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It seems difficult to make the argument that replicating data to a warehouse is "simpler" than leaving the data in its original place (which could actually be a

It seems difficult to make the argument that replicating data to a warehouse is "simpler" than leaving the data in its original place (which could actually be a warehouse) and querying it through an FDW. In your scenario you need to maintain and monitor an ETL pipeline, pay storage costs for the warehouse, and likely pay bandwidth costs for moving your data into it. In the blog post scenario, the only cost is a Postgres instance and bandwidth for the data your queries return. There are many cases where replication to a warehouse makes sense, but there are also plenty where it doesn't. Our philosophy at Splitgraph, where we're building something just like this blog post, is that querying should be cheap and easy in the experimentation phase. Analysts shouldn't need to ask engineers to setup a data pipeline just so they can query some data they might not even want. So why not start with a "data lake" (or "data mesh")? You can query any data connected to the mesh without any setup or waiting time. If you eventually decide that you don't want the tradeoffs of federation, then you can selectively warehouse only the data you need to optimize certain queries. (I might go even further and suggest that the data industry has the idea of the "modern data stack" completely wrong. Replicating data to a warehouse is the ultimate act of centralization. Every other aspect of software is trending toward decentralization; it's the only real way to deal with increasing complexity and sprawl. It's only a matter of time before this paradigm shift hits the data stack too.)

nunie123 6y ago
I agree with much of what you said. However, I'm disinclined to believe there will be a trend away from a centralized data warehouse (or data lake). There is inherent value in having a single source of truth for analytics. With modern cloud tools abstracting away the complexity distributed analytical databases, data warehousing is getting easier and more powerful. It's true that there is added complexity in centralizing the data. But as the author of this article suggested, you're in a bad spot if your marketing team and your sales team can't agree on last month's revenue. I'm not sure how you'd solve that problem in an architecture where the data isn't getting centralized.
- michael_j_ward 6y ago
  > There is inherent value in having a single source of truth for analytics. > I'm not sure how you'd solve that problem in an architecture where the data isn't getting centralized. Isn't the entire point of this article that the author achieved a single source of truth without moving the data by using postgres's Foreign Data Wrappers?
  - nunie123 6y ago
    The data is being moved, it's just that the mechanism to move it was the FDWs, instead of e.g. an Airflow DAG. The FDWs are interesting because they're update-on-query as opposed to updating on a fixed schedule or update on a source system event, which is more common.
    - michael_j_ward 6y ago
      > The data is being moved My understanding is that FDW's act as a proxy. I don't believe the tables are materialized. Can you link me where it says otherwise? I don't see anything in the docs [1]. [1] https://www.postgresql.org/docs/current/postgres-fdw.html https://www.postgresql.org/docs/current/postgres-fdw.html
sa46 6y ago
> decentralization; it's the only real way to deal with increasing complexity and sprawl Decentralization can help with sprawl (aka scale) but it makes software more complex so it’s not a way to tackle complexity. Decentralization is only tolerated because, at some point, it’s the only method left to scale. As an example, distributed databases are significantly more complex to build than a single node database. You’ve also ignored all of the downsides of using your OLTP database for OLAP queries. The most important is that adhoc queries can impact production. In my experience, that cost dwarfs the costs of maintaining an ETL pipeline as soon as you have your first outage. Another downside is that with a federated, pull based model you need to keep the mapping (FDW definition) up to date. In essence, the maintenance cost moves from an ETL pipeline to an FDW definition.
- chatmasta 6y ago
  Re: ad-hoc queries impacting production, you can mitigate this by pointing your data mesh to a read replica of the prod DB (which you can colocate with it to avoid bandwidth costs). Admittedly at that point you might be approaching a warehousing use case. Re: maintaining the mapping. Sure that's a tradeoff. But I'd rather manage declarative FDW definitions than a programmatic ETL pipeline. Yes, handling schema changes is non-trivial (a naive starting point is periodic introspection of the remote table), but that's true in an ETL pipeline too. As you say, you're moving the cost from the ETL pipeline to the FDW definition. But what if we can lower the cost of the "FDW definition" (a.k.a. setting up and maintaining the data mesh)? From our perspective, at least with the product we're building, we think we can make a data mesh that's low maintenance enough that it makes sense as a first default before ETL. But the two aren't mutually exclusive. A data mesh can connect multiple warehouses just as easily as it can your production databases. For example, you could move data (ETL) from your data sources to your Snowflake and BigQuery warehouses, and then connect them both to your data mesh. Also, a mesh can add value as a unified access layer that proxies queries and applies data governance rules (e.g. sharing data, access control, column masking, query whitelisting, rewriting, rate limiting, auditing, firewalling, etc.). It would be hard to do this in your warehouse, because as soon as you have another one, or there's some data that you can't ETL into it, you lose any benefits of its access layer. (e.g. Maybe you have two warehouses because you haven't fully migrated to one, or for cost reasons like storing FireBase clickstream data in BigTable that you can't move to Snowflake with the rest of your data. Now you need to maintain two sets of accounts and ACLs for each warehouse.). (And for what it's worth -- this is a bit off topic of the current thread -- we do recognize there is often a use case for warehousing when the tradeoffs of federation aren't worth it. We can actually act as a warehouse too, which makes it easy to start with a data mesh but have the option to bail out to warehousing for the specific data assets that need it. Ideally, you can just click a button that says something like "snapshot data" or "schedule query" or "replicate data source." To ingest and store the data, we can leverage the concept we introduced of "data images" for storing versioned snapshots of data in delta-compressed, content-addressable chunks of hot-swappable cstore_fdw files. You can create data images with a declarative, Dockerfile-like syntax called Splitfiles. They look something like FROM upstream:tag IMPORT { SELECT x, y FROM ... }). You get full provenance and can `sgr rebuild` an updated image only when its upstream changes, just like Docker.)
redandblack 6y ago
Same thesis for us. Pushing to use replicated sources for a data mesh, and build around query and cache services. The thesis is analysts know what they want, and operating only on production ready reports takes too much away from business. So, more power to analysts
georgewfraser 6y ago
Replicating a Postgres instance to a data warehouse takes ~1 hour to set up and $500/month, depending on the database size. The cost of the data warehouse is similar. If you can’t afford that cost, you’re not going to be able to afford an analytics team to make meaningful use of that data. In that scenario, you’re better off using Google analytics + the occasional ad hoc query of your production DB (which you should only have one of, you are at way too small scale to have multiple DBs). Federated data systems, like what is described in this post, are a kind of “nerd trap.” They’re fascinating and people keep building them, but they have little real world utility. Everyone is either big enough where it’s worth it to build a real data warehouse, or small enough that they shouldn’t be messing around with complex data infrastructure.

