I've been tasked with coming up with a recommendation of how to proceed with a EDW and am looking for clarification on what I'm seeing. Everything that I am learning about states that Kimball's approach will bring value quicker to business vs Inmon's. I get that Kimball's approach is a dimensional model from the getgo and different data marts (star schema) are integrated through conformed dimensions... thus the theory is I can simply come up with my immediate DM to solve business need and go on from there.
What I'm learning states that Inmon's model suggests that I have a EDW designed in 3NF. The EDW is not defined by source system but instead the structure of the business, Corporate Factory (Orders, HR, etc.). So data from disparate systems map into this structure. Once the data is in this form, ETLs are then created to produce DMs.
Personally I feel Inmon's approach is a better way. I believe this way is going to ensure that data is going to be consistent and it feels like you can do more with this data. What holds me back with this approach though is everything I'm reading says it's going to take much more time to deliver something but I'm not seeing how that is true. From my narrow view, it feels like no matter what the end result is we need a DM. Regardless of using Kimball's or Inmon's approach the end result is the same.
So then the question becomes how do we get there? In Kimballs approach we will create ETLs to some staging location and generally from there create a DM. In Inmon's approach I feel we just add in another layer... that is from the staging area we load this data into another database in 3NF organized by function. What I'm missing is how this step adds so much time.
I feel I can look at the end DM that needs to be made. Map those back to a DW in 3NF and then as more DMs are requested keep building up the DW in 3NF with more and more data. However if I create a DM in Kimballs model that DM is going to be built around the level of grain decided for that DM and what if the next DM requested wants reporting at even a deeper grain (to me it feels like Kimballs methodology would take more work) and with Inmon's it doesn't matter. I have everything at the transnational level so DMs of varying grains are requested, well I have the data, just ETL it to a DM and all DMs will report the same since they are sourced from the same data.
I dunno... just looking for others views. Everything I read says Kimball's is quicker... I say sure maybe a little bit but there is certainly a cost attributed by going to quicker route. And for sake of argument... let's say it takes a week to get a DM up and running through Kimballs methodology... to me it feels like it should only take 10% maybe 20% longer utilizing Inmon's.
If anyone has any real world experience with the different models and if one really takes so much longer then the other... please share. Or if I have this so backwards tell me that too!
For context; I look after a 3 billion record data warehouse, for a large multi-national. Our data makes its way from the various source systems through staging and into a 3NF db. From here our ELT processes move the data into a dimensionally modelled, star schema db.
If I could start again I would definitely drop the 3NF step. When I first built that layer I thought it would add real value. I felt sure that normalisation would protect the integrity of my data. I was equally confident the 3NF db would be the best place to run large/complex queries.
But in practice, it has slowed our development. Most changes require an update to the stage, 3NF and star schema db.
The extra layer also increases the amount of time it takes to publish our data. The additional transformations, checks and reconciliations all add up.
The promised improvement in integrity never materialised. I realise now that because I control the ETL, and the validation processes within, I can ensure my data is both denormalised and accurate. In reporting data we control every cell in every table. The more I think about that, the more I see it as a real opportunity.
Large and complex queries was another myth that has been busted by experience. I now see the need to write complex reporting queries as a failing of my star db. When this occurs I always ask myself: why isn't this question easy to answer? The answer is most often bad table design. The heavy lifting is best carried out when transforming the data.
Running a 3NF and star also creates an opportunity for the two systems to disagree. When this happens it is often a very subtle difference. Neither is wrong, per se. Instead, it is possible the 3NF and star query are asking slightly different questions, and therefore returning different results. Although technically correct, this can be hard to explain. Even minor and explainable differences can erode confidence, over time.
In defence of our 3NF db, it does make loading into the star easier. But I would happily trade more complex SSIS packages for one less layer.
Having said all of this; it is very hard to recommend an approach to anyone without a deep understanding of their systems, requirements, culture, skills, etc. Having read your question I am sure you have wrestled with all these issues, and many more no doubt! In the end, only you can decide what the best approach for your situation is. Once you've made your mind up, stick with it. Consistency, clarity and a well-defined methodology are more important that anything else.
