I have tables in a cluster at region-1 and I want to copy some of those tables in another cluster at some other region (region-2).
Till now I have used matillion and for that I have followed following steps-
Copy data to s3 from cluster-a.
Load this data from s3 to cluster-b.
Since matillion is a little bit costly for me to do work, and I want to have an alternative solution for this.
Although I have heard about CLI, Lambda and API but I am having no idea for how should I use these, since I go through this procedure on weekly basis and I want to automate this process.
The AWS Command-Line Interface (CLI) is not relevant for this use-case, because it is used to control AWS services (eg launch an Amazon Redshift database, change security settings). The commands to import/export data to/from Amazon Redshift must be issued to Redshift directly via SQL.
To copy some tables to an Amazon Redshift instance in another region:
Use an UNLOAD command in Cluster A to export data from Redshift to an Amazon S3 bucket
Use a COPY command in Cluster B to load data from S3 into Redshift, using the REGION parameter to specify the source region
You will therefore need separate SQL connections to each cluster. Any program that can connect to Redshift via JDBC would suffice. For example, you could use the standard psql tool (preferably version 8.0.2) since Redshift is based on PostgreSQL 8.0.2.
See: Connect to Your Cluster by Using the psql Tool
So, your script would be something like:
psql -h clusterA -U username -d mydatabase -c 'UNLOAD...'
psql -h clusterB -U username -d mydatabase -c 'COPY...'
You could run this from AWS Lambda, but Lambda functions only run for a maximum of five minutes, and your script might exceed that limit. Instead, you could run a regular cron job on some machine.
Related
I want to backup the REDIS data on google storage bucket as flat file, is there any existing utility to do that?
Although, I do not fully agree to idea of backing up of cache data on cloud. I was wondering if there is any existing utility rather than reinventing the wheel.
If you are using Cloud Memorystore for Redis you can simply refer to the following documentation. Notice that you can simply use the following gcloud command:
gcloud redis instances export gs://[BUCKET_NAME]/[FILE_NAME].rdb [INSTANCE_ID] --region=[REGION] --project=[PROJECT_ID]
or use the Export operation from the Cloud Console.
If you manage your own instance (e.g. you have the Redis instance hosted on a Compute Engine Instance) you could simply use the SAVE or BGSAVE (preferred) commands to take a snapshot of the instance and then upload the .rdb file to Google Cloud Storage using any of the available methods, from which I think the most convenient one would be gsutil (notice that it will require the following installation procedure) in a similar fashion to:
gsutil cp path/to/your-file.rdb gs://[DESTINATION_BUCKET_NAME]/
I am trying to find a solution to move files from an S3 bucket to Snowflake internal stage (not table directly) with Airflow but it seems that the PUT command is not supported with current Snowflake operator.
I know there are other options like Snowpipe but I want to showcase Airflow's capabilities.
COPY INTO is also an alternative solution but I want to load DDL statements from files, not run them manually in Snowflake.
This is the closest I could find but it uses COPY INTO table:
https://artemiorimando.com/2019/05/01/data-engineering-using-python-airflow/
Also : How to call snowsql client from python
Is there any way to move files from S3 bucket to Snowflake internal stage through Airflow+Python+Snowsql?
Thanks!
I recommend you execute the COPY INTO command from within Airflow to load the files directly from S3, instead. There isn't a great way to get files to internal stage from S3 without hopping the files to another machine (like the Airflow machine). You'd use SnowSQL to GET from S3 to local, and the PUT from local to S3. The only way to execute a PUT to Internal Stage is through SnowSQL.
I would like to transfer data from a table in BigQuery, into another one in Redshift.
My planned data flow is as follows:
BigQuery -> Google Cloud Storage -> Amazon S3 -> Redshift
I know about Google Cloud Storage Transfer Service, but I'm not sure it can help me. From Google Cloud documentation:
Cloud Storage Transfer Service
This page describes Cloud Storage Transfer Service, which you can use
to quickly import online data into Google Cloud Storage.
I understand that this service can be used to import data into Google Cloud Storage and not to export from it.
Is there a way I can export data from Google Cloud Storage to Amazon S3?
You can use gsutil to copy data from a Google Cloud Storage bucket to an Amazon bucket, using a command such as:
gsutil -m rsync -rd gs://your-gcs-bucket s3://your-s3-bucket
Note that the -d option above will cause gsutil rsync to delete objects from your S3 bucket that aren't present in your GCS bucket (in addition to adding new objects). You can leave off that option if you just want to add new objects from your GCS to your S3 bucket.
Go to any instance or cloud shell in GCP
First of all configure your AWS credentials in your GCP
aws configure
if this is not recognising the install AWS CLI follow this guide https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html
follow this URL for AWS configure
https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
Attaching my screenshot
Then using gsutil
gsutil -m rsync -rd gs://storagename s3://bucketname
16GB data transferred in some minutes
Using Rclone (https://rclone.org/).
Rclone is a command line program to sync files and directories to and from
Google Drive
Amazon S3
Openstack Swift / Rackspace cloud files / Memset Memstore
Dropbox
Google Cloud Storage
Amazon Drive
Microsoft OneDrive
Hubic
Backblaze B2
Yandex Disk
SFTP
The local filesystem
Using the gsutil tool we can do a wide range of bucket and object management tasks, including:
Creating and deleting buckets.
