guildai VS nodejs-bigquery

Experiment tracking in DvC is implemented using git to store snapshots of a project and related artifacts. You might take a look at Guild AI's support for DvC, which is tightly integrated with DvC stages. You can run any of the stages defined for a project and you get a properly isolated run (each run is a project copy to ensure that you're not corrupting the run if you modify files while it's running - as well as properly supporting concurrent runs). Once you have runs in Guild, you can use any number of tools to study, compare, export, etc.

I built an experiment tracking tool (Guild AI) that focuses on code/model reuse and so this question is dear to my heart :) Best of luck!

I'm not aware of experiment tracking in Jupyter notebooks themselves. Guild AI is able to run notebooks as experiments however.

Disclosure - I'm the author of Guild AI so take this for the biased opinion that it is.

I'm the author of Guild AI (open source experiment tracking). For some time now Guild users have asked for DvC support. This is now available as a pre-release.

Guild AI now has support for running DvC stages as experiments. DvC uses git under the covers to manage project state for each experiment, along with the experiment results. Guild doesn't touch your git repo and instead copies your project source to a new run directory. This ensures that you have a correct record of your experiment without churning your project state.

Guild.ai - https://guild.ai/

Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.

If you've ever wondered what it's like to manage a BigQuery instance at Reddit scale, know that it's exactly like smaller systems just with much, much bigger numbers in the logs. Database management fundamentals are eerily similar regardless of scale or platform; BigQuery handles just about anything we throw at it, and we do indeed throw it the whole book. Our BigQuery platform is more than 100 petabytes of data that supports data science, machine learning, and analytics workloads that drive experiments, analytics, advertising, revenue, safety, and more. As Reddit grew, so did the workload velocity and complexity within BigQuery and thus the need for more elegant and fine-tuned workload management.

Now I know I've got some data I could use, I now need to find a platform that I can use to analyse the data coming from the Forem API. I did consider some other pieces of software, such as Google BigQuery (with looker studio) and ElasticSearch (with Kibana), I ultimately went with OpenSearch which is essentially a forked version of ElasticSearch maintained by AWS. The main reasons are that I could host it locally for free (unlike BigQuery). I do have some prior experience with both elastic (back when it was called ELK) and OpenSearch, but my work with OpenSearch was far more recent, so I decided to go with that.

Bigquery as our data warehouse

Google BigQuery

You build a data integration between all the ad service providers (e.g. Google Ads, Facebook Ads, etc.), ingesting data from those APIs and storing it in your BigQuery data warehouse.

It also includes some extensions that integrate Firebase with Google Cloud Platform services such as BigQuery.

In addition, batch tasks require knowledge of the data schema of each service in order to get the data correctly and save it to the corresponding warehouse table. Assuming our data warehouse is GCP BigQuery, the schema in the warehouse table also needs to be created and modified manually.

BigQuery has a pricing model close to Pub/Sub : you pay for what you insert on the database (in streaming) and the storage of these data. The main difference is on what you can do with these data. BigQuery is not a message queuing service, this is a data warehouse service. It proposes a query service to exploit these data and you pay for these queries. Actually, not on the query itself but on the quantity of data manipulated for producing the results of the query. This means that you do not directly pay for a power capacity on query but on data transfer to produce the result which is very different from a none managed database perspective such SQL databases where the main model pricing is the node size to store and query data.

A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either using an open source data warehouse like Apache Kylin or use a cloud-based one like Redshift or Snowflake. In this instance, the organization uses BigQuery. Data to this warehouse can be loaded through a separate Kafka topic. The below diagram summarizes the complete architecture.