nunie123 6y ago

I agree with much of what you said. However, I'm disinclined to believe there will be a trend away from a centralized data warehouse (or data lake). There is inherent value in having a single source of truth for analytics. With modern cloud tools abstracting away the complexity distributed analytical databases, data warehousing is getting easier and more powerful. It's true that there is added complexity in centralizing the data. But as the author of this article suggested, you're in a bad spot if your marketing team and your sales team can't agree on last month's revenue. I'm not sure how you'd solve that problem in an architecture where the data isn't getting centralized.

michael_j_ward 6y ago
> There is inherent value in having a single source of truth for analytics. > I'm not sure how you'd solve that problem in an architecture where the data isn't getting centralized. Isn't the entire point of this article that the author achieved a single source of truth without moving the data by using postgres's Foreign Data Wrappers?
- nunie123 6y ago
  The data is being moved, it's just that the mechanism to move it was the FDWs, instead of e.g. an Airflow DAG. The FDWs are interesting because they're update-on-query as opposed to updating on a fixed schedule or update on a source system event, which is more common.
  - michael_j_ward 6y ago
    > The data is being moved My understanding is that FDW's act as a proxy. I don't believe the tables are materialized. Can you link me where it says otherwise? I don't see anything in the docs [1]. [1] https://www.postgresql.org/docs/current/postgres-fdw.html https://www.postgresql.org/docs/current/postgres-fdw.html