Uploading, downloading, and deleting objects.
Listing buckets and objects. Moving, copying, and renaming objects.
we can copy data from a Google Cloud Storage bucket to an amazon s3 bucket using gsutil rsync and gsutil cp operations. whereas
gsutil rsync collects all metadata from the bucket and syncs the data to s3
gsutil -m rsync -r gs://your-gcs-bucket s3://your-s3-bucket
gsutil cp copies the files one by one and as the transfer rate is good it copies 1 GB in 1 minute approximately.
gsutil cp gs://<gcs-bucket> s3://<s3-bucket-name>
if you have a large number of files with high data volume then use this bash script and run it in the background with multiple threads using the screen command in amazon or GCP instance with AWS credentials configured and GCP auth verified.
Before running the script list all the files and redirect to a file and read the file as input in the script to copy the file
gsutil ls gs://<gcs-bucket> > file_list_part.out
Bash script:
#!/bin/bash
echo "start processing"
input="file_list_part.out"
while IFS= read -r line
do
command="gsutil cp ${line} s3://<bucket-name>"
echo "command :: $command :: $now"
eval $command
retVal=$?
if [ $retVal -ne 0 ]; then
echo "Error copying file"
exit 1
fi
echo "Copy completed successfully"
done < "$input"
echo "completed processing"
execute the Bash script and write the output to a log file to check the progress of completed and failed files.
bash file_copy.sh > /root/logs/file_copy.log 2>&1
I needed to transfer 2TB of data from Google Cloud Storage bucket to Amazon S3 bucket.
For the task, I created the Google Compute Engine of V8CPU (30 GB).
Allow Login using SSH on the Compute Engine.
Once logedin create and empty .boto configuration file to add AWS credential information. Added AWS credentials by taking the reference from the mentioned link.
Then run the command:
gsutil -m rsync -rd gs://your-gcs-bucket s3://your-s3-bucket
The data transfer rate is ~1GB/s.
Hope this help.
(Do not forget to terminate the compute instance once the job is done)
For large amounts of large files (100MB+) you might get issues with broken pipes and other annoyances, probably due to multipart upload requirement (as Pathead mentioned).
For that case you're left with simple downloading all files to your machine and uploading them back. Depending on your connection and data amount, it might be more effective to create VM instance to utilize high-speed connection and ability to run it in the background on different machine than yours.
Create VM machine (make sure the service account has access to your buckets), connect via SSH and install AWS CLI (apt install awscli) and configure the access to S3 (aws configure).
Run these two lines, or make it a bash script, if you have many buckets to copy.
gsutil -m cp -r "gs://$1" ./
aws s3 cp --recursive "./$1" "s3://$1"
(It's better to use rsync in general, but cp was faster for me)
Tools like gsutil and aws s3 cp won't use multipart uploads/downloads, so will have poor performance for large files.
Skyplane is a much faster alternative for transferring data between clouds (up to 110x for large files). You can transfer data with the command:
skyplane cp -r s3://aws-bucket-name/ gcs://google-bucket-name/
(disclaimer: I am a contributor)
I'd like to upload a Hive external table to AWS Redshift directly via command line. I don't want to use the Data Pipeline. Do I have to upload the table to S3 first and then copy it to Redshift? Is there any way to do it directly?
You can load Redshift directly from remote hosts using SSH or, if you're using their EMR version of Hadoop, you can load directly from the HDFS file system.
http://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html
There are some large datasets (25gb+, downloadable on the Internet) that I want to play around with using Amazon EMR. Instead of downloading the datasets onto my own computer, and then re-uploading them onto Amazon, what's the best way to get the datasets onto Amazon?
Do I fire up an EC2 instance, download the datasets (using wget) into S3 from within the instance, and then access S3 when I run my EMR jobs? (I haven't used Amazon's cloud infrastructure before, so not sure if what I just said makes any sense.)
I recommend the following...
fire up your EMR cluster
elastic-mapreduce --create --alive --other-options-here
log on to the master node and download the data from there
wget http://blah/data
copy into HDFS
hadoop fs -copyFromLocal data /data
There's no real reason to put the original dataset through S3. If you want to keep the results you can move them into S3 before shutting down your cluster.
If the dataset is represented by multiple files you can use the cluster to download it in parallel across the machines. Let me know if this is the case and I'll walk you through it.
Mat
If you're just getting started and experimenting with EMR, I'm guessing you want these on s3 so you don't have to start an interactive Hadoop session (and instead use the EMR wizards via the AWS console).
The best way would be to start a micro instance in the same region as your S3 bucket, download to that machine using wget and then use something like s3cmd (which you'll probably need to install on the instance). On Ubuntu:
wget http://example.com/mydataset dataset
sudo apt-get install s3cmd
s3cmd --configure
s3cmd put dataset s3://mybucket/
The reason you'll want your instance and s3 bucket in the same region is to avoid extra data transfer charges. Although you'll be charged in bound bandwidth to the instance for the wget, the xfer to S3 will be free.
I'm not sure about it, but to me it seems like hadoop should be able to download files directly from your sources.
just enter http://blah/data as your input, and hadoop should do the rest. It certainly works with s3, why should it not work with http?