Dimensions and measures are a well proven method for presenting and simplifying data to end users.
If you present a schema based on the source system (3nf) to an end user, vs a dimensionally modelled star schema (Kimball) to an end user, they will be able to make much more sense of the dimensionally modelled one
I've never really looked into an Inmon decision support system but to me it seems to be just the ODS portion of a full datawarehouse.
You are right in saying "The EDW is not defined by source system but instead the structure of the business". A star schema reflects this but an ODS (a copy of the source system) doesn't
A star schema takes longer to build than just an ODS but gives many benefits including
Slowly changing dimensions can track changes over time
Denormalisation simplifies joins and improves performance
Surrogate keys allow you to disconnect from source systems
Conformed dimensions let you report across business units (i.e. Profit per headcount)
If your Inmon 3NF database is not just an ODS (replica of source systems), but some kind of actual business model then you have two layers to model: the 3NF layer and the star schema layer.
It's difficult nowadays to sell the benefit of even one layer of data modelling when everyone thinks they can just do it all in a 'self service' tool! (which I believe is a fallacy). Your system should be no more complicated than it needs to be because all that complexity adds up to maintenance and that's the real issue - introducing changes 12 months into the build when you have to change many layers
To paraphrase #destination-data: your source system to star schema transformation (and seperation) is already achieved through ETL so the 3nf seems redundant to me. You design your star schema to be independent from source systems by correctly implementing surrogate keys and business keys, and modelling it on the business, not on the source system
With ETL and back-end data wrangling taking up about 70% of the project time for this kind of endeavour, an extra layer makes a big difference. Its an extra layer of transforming from source to target, to agree with the business and to test. It all adds up.
Whilst I'm not saying that dimensional models (the Kimball kind) are always easy to change, you've got a whole lot more inflexibility should you have to always change lots of layers when you want to change your BI.
In fact, where I've been consulting in places that have data warehouses that are considered to be inflexible and expensive to develop for, and not keeping pace with changes to the business, they have without exception included the 3NF layer prior to the DMs. As Nick mentioned, it is hard nowadays to sell the idea of a 'proper' data warehouse as opposed to a Data Discovery Bi tool- and the appeal of these is often driven by DWs being seen to be slow and expensive to develop.
Kimball isn't against having a 3NF layer prior to his DW if it makes sense for a situation, he just doesn't agree with Inmon that there's a point.
One common misunderstanding is that Kimball proposes distinct data marts, so that you'd have to change it each time there is a different reporting request. Instead, Kimball's DMs are based on real life business processes and modelled accordingly. Although its true you will then try and make them suitable for reporting, you try and make them so they can answer forseaable queries. You don't aggregate and store just the aggregates: you work with the transactional data in a Kimball dimensional model.
So no need to be reluctant from that perspective.
If an ODS works for you, then go for it- but a Kimball DW will meet the majority of requirements.
I have the following models..
Provider
Patient
Clinic
Like an eco-system, all models should have many-to-many relationships with one another. It's very important that I am able to query data from all directions.
After intensive research on Active Record associations, I find many blogs warning against has_and_belongs_to_many and using has_many :through. Only issue is that requires a table to act as the "middle-man" for lack of better words but I'm unsure how that would work with a 3 models.
The other option is a polymorphic association but I'm unsure if I should invest the time in understanding that method if it's inapplicable for this particular situation.
Any advice on how to create these relationships for maximum flexibility and efficiency?
I have the following models
Tables, if they were modelled for a Relational database, files otherwise.
It's very important that I am able to query data from all directions.
Understood. That is simple and easy in a Relational Database.
Like an eco-system, all models should have many-to-many relationships with one another.
That is not correct.
If each of the three can be related to the other two, then your data is not modelled. There are basic dependencies that you have not identified. Eg: I can imagine that:
Providers provide services at Clinics
Clinics provide services to Patients
Patients visit Clinics to obtain services
Therefore, any relationship that a Patient may have with a Provider is via a Clinic, and not privately, without a Clinic.
