Is a Star-Schema design essential to a data warehouse? Or can you do data warehousing with another design pattern?
Using star schemas for a data warehouse system gets you several benefits and in most cases it is appropriate to use them for the top layer. You may also have an operational data store (ODS) - a normalised structure that holds 'current state' and facilitates operations such as data conformation. However there are reasonable situations where this is not desirable. I've had occasion to build systems with and without ODS layers, and had specific reasons for the choice of architecture in each case.
Without going into the subtlties of data warehouse architecture or starting a Kimball vs. Inmon flame war the main benefits of a star schema are:
Most database management systems have facilities in the query optimiser to do 'Star Transformations' that use Bitmap Index structures or Index Intersection for fast predicate resolution. This means that selection from a star schema can be done without hitting the fact table (which is usually much bigger than the indexes) until the selection is resolved.
Partitioning a star schema is relatively straightforward as only the fact table needs to be partitioned (unless you have some biblically large dimensions). Partition elimination means that the query optimiser can ignore patitions that could not possibly participate in the query results, which saves on I/O.
Slowly changing dimensions are much easier to implement on a star schema than a snowflake.
The schema is easier to understand and tends to involve less joins than a snowflake or E-R schema. Your reporting team will love you for this
Star schemas are much easier to use and (more importantly) make perform well with ad-hoc query tools such as Business Objects or Report Builder. As a developer you have very little control over the SQL generated by these tools so you need to give the query optimiser as much help as possible. Star schemas give the query optimiser relatively little opportunity to get it wrong.
Typically your reporting layer would use star schemas unless you have a specific reason not to. If you have multiple source systems you may want to implement an Operational Data Store with a normalised or snowflake schema to accumulate the data. This is easier because an ODS typically does not do history. Historical state is tracked in star schemas where this is much easier to do than with normalised structures. A normalised or snowflaked Operational Data Store reflects 'current' state and does not hold a historical view over and above any that is inherent in the data.
ODS load processes are concerned with data scrubbing and conforming, which is easier to do with a normalised structure. Once you have clean data in an ODS, dimension and fact loads can track history (changes over time) with generic or relatively simple mechanisms relatively simply; this is much easier to do with a star schema, Many ETL tools (for example) provide built-in facilities for slowly changing dimensions and implementing a generic mechanism is relatively straightforward.
Layering the system in this way providies a separation of responsibilities - business and data cleansing logic is dealt with in the ODS and the star schema loads deal with historical state.