Question: I am in the process of mapping my country's open data, and the relationships between them, and I need to do it in both human and machine readable formats.

I am looking at diagramming tools for the human readable part, but I would need the tool to be able to export the results to a machine readable format other than SQL, such as json or xml, so as to be able to pass it to a program of my own writing for later processing.

Since I would not need to be dependent on SQL, I would like as many of the UML relationship types to be supported. However, I would be able to make do with association, reflective association (self reference) and multiplicity.

Is there any free tool that can achieve this?

P.S.: If not, are there any Python or C# libraries that can read SQL DDL statements into a class structure?

Answer: 
I'm very excited about TimescaleDB, which is an extension to Postgres that creates hyper tables that handle all the time series partitioning magic and supposedly bring huge scale to write throughput. We're going to try a POC with this:

<https://www.timescale.com>

Here are some relevant links to frame the discussion of "Can relational databases be used for time series data"

<https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63950>

<https://blog.timescale.com/time-series-data-postgresql-10-vs-timescaledb-816ee808bac5>

<https://blog.timescale.com/tutorial-installing-timescaledb-on-aws-c8602b767a98>

<https://blog.timescale.com/choose-postgresql-for-iot-19688efc60ca>

<http://www.timescale.com/papers/timescaledb.pdf>

Scroll to slides. Wow
<https://www.percona.com/live/17/sessions/building-scalable-time-series-database-postgresql>
I discovered [redash](https://github.com/getredash/redash)

Here the top of the README

START README

Redash is our take on freeing the data within our company in a way that will better fit our culture and usage patterns.

Prior to Redash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.

Redash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL). Today Redash has support for querying multiple databases, including: Redshift, Google BigQuery, PostgreSQL, MySQL, Graphite, Presto, Google Spreadsheets, Cloudera Impala, Hive and custom scripts.

Redash consists of two parts:

**Query Editor**: think of JS Fiddle for SQL queries. It's your way to share data in the organization in an open way, by sharing both the dataset and the query that generated it. This way everyone can peer review not only the resulting dataset but also the process that generated it. Also it's possible to fork it and generate new datasets and reach new insights.

**Dashboards/Visualizations**: once you have a dataset, you can create different visualizations out of it, and then combine several visualizations into a single dashboard. Currently it supports charts, pivot table and cohorts.

END README