Add "when to use" to the What is Citus page (#92)

* Add "when to use" to the What is Citus page

* Shorten the considerations section

Note JOIN support

Note query parallelization engine

aboutcitus/what_is_citus.rst

@@ -1,6 +1,39 @@
 What is Citus?
 ==============
 
-Citus horizontally scales PostgreSQL across commodity servers using sharding and replication. Its query engine parallelizes incoming SQL queries across these servers to enable real-time responses on large datasets.
+Citus horizontally scales PostgreSQL across multiple machines using sharding and replication. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (a.k.a. less than a second) responses on large datasets.
 
 Citus extends the underlying database rather than forking it, which gives developers and enterprises the power and familiarity of a traditional relational database. As an extension, Citus supports new PostgreSQL releases, allowing users to benefit from new features while maintaining compatibility with existing PostgreSQL tools.
+
+When to Use Citus
+-----------------
+
+Example use cases:
+
+* Analytic dashboards with subsecond response times
+* Exploratory queries on unfolding events, including JOIN queries
+* Aggregate reports on large archives
+* Analyzing sessions with funnels, segmentation, and cohorts
+* Extending a data warehouse with real-time capabilities
+
+For concrete examples check out our customer `use cases <https://www.citusdata.com/solutions/case-studies>`_. Typical Citus workloads are operational, with aggregate queries and no long-lived transactions.
+
+Considerations for Use
+----------------------
+
+Although Citus extends PostgreSQL with distributed functionality, it is not a drop-in replacement that scales out all workloads. A performant Citus cluster involves thinking about the data model, tooling, and choice of SQL features used.
+
+Data models that have fewer tables (<10) work much better than those that have hundreds of tables. This is a property of distributed systems: the more tables, the more distributed dependencies.
+
+A good way to think about tool and SQL feature coverage is the following: if your workload aligns with use-cases noted in the "When to use Citus" section and you happen to run into an unsupported query or tool, then there’s usually a good workaround.
+
+When Citus is Inappropriate
+---------------------------
+
+Workloads which require a large (non-aggregated) flow of information between nodes generally do not work as well. For instance:
+
+* Traditional data warehousing with long, free-form SQL
+* Distributed transactions across multiple shards
+* Queries that return data-heavy ETL results rather than summaries
+
+These constraints come from the fact that we operate across many nodes (as compared to a single node database), giving you easy horizontal scaling as well as high availability.