come up with is to define a Lambda, which fires periodically, and sets TTL of say 1 day on the DynamoDB records manually. Is there a better approach than this ?
You can configure DynamoDB with same expiration policy as your S3 objects have
https://aws.amazon.com/blogs/aws/new-manage-dynamodb-items-using-time-to-live-ttl/
and in this case, you ensure both DynamoDB and S3 have the same existing objects
I am using s3 bucket to store my data. And I keep pushing data to this bucket every single day. I wonder whether there is feature I can compare the files different in my bucket between two date. I not, is there a way for me to build one via aws cli or sdk?
The reason I want to check this is that I have a s3 bucket and my clients keep pushing data to this bucket. I want to have a look how much data they pushed since the last time I load them. Is there a pattern in aws support this query? Or do I have to create any rules in s3 bucket to analyse it?
Listing from Amazon S3
You can activate Amazon S3 Inventory, which can provide a daily file listing the contents of an Amazon S3 bucket. You could then compare differences between two inventory files.
List it yourself and store it
Alternatively, you could list the contents of a bucket and look for objects dated since the last listing. However, if objects are deleted, you will only know this if you keep a list of objects that were previously in the bucket. It's probably easier to use S3 inventory.
Process it in real-time
Instead of thinking about files in batches, you could configure Amazon S3 Events to trigger something whenever a new file is uploaded to the Amazon S3 bucket. The event can:
Trigger a notification via Amazon Simple Notification Service (SNS), such as an email
Invoke an AWS Lambda function to run some code you provide. For example, the code could process the file and send it somewhere.
I've created an AWS glue table based on contents of a S3 bucket. This allows me to query data in this S3 bucket using AWS Athena. I've defined an AWS Glue crawler and run it once to auto-determine the schema of the data. This all works nicely.
Afterwards, all newly uploaded data into the S3 bucket is nicely reflected in the table. (by doing a select count(*) ... in Athena.
Why then would I need to periodically run (i.e.: schedule) an AWS Glue Crawler? After all, as said, updates to the s3 bucket seem to be properly reflected in the table. Is it to update statistics on the table so the queryplanner can be optimized or something?
Crawler is needed to register new data partitions in Data Catalog. For example, your data is located in folder /data and partitioned by date (/data/year=2018/month=9/day=11/<data-files>). Each day files are coming into a new folder (day=12, day=13 etc). To make new data available for querying these partitions must be registered in Data Catalog which can be done by running a crawler. Alternative solution is to run 'MSCK REPAIR TABLE {table-name}' in Athena.
Besides that crawler can detect a change in schema and make appropriate actions depending on your configuration.
I have some data stored in S3 . I need to clone/copy this data periodically from S3 to Redshift cluster. To do bulk copy , I can use copy command to copy from S3 to redshift.
Similarly is there any trivial way to copy data from S3 to Redshift periodically .
Thanks
Try using AWS Data Pipeline which has various templates for moving data from one AWS service to other. The "Load data from S3 into Redshift" template copies data from an Amazon S3 folder into a Redshift table. You can load the data into an existing table or provide a SQL query to create the table. The Redshift table must have the same schema as the data in Amazon S3.
Data Pipeline supports pipelines to be running on a schedule. You have a cron style editor for scheduling
AWS Lambda Redshift Loader is a good solution that runs a COPY command on Redshift whenever a new file appears pre-configured location on Amazon S3.
Links:
https://aws.amazon.com/blogs/big-data/a-zero-administration-amazon-redshift-database-loader/
https://github.com/awslabs/aws-lambda-redshift-loader
I believe Kinesis Firehose is the simplest way to get this done. Simply create a Kinesis Forehose stream, point it a a specific table in your Redshift cluster, write data to the stream, done :)
Full setup procedure here:
https://docs.aws.amazon.com/ses/latest/DeveloperGuide/event-publishing-redshift-firehose-stream.html
Kinesis option works only if redshift is publicly accessible.
You can use copy command with lambda. You can configure 2 lambdas. One will create a manifest file for you upcoming new data and another will read from that manifest for load it on redshift with Redshift data api.