Straighten those rules and dependencies out first, that will result in less than three Associative tables second. Something like this:
Clinic Provider Patient Table Relation Diagram
Response to Comment
Any advice on how to create these relationships for maximum flexibility and efficiency?
Well, Dr E F Codd's Relational Model is strongly established as the model, the method for organising data, such that it has (a) complete integrity (objects can't) (b) maximum flexibility (c) maximum speed. In the 45 years since its advent, there have been no other contenders. That is the model I am using. The principles that underpin that model are the principles that I rely upon, when I make my proposals.
All that has been, of course, confirmed and reinforced during my 34 years of database implementations. As well as thousands of other high-end implementations.
Data Independence
I've taken your post with great consideration and the difficulty is that there are many unique nuances with my app that won't allow me to simplify it to mirror the real world.
It is the other way around. The fact is, you are writing an app+db that deals with the real world. Therefore the db has to reflect that real world (limited to the scope of the enterprise) that you wish to engage with. Thousands of modellers have done that successfully (millions have done it incorrectly).
To the extent that you have "nuances" and complexities in the app, you have not modelled the data, as data, and the result will be a complex app that engages with an incorrectly modelled database, or worse, a non-database. All those "nuances" and complexities are in fact data; facts about data; rules about the data; and relationships between data. But you have an established view that the "nuances" and complexities are in the app, in your "models", not in the data.
Therefore your notion is false.
Every rule, constraint, control, nuance, complexity re the data, must be implemented in the data. That is simply data definition as per the RM. Otherwise you have no data independence, no database (just a bunch of files for data storage), no integrity, no relational power, no speed. And worse, you will be forever fixing up the "nuances" and complexities in the app layers, the object stack.
Data Definition
Let me start from first principles.
a databse is a collections of Facts, including relationships between those Facts
Assuming you understand that, there is an important next point.
There is no Fact that cannot be declared.
This is First order Logic, which the RM is based on. Therefore there is no such thing, as a "model" that is too complex (has "nuances" or too many "nuances") to be declared in terms of FOL. The scientific exercise that is called for, is to reduce that complexity to FOL Facts.
To the extent that those Facts are Facts about the real world, and they reflect the truth, your database will be isolated from the effect of change (you can extend the database and the app easily). Eg. Provider, Service, Specilisation are separate Facts.
To the extent that those "facts" are "facts" that you have chosen to store (as being relevant, from the perspective of your app, object design, etc), eg. Provider, Service and Specialisation as a single complex "fact", not discrete Facts, and not a reflection of the real world, your database and objects (a) will be hard to change, and (b) will keep changing, forever, until they are elevated, such that they do reflect the real world. You will have to "re-factor" the "database" every quarter.
Confidentiality
The data is very confidential so I'm reluctant to get to far into the matter.
We have been working with confidential data and/or systems for over 45 years without breaching confidentiality.
There is nothing new under the Sun.
We are dealing with structure, not content, it is not reasonable to suggest that your structure is so new and unique that it cannot be (i) discussed or (ii) modelled.
But most important, if you cannot describe it (in FOL terms), you cannot model it. If you cannot model it, you cannot write an app to engage with it (you can try, but as evidenced here, you will be stuck in that unresolved position).
Noting that the OO/ORM literature teaches people to obsess about the data content, in order to avoid dealing with the structure, meaning, relevance, etc, please note that I do not want to know. and the exercise does not need to know, the content. Describe only the data in terms of meaning, relevance, relationships, and we will model the required structure.
My question was more about how to create a cycle of many-to-many relationships with 3 models than whether my models "should" have them or not.
I think I understand that. That would be adding complexity to an article of which the complexity has been determined to be the fundamental problem. If you ask me to build an airplane without wings, and I tell you that your approach is incorrect, that you need wings, there is no point in telling me that you are seeking someone who can tell you HOW to build an airplane without wings, you have missed the point.
Reasoning
I would love to hear your reasoning if you believe there should never be a situation like this in any database.
