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.
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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 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).
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
We want to use Grafana to show measuring data. Now, our measuring setup creates a huge amount of data that is saved in files. We keep the files as-is and do post-processing on them directly with Spark ("Data Lake" approach).
We now want to create some visualization and I thought of setting up Cassandra on the cluster running Spark and HDFS (where the files are stored). There will be a service (or Spark-Streaming job) that dumps selected channels from the measuring data files to a Kafka topic and another job that puts them into Cassandra. I use this approach because we have other stream processing jobs that do on the fly calculations as well.
I now thought of writing a small REST service that makes Grafana's Simple JSON datasource usable to pull the data in and visualize it. So far so good, but as the amount of data we are collecting is huge (sometimes about 300MiB per minute) the Cassandra database should only hold the most recent few hours of data.
My question now is: If someone looks at the data, finds something interesting and creates a snapshot of a dashboard or panel (or a certain event occurrs and a snapshot is taken automatically), and the original data is deleted from Cassandra, can the snapshot still be viewed? Is the data saved with it? Or does the snapshot only save metadata and the data source is queried anew?
According to Grafana docs:
Dashboard snapshot
A dashboard snapshot is an instant way to share an interactive dashboard publicly. When created, we strip sensitive data like queries (metric, template and annotation) and panel links, leaving only the visible metric data and series names embedded into your dashboard. Dashboard snapshots can be accessed by anyone who has the link and can reach the URL.
So, data is saved inside snapshot and no longer depends on original data.
As far as I understand Local Snapshot is stored in grafana db. At your data scale using external storage (webdav, etc) for snapshots can be more a better option.
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)