I'm trying to setup a workflow to backup Accounts & Contact objects from Salesforce to S3 via AWS Appflow. Perhaps, I'm able to setup the connection and able to backup the files on-demand.
However, for restoration I would like to import the mapping using .csv file and below are sample first 3 lines (using comma-separator source & destination fields).
Name, Name
Type, Account Type
AccountNumber, Account Number
But Appflow is unable to import as " Couldn't parse rows from the file" - Am I missing something ?
This was bug on AWS side and it taken up ! Workaround is to do manual mapping instead of external CSV; make sure the source field attributes match with the corresponding objects in Salesforce.
Related
I need to validate data between source and target.validatiins like record count match,data match.
I found dms validation in aws.however it not doing validation in s3 bucket files(it can be source or target).giving validation_s3 feature not available.
Kindly suggest any other tool/aws cloud services to achieve this.Thanks!
I am trying to connect BigQuery of ProjectA to Data Fusion of ProjectB and its asking me to enter a service key file. I have tried to upload the service key file to Cloud Storage of ProjectB and provided the link but it's asking me to provide a local file path.
Can someone help me on this?
Thanks in advance.
Can you try this, grant BQ permission of project A to data fusion in project B.
service-project_number#gcp-sa-datafusion.iam.gserviceaccount.com.
project_number-compute#developer.gserviceaccount.com.
Steps:
Navigate to the customer project that contains the CDF instance and copy the project number (this is found on the Home Page in the Project Info card)
Navigate to the project that contains the resources you would like to interact with.
In the sidebar, click on ‘IAM & Admin’
Click on ‘Add’ at the top of the page.
Provide the first service account name from the table above, be sure to replace with the actual number you obtained in step 1
Grant the Admin role for the resource you would like to interact with. Ex. BigQuery Admin for reading/writing to BigQuery. For BigQuery, you will also need to grant the BigQuery Data Owner role as well.
Repeat steps 5 & 6 for the second service account in the table above.
In your pipeline, ensure you define the correct Project Id for the sources/sinks. Using ‘auto-detect’ will default to the customer project that contains the CDF instance.
Can you try download the service key json file to the local, ie you local computer? And try to put the file into some folder and provide the full path to that service key file in the BigQuery properties.
I have successfully scheduled my query in BigQuery, and the result is saved as a table in my dataset. I see a lot of information about scheduling data transfer in to BigQuery or Cloud Storage, but I haven't found anything regarding scheduling an export from a BigQuery table to Cloud Storage yet.
Is it possible to schedule an export of a BigQuery table to Cloud Storage so that I can further schedule having it SFTP-ed to me via Google BigQuery Data Transfer Services?
There isn't a managed service for scheduling BigQuery table exports, but one viable approach is to use Cloud Functions in conjunction with Cloud Scheduler.
The Cloud Function would contain the necessary code to export to Cloud Storage from the BigQuery table. There are multiple programming languages to choose from for that, such as Python, Node.JS, and Go.
Cloud Scheduler would send an HTTP call periodically in a cron format to the Cloud Function which would in turn, get triggered and run the export programmatically.
As an example and more specifically, you can follow these steps:
Create a Cloud Function using Python with an HTTP trigger. To interact with BigQuery from within the code you need to use the BigQuery client library. Import it with from google.cloud import bigquery. Then, you can use the following code in main.py to create an export job from BigQuery to Cloud Storage:
# Imports the BigQuery client library
from google.cloud import bigquery
def hello_world(request):
# Replace these values according to your project
project_name = "YOUR_PROJECT_ID"
bucket_name = "YOUR_BUCKET"
dataset_name = "YOUR_DATASET"
table_name = "YOUR_TABLE"
destination_uri = "gs://{}/{}".format(bucket_name, "bq_export.csv.gz")
bq_client = bigquery.Client(project=project_name)
dataset = bq_client.dataset(dataset_name, project=project_name)
table_to_export = dataset.table(table_name)
job_config = bigquery.job.ExtractJobConfig()
job_config.compression = bigquery.Compression.GZIP
extract_job = bq_client.extract_table(
table_to_export,
destination_uri,
# Location must match that of the source table.
location="US",
job_config=job_config,
)
return "Job with ID {} started exporting data from {}.{} to {}".format(extract_job.job_id, dataset_name, table_name, destination_uri)
Specify the client library dependency in the requirements.txt file
by adding this line:
google-cloud-bigquery
Create a Cloud Scheduler job. Set the Frequency you wish for
the job to be executed with. For instance, setting it to 0 1 * * 0
would run the job once a week at 1 AM every Sunday morning. The
crontab tool is pretty useful when it comes to experimenting
with cron scheduling.
Choose HTTP as the Target, set the URL as the Cloud
Function's URL (it can be found by selecting the Cloud Function and
navigating to the Trigger tab), and as HTTP method choose GET.
Once created, and by pressing the RUN NOW button, you can test how the export
behaves. However, before doing so, make sure the default App Engine service account has at least the Cloud IAM roles/storage.objectCreator role, or otherwise the operation might fail with a permission error. The default App Engine service account has a form of YOUR_PROJECT_ID#appspot.gserviceaccount.com.