Again, you have that the wrong way around. It is not that there should never be a situation like this in any database, it is that if there is a situation like this, it raises a red flag (to qualified and experienced modellers) that the data is not yet Normalised, not yet organised into discrete Facts. That means you need to take a step back and deal with the complexity in your "models", first. Then the relationships that are your current focus will be simplified.
Then, yes, there will not be a situation like this in the database.
My reasoning is Codd's RM, and the principles behind it. It has been the subject of many papers. (As well as many "papers" that are non-relational or anti-relational, such as those that support the OO/ORM "model".)
Specifically, here, that if you have a n-ary relation (technical term for the three-way relationship that you are seeking) that that can be, and should be, resolved into [multiple] binary relations (two-way relationships). Eg. the TRD I have suggested.
OO/ORM Mythology
In the context of "love to hear your reasoning", there are two sides, I have given the what you should be doing side, above. This is the what you should not be doing or why your method is broken side. Where do I start.
The OO/ORM model is that the "database" is merely a storage location to make the objects "persistent", a slave of the objects, and that constructing a monolith, layers of object classifiers, complexity, is the way to solve any problem.
The OO/ORM model is a total, abject failure. It has no scientific basis whatsoever.
(Noting that due to the destruction of the education system, these days "mathematicians" and "theoreticians" write "papers" that "prove" complete and utter nonsense. It is nonsense because it contradicts established science. They are not the class of mathematicians and theoreticians of the old school, who reject contradiction; non-science; nonsensical proofs. The only way that they can write such absurd "proofs", in to maintain a state of ignorance, a state of pathological denial, of other sciences.)
Specifically, they are in denial of the Relational Model (whilst referring to it, to give their papers some credibility, which is a fraud); its prescriptions (such as Data Independence, FOL); its prohibitions, they are in denial of Relational data models (UML cannot model data like IDEF1X can), thus they produce non-relational files, which have no Relational integrity, power, or speed.
They employ the Hammer phylosophy (ie. if one only knows a hammer, then every problem looks like a nai), in staggering denial that Maslow destroyed it, scientifically, over a century ago. Which leads them to pile more layers, more complexity, into the monolith. The converse is of course, to use the right tool for the job, which means define data according to the standard for data, the RM, and separately, objects according to whatever OO philosophy you choose.
They attempt to do everything in objects, to model everything in UML (which is not a standard by any means; nor adequate [one symbol plus a million notations], it has no decomposition, it is in fact a free-for-all in which everyone does their own thing).
The model exists is denial of the fact that since 1980's, in the software industry, we architect, write, and deploy components. Database components in the database, program components in the objects. Not monolithic Towers of Babel, that is pre-1970's technology.
Since 1984, we have had Client/Server and Open Architecture Standards. We have had OLTP Transaction Standards since 1960, restated in the C/S context in 1984. The OO/ORM crowd are in flagrant rebellion of each and every one of those Standards, they build monolithic object stacks sans architecture, sans components, sans Transactions, sans everything. (Apologies to the Bard.)
You might consider what everyone (even cartoonists) knows about the OO/ORM stack, the monolith, the non-architecture and compare it with the deployment of components in the Open Architecture diagram, given above.
Further, they are in denial that every implementation of the "model" is a massive failure, they deny the evidenced facts, and keep adding complexity to the already complex and unmanageable object layers. A "model" that has failed due to its non-architecture, part of which is precisely that failed complexity.
In case their evidenced pathological denial of the reality does not stand, in and of itself, as evidence of insanity, there is more, much more. The Twelve Steppers have an interesting definition of insanity: doing the same thing over and over again, being aware that it produces the same result, every time, but expecting a different result the next time. That doesn't stop them from adding more complexity to the complex model, or from marketing their pre-1970's technology, as "modern" "science".
But that doesn't stop them from writing yet more books and marketing their failed "model".
The OO/ORM crowd exists in isolation from, in pathological denial of, reality
Put another way, it is insane, in 2015, to implement software, in a un-architected monolith, that "does everything", rather than to architect; design; build; and deploy software components in the correct architectural position.
