There are tons of examples to migrate data from Aurora DB to Redshift, but I couldn't find any example or documentation for migrating data from Redshift to Aurora DB. Any suggestion/example/doc for migrating data from Redshift into Aurora DB in an efficient way?
You can do it by unloading1 the data into S3 directly from RedShift and then loading it into Aurora2&3.
Redshift UNLOAD Official Docs.
Aurora MySQL LOAD FROM S3 Offical Docs.
Aurora PostgreSQL LOAD FROM S3 Official Docs.
Note that the LOAD on Aurora does not support .parquet or other colunar files, so your best shot is to unload it as .csv. Depending on the size of your tables, you might consider doing it in batches or in periods of reduced workload.
By the moment I'm writting this answer you can't use Redshift as a source for DMS, otherwise this would be the prefered way.
Related
I have an AWS Aurora MySQL database on my production environment, and a separate AWS Aurora MySQL database on my performance environment. Periodically, I'll create a copy the production database, and use the copy as my database in my Performance environment, switching out the old performance database and replacing it with the new one.
Does AWS Glue provide the ability to move data from one Aurora MySQL database to another Aurora MySQL database? Could I use it to periodically (maybe once a week) copy over data from the Prod database to the Perf database? Also, if this is possible, would I be able to selectively copy data over from the prod MySQL, without necessarily losing data that was only added on the perf MySQL?
May I suggest not to use Glue for a full copy of a database, but AWS DMS (Database Migration Service) instead.
You can do very quick 1-to-1 migrations between two databases with DMS. You spin a DMS instance (Linux server, low cost, turn it off when not in use), set up a source and a target endpoint, and a replication task, and you're good to go.
Here is a guide you can follow: https://docs.aws.amazon.com/dms/latest/sbs/chap-rdsoracle2aurora.html
We need to replicate data from an Amazon RDS (MySQL) databaes to an Aurora PostgreSQL database. Each database is on a different AWS account and region.
The data must be replicated in the PostgreSQL instance every 6 hours, so we need the guarantee that the data doesn't duplicate even if a field updated for a record.
Which method, tool, or design is better to do that? (Could be different to AWS.)
You could facilitate one of the following services provided by AWS:
Database Migration Service
Glue
The AWS Database Migration Service supports:
Using an Amazon-Managed MySQL-Compatible Database as a Source for AWS DMS
Using a PostgreSQL Database as a Target for AWS Database Migration Service
So I was searching for a solution that could let me export S3 data into Aurora Serverless. I know that the LOAD DATA request is only available for the Aurora cluster not the serverless one.
I've found some documentation about the data injection from S3 to RDS MySQL but I don't know if this still applies to Amazon Aurora MySQL.
If you're ok with temporarily putting the data on an ec2 instance, you can do it in two steps:
aws s3 cp s3://path/to/mydatafile /local/path/to/mydatafile
mysql --defaults-file=/path/to/.my.cnf -e "load data local infile '/local/path/to/mydatafile' into table sampletable"
References
StackOverflow discussion on loading data
MySQL "load data" reference
Copying from s3
Using MySQL options files
I am using AWS Aurora database and created read replica for it. I have inserted some data in that database using MYSQL workbench. Now I want to retrieve that data when i hit the API. I want to put that data into another DynamoDB database. Can anyone suggest me the way to achieve this as I am new to AWS Aurora.
Aurora is compatible to MYSQL so reading from Aurora is same as reading from MYSQL.
I'm doing some studies about Redshift and Hive working at AWS.
I have an application working in Spark, that is in local cluster, working with Apache Hive. Then we will migrate to AWS.
We found that there is a Data Warehouse solution that is Redshift.
Redshift is a Columnar database that is really fast to queries for Tb of data with no big issues. Working with Redshift will not take much time of maintaining. But I have a question, how is the performance of Redshift over Hive?
If I store a Hive with EMR, setting the storage at EMR and handle the metastore with Hive it will take this to process the data with Spark.
What is the performance of Redshift over Hive in EMR? Redshift is the best solution for Apache Spark in terms of performance?
Or Using Hive I will take much performance with spark that will compensates the maintenance time?
-------EDIT-------
Well I read more about it, and I found how Redshift will work with Spark in EMR.
According to what I saw, when you call the data from Redshift it will load the information to a S3 bucket like this:
This information I found at Databricks Blog
According this, is Hive faster than Redshift for EMR?