If you wish to execute exports on different tables,
datasets and buckets for each execution, but essentially employing the same Cloud Function, you can use the HTTP POST method
instead, and configure a Body containing said parameters as data, which
would be passed on to the Cloud Function - although, that would imply doing
some small changes in its code.
Lastly, when the job is created, you can use the Cloud Function's returned job ID and the bq CLI to view the status of the export job with bq show -j <job_id>.
Not sure if this was in GA when this question was asked, but at least now there is an option to run an export to Cloud Storage via a regular SQL query. See the SQL tab in Exporting table data.
Example:
EXPORT DATA
OPTIONS (
uri = 'gs://bucket/folder/*.csv',
format = 'CSV',
overwrite = true,
header = true,
field_delimiter = ';')
AS (
SELECT field1, field2
FROM mydataset.table1
ORDER BY field1
);
This could as well be trivially setup via a Scheduled Query if you need a periodic export. And, of course, you need to make sure the user or service account running this has permissions to read the source datasets and tables and to write to the destination bucket.
Hopefully this is useful for other peeps visiting this question if not for OP :)
You have an alternative to the second part of the Maxim answer. The code for extracting the table and store it into Cloud Storage should work.
But, when you schedule a query, you can also define a PubSub topic where the BigQuery scheduler will post a message when the job is over. Thereby, the scheduler set up, as described by Maxim is optional and you can simply plug the function to the PubSub notification.
Before performing the extraction, don't forget to check the error status of the pubsub notification. You have also a lot of information about the scheduled query; useful is you want to perform more checks or if you want to generalize the function.
So, another point about the SFTP transfert. I open sourced a projet for querying BigQuery, build a CSV file and transfert this file to FTP server (sFTP and FTPs aren't supported, because my previous company only used FTP protocol!). If your file is smaller than 1.5Gb, I can update my project for adding the SFTP support is you want to use this. Let me know
I have an IOT sensor which sends the following message to IoT MQTT Core topic:
{"ID1":10001,"ID2":1001,"ID3":101,"ValueMax":123}
I have added ACT/RULE which stores the incoming message in an S3 Bucket with the timestamp as a key(each message is stored as a seperate file/row in the bucket).
I have only worked with SQL databases before, so having them stored like this is new to me.
1) Is this the proper way to work with S3 storage?
2) How can I visualize the values in a schema instead of separate files?
3) I am trying to create ML Datasource from the S3 Bucket, but get the error below when Amazon ML tries to create schema:
"Amazon ML can't retrieve the schema. If you've just created this
datasource, wait a moment and try again."
Appreciate all advice there is!
1) Is this the proper way to work with S3 storage?
With only one sensor, using the [timestamp](https://docs.aws.amazon.com/iot/latest/developerguide/iot-sql-functions.html#iot-function-timestamp function in your IoT rule would be a way to name unique objects in S3, but there are issues that might come up.
With more than one sensor, you might have multiple messages arrive at the same timestamp and this would not generate unique object names in S3.
Timestamps from nearly the same time are going to have similar prefixes and designing your S3 keys this way may not give you the best performance at higher message rates.
Since you're using MQTT, you could use the traceId function instead of the timestamp to avoid these two issues if they come up.
2) How can I visualize the values in a schema instead of separate files?
3) I am trying to create ML Datasource from the S3 Bucket, but get the error below when Amazon ML tries to create schema:
For the third question, I think you could be running into a data format problem in ML because your S3 objects contain the JSON data from your messages and not a CSV.
For the second question, I think you're trying to combine message data from successive messages into a CSV, or at least output the message data as a single line of a CSV file. I don't think this is possible with just the Iot SQL language since it's intended to produce JSON.
One alternative is to configure your IoT SQL rule with a Lambda action and use a lambda function to make your JSON to CSV conversion and then write the CSV to your S3 bucket. If you go this direction, you may have to enrich your IoT message data with the timestamp (or traceId) as you call the lambda.
A rule like select timestamp() as timestamp, traceid() as traceid, concat(ID1, ID2, ID3, ValueMax) as values, * as message would produce a JSON like
{"timestamp":1538606018066,"traceid":"abab6381-c369-4a08-931d-c08267d12947","values":[10001,1001,101,123],"message":{"ID1":10001,"ID2":1001,"ID3":101,"ValueMax":123}}
That would be straightforward to use as the source for a CSV row with the data from its values property.
We are using DMS to get data from SQL Server and load it in S3 bucket, after which the data is finally loaded into Snowflake DB using Snowpipe for Full Load.
Now, in order for Snowpipe to know there is new data in S3 bucket, the filename needs to be different than the last one. Have tried all the task setting options available (DROP_AND_CREATE, DO_NOTHING, TRUNCATE) to have the file name different, but still not working. It loads the file name as LOAD00000001.csv
In documentation it shows that file name will be incremental (eg. LOAD00000001.csv, LOAD00000002.csv .. and so on) but it's not happening. Which is why the Snowpipe is not able to register the changes.
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html
Can someone please help?
For DMS the incremental counter is started over from 1 each time the task is run. It does not have a "Don't override existing objects" feature.
Your best bet may be to handle the load yourself by looking for updated object timestamps in your folder or setting up S3 event notifications.