OO/ORM "Model" as Data
The fact that you keep calling your tables "model" is another red flag. That confirms that you have way too much complexity in them. A database consists of simple tables, each reflecting a discrete Fact, not models. To the extent that you consider them "models", you have (usually due to fixed notions re complex objects and classifiers) objects+data+complexity combined, not discrete data and discrete objects. That is the precise problem that will cause the app+db to fail.
So the next step is to (a) shelve the current focus re HOW to relate un-normalised complex "models", and to (b) normalise those "models" such that they are defined in terms of a Relational Database, such that they are discrete Facts. Following which, (c) the relating of the then Normalised tables will be straight-forward.
In one instance, each model acts as a type of 'User' but in another, they don't.
That is exactly the type of mashed-up concept that has to be rationalised, Normalised, such that it is (a) absolutely clear [when it is an user, and when it isn't] (b) defined in data, in FOL terms, as discrete Facts (c) such that you can confidently write code against it, build objects from it. Conversely, the absence of that clarity, the retention of complexity in the object layers, will result in complex objects that fail, and more important, data stored in files that have no integrity, power, or speed.
Self-Contradiction
Consider this. Since you are seeking Relational integrity, power, and speed, you cannot at the same time be
seeking to retain unresolved complexity that is well-known to destroy integrity, power, and speed, or
refusing to implement the requirements the host integrity, power, and speed, that you seek.
It is a massive, and double, contradiction, on your side. It is a philosophical and reasoning issue, that you have to consider and resolve for yourself. The OO/ORM seduces people into believing in magicke, into such crazy self-contradictions.
Regardless, I was very impressed with your answer and really appreciate the time you took to make the diagram.
Thank you. You are most welcome.
That took me all of five minutes. Because I have clarity. Because I follow scientific principles and standards. Because we have had a science and a methodology since 1970. Because we have had a modelling methodology and full notation for modelling Relational Data since 1987 (as a standard, IDEF1X, since 1993). The point is, it is nothing special, the sad fact is, it is not common, and it should be. The second sad fact is, it is unknown in the OO/ORM world.
Further Reading
You may be interested in this Question and Answer. The Answer covers many aspects of Relational Databases, that you will most certainly have to deal with, if not now, then at some point in the future. The minimum reading I draw your attention to right now is:
- Response to Update 3, pages 1, 2 and 6,
- specifically including the embedded link to Predicates
That might give you an idea of the reasoning; the depth of data definition that the RM affords; that all Facts can be declared in terms of FOL Predicates, that the OO/ORM crowd is totally ignorant of.
You may choose to add an Update to your question and ask how to declare one or the other "model", in terms of the RM, as discrete Facts, or open a new Question (and ping me).
Conversely, if you choose to stick to your position, the original question, then I think I have answered it (but the answer raises issues that you must address and resolve).
Please study carefully and comment or ask questions.
I've been developing a very basic core data application for over a year now (Toy Collector, http://bit.ly/tocapp), and I'm looking at doing a redesign so that I can build in iCloud support. I figured while I'm doing that, I might as well update my core data model (if needed), and I'm having a heck of a time tracking down "best practices" for the following:
Currently, I have 2 entities:
Toy, Keywords
Toy has all the information about the object: Name, Year, Set, imageName, Owned, Wanted, Manufacturer, etc, (18 attributes in all)
Keywords has the normalized words to help speed up the search
My question is whether or not there is any advantage to breaking out some of the Toy attributes into their own entities. For example, I could have a manufacturer entity that stores the dozen or so manufacturers, instead of keeping that information in the Toy object. My gut tells me this could reduce the memory footprint (instead of 50,000 objects storing a manufacturer string, there would simple be 12 manufacturer strings in an entity with a relationship to the main Toy entity). Does that kind of organization really matter? Am I trying to overcomplicate things? I just feel like my entity has a lot of attributes, and I'm not sure if taking the time to break it apart into multiple entities would make a difference.
Any advice or pointers would be appreciated!
Zack
Your question is pretty broad, since it addresses the topic of database design. Let me say upfront that it is almost impossible to give you any sensible suggestions, since I would need to know a lot more about your app, use cases, etc. than it is possible through a S.O. question.
