Before I start doing this I wanted to get advice from the community on the best and most efficient manner to go about doing it.
Here is what I want to do:
Ingest data from multiple API's which returns JSON
Store it in either S3 or DynamoDB
Modify the data to use my JSON structure
Pipe out the aggregate data as an API
The data will be updated twice a day, so I would pull in the data from the source APIs and put it through my pipeline twice a day.
So basically I want to create an API by aggregating data from multiple source APIs.
I've started playing with Lambda and created the following function using Python.
#https://stackoverflow.com/a/41765656
import requests
import json
def lambda_handler(event, context):
#https://www.nylas.com/blog/use-python-requests-module-rest-apis/ USEFUL!!!
#https://stackoverflow.com/a/65896274
response = requests.get("https://remoteok.com/api")
#print(response.json())
return {
'statusCode': 200,
'body': response.json()
}
#https://stackoverflow.com/questions/63733410/using-lambda-to-add-json-to-dynamodb DYNAMODB
This works and returns a JSON response.
Here are my questions:
Should I store the data on S3 or DynamoDB?
Which AWS service should I use to aggregate the data into my JSON structure?
Which service should I use to publish the aggregate data as an API, API Gateway?
However, before I go further I would like to know what is the best way to go about doing this.
If you have experience with this I would love to hear from you.
The answer will vary depending on the quantity of data you're planning to mine. Lambdas are designed for short-duration, high-frequency workloads and thus might not be suitable.
I would recommend looking into AWS Glue, as this seems like a fairly typical ETL (Extract Transform Load) problem. You can set up glue jobs to run on a schedule, and as for data aggregation, that's the T in ETL.
It's simple to output the glue dataframe (result of a transformation) as s3 files, which can then be queried directly by Amazon Athena (as if they were db content).
As for exposing that data via an API, the serverless framework or SST are great tools for taking the sting out of spinning up a serverless API and associated resources.
Related
I am in a situation where I need to Extract the log JSON data, which might have changes in its data structure, to AWS S3 in real time manner.
I am thinking of using AWS S3 + AWS Glue Streaming ETL. The thing is the structure or schema of the log JSON data might change(these changes are unpredictable), so my solution needs to be aware of such changes and should still stream the log data smoothly without causing errors... But as far as I know, all the AWS Glue tutorials are showing the demo as if there is no changes in the structure of the incoming data.
Can you recommend or tell me the solution within AWS that's suitable for my case?
Thanks.
I have a task to analyze weather forecast data in Quicksight. The forecast data is held in NetCDF binary files in a public S3 bucket. The question is: how do you expose the contents of these binary files to Quicksight or even Athena?
There are python libraries that will decode the data from the binary files, such as Iris. They are used like this:
import iris
filename = iris.sample_data_path('forecast_20200304.nc')
cubes = iris.load(filename)
print(cubes)
So what would be the AWS workflow and services necessary to create a data ingestion pipeline that would:
Respond to an SQS message that a new binary file is available
Access the new binary file and decode it to access the forecast data
Add the decoded data to the set of already decoded data from previous SQS notifications
Make all the decoded data available in Athena / Quicksight
Tricky one, this...
What I would do is probably something like this:
Write a Lambda function in Python that is triggered when new files appear in the S3 bucket – either by S3 notifications (if you control the bucket), by SNS, SQS, or by schedule in EventBridge. The function uses the code snipplet included in your question to transform each new file and upload the transformed data to another S3 bucket.
I don't know the size of these files and how often they are published, so whether to convert to CSV, JSON, or Parquet is something you have to decide – if the data is small CSV will probably be easiest and will be good enough.
With the converted data in a new S3 bucket all you need to do is create an Athena table for the data set and start using QuickSight.
If you end up with a lot of small files you might want to implement a second step where you once per day combine the converted files into bigger files, and possibly Parquet, but don't do anything like that unless you have to.
An alternative way would be to use Athena Federated Query: by implementing Lambda function(s) that respond to specific calls from Athena you can make Athena read any data source that you want. It's currently in preview, and as far as I know all the example code is written in Java – but theoretically it would be possible to write the Lambda functions in Python.