sa46 6y ago

> decentralization; it's the only real way to deal with increasing complexity and sprawl Decentralization can help with sprawl (aka scale) but it makes software more complex so it’s not a way to tackle complexity. Decentralization is only tolerated because, at some point, it’s the only method left to scale. As an example, distributed databases are significantly more complex to build than a single node database. You’ve also ignored all of the downsides of using your OLTP database for OLAP queries. The most important is that adhoc queries can impact production. In my experience, that cost dwarfs the costs of maintaining an ETL pipeline as soon as you have your first outage. Another downside is that with a federated, pull based model you need to keep the mapping (FDW definition) up to date. In essence, the maintenance cost moves from an ETL pipeline to an FDW definition.

chatmasta 6y ago
Re: ad-hoc queries impacting production, you can mitigate this by pointing your data mesh to a read replica of the prod DB (which you can colocate with it to avoid bandwidth costs). Admittedly at that point you might be approaching a warehousing use case. Re: maintaining the mapping. Sure that's a tradeoff. But I'd rather manage declarative FDW definitions than a programmatic ETL pipeline. Yes, handling schema changes is non-trivial (a naive starting point is periodic introspection of the remote table), but that's true in an ETL pipeline too. As you say, you're moving the cost from the ETL pipeline to the FDW definition. But what if we can lower the cost of the "FDW definition" (a.k.a. setting up and maintaining the data mesh)? From our perspective, at least with the product we're building, we think we can make a data mesh that's low maintenance enough that it makes sense as a first default before ETL. But the two aren't mutually exclusive. A data mesh can connect multiple warehouses just as easily as it can your production databases. For example, you could move data (ETL) from your data sources to your Snowflake and BigQuery warehouses, and then connect them both to your data mesh. Also, a mesh can add value as a unified access layer that proxies queries and applies data governance rules (e.g. sharing data, access control, column masking, query whitelisting, rewriting, rate limiting, auditing, firewalling, etc.). It would be hard to do this in your warehouse, because as soon as you have another one, or there's some data that you can't ETL into it, you lose any benefits of its access layer. (e.g. Maybe you have two warehouses because you haven't fully migrated to one, or for cost reasons like storing FireBase clickstream data in BigTable that you can't move to Snowflake with the rest of your data. Now you need to maintain two sets of accounts and ACLs for each warehouse.). (And for what it's worth -- this is a bit off topic of the current thread -- we do recognize there is often a use case for warehousing when the tradeoffs of federation aren't worth it. We can actually act as a warehouse too, which makes it easy to start with a data mesh but have the option to bail out to warehousing for the specific data assets that need it. Ideally, you can just click a button that says something like "snapshot data" or "schedule query" or "replicate data source." To ingest and store the data, we can leverage the concept we introduced of "data images" for storing versioned snapshots of data in delta-compressed, content-addressable chunks of hot-swappable cstore_fdw files. You can create data images with a declarative, Dockerfile-like syntax called Splitfiles. They look something like FROM upstream:tag IMPORT { SELECT x, y FROM ... }). You get full provenance and can `sgr rebuild` an updated image only when its upstream changes, just like Docker.)

redandblack 6y ago

Same thesis for us. Pushing to use replicated sources for a data mesh, and build around query and cache services. The thesis is analysts know what they want, and operating only on production ready reports takes too much away from business. So, more power to analysts

georgewfraser 6y ago

Replicating a Postgres instance to a data warehouse takes ~1 hour to set up and $500/month, depending on the database size. The cost of the data warehouse is similar. If you can’t afford that cost, you’re not going to be able to afford an analytics team to make meaningful use of that data. In that scenario, you’re better off using Google analytics + the occasional ad hoc query of your production DB (which you should only have one of, you are at way too small scale to have multiple DBs). Federated data systems, like what is described in this post, are a kind of “nerd trap.” They’re fascinating and people keep building them, but they have little real world utility. Everyone is either big enough where it’s worth it to build a real data warehouse, or small enough that they shouldn’t be messing around with complex data infrastructure.