I want to write a python function that send data to Bigquery every time a put event occurs in my S3 bucket but I'm new in AWS is it possible to integrate bigquery with a lambda function? or can someone give me another way to stream my dynamodb data to bigquery? Thank you my language is python
N.B: I used dynamostream firehose to send my data in S3 now I want to retrieve my data from s3 every time a put event occur to send it into bigquery.
There are already plenty of resources online about "how to trigger a lambda after a put object on a S3".
But here are a few links to get you set up:
You will need to set up an EventBridge (CloudWatch Events are legacy) to trigger your Lambda when some action happens on your S3 bucket:
https://aws.amazon.com/fr/blogs/compute/using-dynamic-amazon-s3-event-handling-with-amazon-eventbridge/
You can use the boto3 Python framework to write AWS Lambdas:
https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
You can check the BigQuery Python SDK by GCP to communicate with your BQ database: https://googleapis.dev/python/bigquery/latest/index.html
Related
What is the easiest way to automatically ingest csv-data from a S3 bucket into a Timestream database ?
I have a s3-bucket which continuasly is generating csv files inside a folder structure. I want to save these files inside a timestream database so i can visualize them inside my grafana instance.
I already tried to do that via a Glue crawler but that wont wont for me. Is there any workaround or tutorial on how to solve this task ?
I do this using a Lambda function, an SNS topic and a queue.
New files in my bucket triggers a notification on an SNS Topic
The notification gets added to an SQS queue.
The lambda function consumes the queue, recovers the bucket and key of the new s3 object, downloads the csv file, does some processing and ingests the data into timestream. The lambda is implemented in Python.
This has been working ok, with the caveat that large files may not ingest fully within the lambda 15 minute limit. Timestream is not super fast. It gets better by using multi-valued records, as well as using the "common attributes' feature of the timestream client in boto3.
(it should be noted that the lambda can be triggered directly by the S3 bucket, if one prefers. Using a queue allows a bit more flexibility, such as being able to manually add files to the queue for reprocessing)
We need to run an analysis of the data in Amazon DynamoDB. Since doing it in the DDB isn't an option due to DDB's limitations with analysis, based on the recommendations I am leaning towards DDB -?> S3 -> Athena.
It is a data-heavy application with data streaming from AWS IoT devices and is also a multi-tenant application. Now, to sync data from DDB to Amazon S3, it will be probably a couple of times a day. How do we set up incremental exports for this purpose?
There is an Athena connector to be able to query your data in DynamoDB table directly using SQL query.
https://docs.aws.amazon.com/athena/latest/ug/athena-prebuilt-data-connectors-dynamodb.html
https://dev.to/jdonboch/finally-dynamodb-support-in-aws-quicksight-sort-of-2lbl
Another solution for this use case is you can write an AWS Step Functions workflow that when invoked, can read data from an Amazon DynamoDB table and then format the data to the way you want it and place the data into an Amazon S3 bucket (an example that shows a similar use case will be available soon):
This is the reverse (here the source is an Amazon S3 bucket and the target is an Amazon DynamoDB table) but you can build the Workflow so the target is an Amazon S3 bucket. Because it's a workflow, you can use a Lambda function that is scheduled to fire a few times a day based on a CRON expression. The job of this Lambda function is to invoke the workflow using the Step Functions API.
I'd like to peform the following tasks on a regular basis (e.g. every day at 6AM) using AWS:
get new set of data using API. This dataset is updated on a daily basis.
run a python script that would process the obtained dataset by the means of several python libraries like matplotlib, pandas, plotly
automatically send the output of the script, which would be a single pdf file or a html dashboard, via email to a group of specified recipients
I know how to perform all of the above items locally - my goal is to automate this routine. I'm new to AWS and would appreciate some advice on how to perform these tasks in a straightforward way. Based on the reading I did so far, it looks like the serverless approach may be able to do the job and also reduce the complexity, but I'm not sure which functionalities exactly I should use.
For scheduling you can use aws event bridge.
You can schedule AWS lambda or AWS Step Functions both of these are serverless :).
You can have 3 lambdas
To get the data and save it in S3/dynamo (if you want to persist the data)
Processor lambda and save the report to S3.
Another lambda to send email using AWS SES which will read the report from S3 and send it.
If you don't want to use step function you can start your lambda from S3 put event or you can trigger one lambda from another lambda using aws-sdk.
So there are different approaches you can take.
First off, I would create a Lambda. You can schedule the function to run on a cron job.
If the Message you want to send is small:
I would create a SNS Topic with a email fan out.
Inside your lambda you can then transform the data and send out via SNS.
Otherwise:
I would use SES and send a mail via the SES SDK.
I have gone through couple of stackoverflow questions regarding hourly backups from DDB to S3 where the best solution turned out to be to enable DDB Stream, subscribe lambda function and push to S3.
I am trying to understand if directly pushing from Lambda to S3 is fine or from Lambda to Kinesis Firehose and then to S3. Can someone share what is the advantage if we introduce Firehose in between. We anyways trigger lambda only after specific batch window that implies we are already buffering there.
Thanks in advance.
Firehose gives you the possibility to convert and compress your data. In addition you can directly attach a Glue Metadata table, so you can query your data with Athena.
You can write a Lambda function that reads a DynamoDB table, gets a result set, encodes the data to some format (ie, JSON), then place that JSON into an Amazon S3 bucket. You can use scheduled events to fire off the Lambda function on a regular schedule.
Here in AWS tutorial that shows you how to use scheduled events to invoke a Lambda function:
Creating scheduled events to invoke Lambda functions
This AWS tutorial also shows you how to read data from an Amazon DynamoDB table from a Lambda function.
I am trying to take sql data stored in a csv file in an s3 bucket and transfer the data to AWS Redshift and automate that process. Would writing etl scripts with lambda/glue be the best way to approach this problem, and if so, how do I get the script/transfer to run periodically? If not, what would be the most optimal way to pipeline data from s3 to Redshift.
Tried using AWS Pipeline but that is not available in my region. I also tried to use the AWS documentation for Lambda and Glue but don't know where to find the exact solution to the problem
All systems (including AWS Data Pipeline) use the Amazon Redshift COPY command to load data from Amazon S3.
Therefore, you could write an AWS Lambda function that connects to Redshift and issues the COPY command. You'll need to include a compatible library (eg psycopg2) to be able to call Redshift.
You can use Amazon CloudWatch Events to call the Lambda function on a regular schedule. Or, you could get fancy and configure Amazon S3 Events so that, when a file is dropped in an S3 bucket, it automatically triggers the Lambda function.
If you don't want to write it yourself, you could search for existing code on the web, including:
The very simply Python-based christianhxc/aws-lambda-redshift-copy: AWS Lambda function that runs the copy command into Redshift
A more fully-featured node-based A Zero-Administration Amazon Redshift Database Loader | AWS Big Data Blog