I'm not sure whether it would be less work than implementing an ETL workflow like the one you suggest, but yours is one of the use cases for which Athena Federated Query was designed for and it might be worth looking into. If NetCDF files are common and a data source for such files would be useful for other people I'm sure the Athena team would love to talk to you and help you out.
We're currently processing daily mobile app log data with AWS lambda and posting it into redshift. The lambda structures the data but it is essentially raw. The next step is to do some actual processing of the log data into sessions etc, for reporting purposes. The final step is to have something do feature engineering, and then use the data for model training.
The steps are
Structure the raw data for storage
Sessionize the data for reporting
Feature engineering for modeling
For step 2, I am looking at using Quicksight and/or Kibana to create reporting dashboard. But the typical stack as I understand it is to do the log processing with logstash, then have it go to elasticsreach and finally to Kibana/Quicksight. Since we're already handling the initial log processing through lambda, is it possible to skip this step and pass it directly into elasticsearch? If so where does this happen - in the lambda function or from redshift after it has been stored in a table? Or can elasticsearch just read it from the same s3 where I'm posting the data for ingestion into a redshift table?
Elasticsearch uses JSON to perform all operations. For example, to add a document to an index, you use a PUT operation (copied from docs):
PUT twitter/_doc/1
{
"user" : "kimchy",
"post_date" : "2009-11-15T14:12:12",
"message" : "trying out Elasticsearch"
}
Logstash exists to collect log messages, transform them into JSON, and make these PUT requests. However, anything that produces correctly-formatted JSON and can perform an HTTP PUT will work. If you already invoke Lambdas to transform your S3 content, then you should be able to adapt them to write JSON to Elasticsearch. I'd use separate Lambdas for Redshift and Elasticsearch, simply to improve manageability.
Performance tip: you're probably processing lots of records at a time, in which case the bulk API will be more efficient than individual PUTs. However, there is a limit on the size of a request, so you'll need to batch your input.
Also: you don't say whether you're using an AWS Elasticsearch cluster or self-managed. If the former you'll also have to deal with authenticated requests, or use an IP-based access policy on the cluster. You don't say what language your Lambdas are written in, but if it's Python you can use the aws-requests-auth library to make authenticated requests.
I have a whole bunch of data in AWS S3 stored in JSON format. It looks like this:
s3://my-bucket/store-1/20190101/sales.json
s3://my-bucket/store-1/20190102/sales.json
s3://my-bucket/store-1/20190103/sales.json
s3://my-bucket/store-1/20190104/sales.json
...
s3://my-bucket/store-2/20190101/sales.json
s3://my-bucket/store-2/20190102/sales.json
s3://my-bucket/store-2/20190103/sales.json
s3://my-bucket/store-2/20190104/sales.json
...
It's all the same schema. I want to get all that JSON data into a single database table. I can't find a good tutorial that explains how to set this up.
Ideally, I would also be able to perform small "normalization" transformations on some columns, too.
I assume Glue is the right choice, but I am open to other options!
If you need to process data using Glue and there is no need to have a table registered in Glue Catalog then there is no need to run Glue Crawler. You can setup a job and use getSourceWithFormat() with recurse option set to true and paths pointing to the root folder (in your case it's ["s3://my-bucket/"] or ["s3://my-bucket/store-1", "s3://my-bucket/store-2", ...]). In the job you can also apply any required transformations and then write the result into another S3 bucket, relational DB or a Glue Catalog.
Yes, Glue is a great tool for this!
Use a crawler to create a table in the glue data catalog (remember to set Create a single schema for each S3 path under Grouping behavior for S3 data when creating the crawler)
Read more about it here
Then you can use relationalize to flatten our your json structure, read more about that here
Json and AWS Glue may not be the best match. Since AWS Glue is based on hadoop, it inherits hadoop's "one-row-per-newline" restriction, so even if your data is in json, it has to be formatted with one json object per line [1]. Since you'll be pre-processing your data anyway to get it into this line-separated format, it may be easier to use csv instead of json.
Edit 2022-11-29: There does appear to be some tooling now for jsonl, which is the actual format that AWS expects, making this less of an automatic win for csv. I would say if your data is already in json format, it's probably smarter to convert it to jsonl than to convert to csv.
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.