I want to upload my AnyLogic model to AnyLogic Cloud through the run configuration process, and for outputs, I want to add several dataset log files. I was wondering how can I do that?
Just add the datasets themselves as model outputs; graphs for them will then be included in the Cloud experiments' dashboards.
The AnyLogic Cloud doesn't currently support having Excel outputs from your models (just Excel inputs), so you can't use AnyLogic-database-dataset-logs exported to Excel. But you can now (since the most recent version) download the outputs from all your model runs in JSON format (which would include the dataset data).
Otherwise you have to do it 'Cloud-natively' instead; e.g., write relevant output data to some Cloud-based database (like an Amazon database). That obviously requires the Java knowledge on how to do that (and an AnyLogic Cloud subscription, or Private Cloud installation, to be able to access external resources).
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I have two sources:
A csv that will be uploaded to a cloud storage service, probably GCP Cloud Storage.
The output of a scrapping process done with Python.
When a user updates 1) (the cloud stored file) an event should be triggered to execute 2) (the scrapping process) and then some transformation should take place in order to merge these two sources into one in a JSON format. Finally, the content of this JSON file should be stored in a DB of easy access and low cost. The files the user will update are of max 5MB and the updates will take place once weekly.
From what I've read, I can use GCP Cloud Functions to accomplish this whole process or I can use Dataflow too. I've even considered using both. I've also thought of using MongoDB to store the JSON objects of the two sources final merge.
Why should I use Cloud Functions, Dataflow or both? What are your thoughts on the DB? I'm open to different approaches. Thanks.
Regarding de use of Cloud Functions and Dataflow. In your case I will go for Cloud Functions as you don't have a big volume of data. Dataflow is more complex, more expensive and you will have to use Apache Beam. If you are confortable with python and having into consideration your scenario I will choose Cloud Functions. Easy, convenient...
To trigger a Cloud Functions when Cloud Storage object is updated you will have to configure the triggers. Pretty easy.
https://cloud.google.com/functions/docs/calling/storage
Regarding the DB. MongoDB is a good option but if you wanth something quick an inexpensive consider DataStore
As a managed service it will make your life easy with a lot of native integrations. Also it has a very interesting free tier.
I am using the delta lake oss version 0.8.0.
Let's assume we calculated aggregated data and cubes using the raw data and saved the results in a gold table using delta lake.
My question is, is there a well known way to access these gold table data and deliver them to a web dashboard for example?
In my understanding, you need a running spark session to query a delta table.
So one possible solution could be to write a web api, which executes these spark queries.
Also you could write the gold results in a database like postgres to access it, but that seems just duplicating the data.
Is there a known best practice solution?
The real answer depends on your requirements regarding latency, number of requests per second, amount of data, deployment options (cloud/on-prem, where data located - HDFS/S3/...), etc. Possible approaches are:
Have the Spark running in the local mode inside your application - it may require a lot of memory, etc.
Run Thrift JDBC/ODBC server as a separate process, and access data via JDBC/ODBC
Read data directly using the Delta Standalone Reader library for JVM, or via delta-rs library that works with Rust/Python/Ruby
*Data newbie here *
Currently, to run analytics report on data pulled from Dynamics 365, I use Power BI.
Issue with this is, Power BI is quite slow processing large data. I carry out a number of transform steps (e.g. Merge, Join, deleting or renaming columns, etc). So, when I try to run a query in Power BI with said steps, it takes a long time to complete.
So, as a solution, I decided to make use of Azure Data Factory(ADF). The plan is to use ADF to pull the data from CRM (i.e. Dynamics 365), perform transformations and publish the data. Then I'll use Power BI for visual analytics.
My question is:
What azure service will I need in addition to Data Factory? Will I need to store the data I pulled from CRM somewhere - like Azure Data Lake or Blob storage? Or can I do the transformation on the fly, right after the data is ingested?
Initially, I thought I could use the 'copy' activity to ingest data from CRM and start playing with the data. But using the copy activity, I needed to provide a sink (destination for the data. Which has to be a storage of some sort).
I also thought, I could make use of the 'lookup' activity. I tried to use it, but getting errors (no exception message is produced).
I have scoured the internet for a similar process (i.e. Dynamics 365 -> Data Factory -> Power BI), but I've not been able to find any.
Most of the processes I've seen however, utilises some sort of data storage right after data ingest.
All response welcome. Even if you believe I am going about this the wrong way.
Thanks.
Few things here:
The copy activity just moves data from a source, to a sink. It doesnt modify it on the fly.
The lookup activity is just to look for some atributes to use later on the same pipeline.
ADF cannot publish a dataset to power bi (although it may be able to push to a streaming dataset).
You approach is correct, but you need that last step of transforming the data. You have a lot of options here, but since you are already familiar with Power Bi you can use the Wrangling Dataflows, which allows you to take a file from the datalake, apply some power query and save a new file in the lake. You can also use Mapping Dataflows, databricks, or any other data transformation tool.
Lastly, you can pull files from a data lake with Power Bi to make your report with the data on this new file.
Of course, as always in Azure there are a lot of ways to solve problems or architect services, this is the one I consider simpler for you.
Hope this helped!
I found that Google ml engine expects data in cloud storage, big query etc. Is there any way to stream data to ml-engine. For example, imagine that I need to use data in WordPress or Drupal site to create a tensorflow model, say a spam detector. One way is to export the whole data as CSV and upload it to cloud storage using google-cloud--php library. The problem here is that, for every minor change, we have to upload the whole data. Is there any better way?
By minor change, do you mean "when you get new data, you have to upload everything--the old and new data--again to gcs"? One idea is to export just the new data to gcs on some schedule, making many csv files over time. You can write your trainer to take a file pattern and expand it using get_matching_files/Glob or multiple file paths.
You can also modify your training code to start from an old checkpoint and train over just the new data (which is in its own file) for a few steps.
I'm designing a data warehouse system, the origin data sources are two: files (hexadecimal format, record structure known) and PostgreSQL database.
The ETL phase has to read the content of the two sources (files and DB) and combining/integrating/cleaning them. After this, loading data into the DW.
For this purpose, is better a tool (for example Talend) or ad-hoc solution (writing ad-hoc routines by using a programming language)?
I would suggest you use the Bulk Loader to get your flat file into DB. This allows you to customize the loading rules and then process/cleanse the resulting data set using regular SQL (no other custom code to write)