Coming to your concrete questions, I would say that you correctly identify one of advantages of splitting a table into multiple ones; actually, the advantage of doing that is not just reducing the database footprint, rather keep data redundancy to a minimum. Redundancy not only affects memory footprint but also manageability and modifiability of your data, and lack of redundancy could even cause anomalies or corruption. There is even a whole database theory topic which is known as database normalisation that addresses this king of concerns.
On the other hand, as it is always the case, redundancy can help performance, and this is actually the case when you can fetch your data through a simple query instead of multiple queries or table joins. There is a technique to improving a database performance which is known as database denormalization and is the exact opposite to normalisation. Your current scheme is fully denormalized.
Using Core Data, which is a relational object graph manager running often on top of SQLite, which is a relational database manager, you have also to take into account the fact that Core Data will automatically build your object graph and fetch into memory the data when you need it. This means that if you can take a smaller memory footprint on disk for granted, this might not be the case when it comes to RAM footprint of your query results (Core Data will "explode", so to say, at some moment your data from multiple tables into one object plus its attributes).
In your specific case, you should also possibly take into account the cost of migrating your existing user base (if the database is not read-only).
All in all, I would say that if your app does not have any database footprint issues at the moment; if you do not feel that creating new tables might be useful, e.g., in order to add new functionality, such as listing all manufacturers; and, finally, if you do not foresee tasks like renaming a manufacturer or such at some point, then maybe refactoring your database will not add much benefit. But, as I say, without knowing your app in detail and your roadmap for it, it is difficult to say anything really on spot. In any case, I hope this general considerations will help you take a decision.
EDIT:
If you want to investigate your core data performance and try to understand where the bottlenecks are, give a try to Instruments/Core Data tool (Product/Profile menu). There are a lot of things that can go bad.
On the other hand, it is really hard to help you further without having more details about the type of searches your app allows to do. One thing that is not clear to me is if your searches are slow only when they return a lot of results or they are slow even when returning a few results.
Normalizing might help performance if you only use (say, after doing a search) just one normalized entity (e.g., to display the toy name in a table). In this case all of the attributes referring to other entities would be faults (hence would not occupy memory nor take) and this might speed up things. But, if you do a search and then display the information from the other tables as well, then there might not be any advantage, quite the opposite, since the faults would have to be resolved immediately and this would produce more accesses to the database.
Also it is true that depending on how you use it, core data could not be the best way to handle your data. Have a look at this Brent Simmons' post relating his experience.
I'm developing a simulation that will feature many entities constantly updating, perhaps 30 times a second. Let's imagine we have 1000 entities, each of which has a velocity, and consequently a position that must be updated every tick.
So, how would you implement this using the actor model? I'm not necessarily using Erlang for this project, but for the sake of argument, let's just say I am. Would you have an actor for each of these entities? Or would you have a "manager" actor that maintains and updates a list of these entities?
Learn You Some Erlang says:
It is true that Erlang processes are very light: you can have hundreds
of thousands of them existing at the same time, but this doesn't mean
you have to use it that way just because you can. For example,
creating a shooter game where everything including bullets is its own
actor is madness. The only thing you'll shoot with a game like this is
your own foot. There is still a small cost in sending a message from
actor to actor, and if you divide tasks too much, you will make things
slower!
So that seems to suggest that managers would be better. Or is there a third option that I'm not seeing?
You say it! there is not one single good solution.
Now to be more helpful, and with the few background I have, I think you should look at these aspects of your project:
You say simulation. If you need to refresh a collection of entities every 30ms, first work to simplify the operations and the data model, and only second think how you can traverse the collection of data efficiently.
On the other end, if you have a huge and/or evolving collection of objects, with trivial algorithm/data model, then look at smarter data structure than lists, take care of the data copy...
If you use a multi-core (or a cluster) then think to have your entities grouped in several super entities in order to take advantage of parallelism, managing them in separate processes.
Next think if these groups can help you to reduce the number of evaluation (have an adaptive time slice? evaluation on demand? ...) .
Last, I think than generally speaking, Erlang is compact and easy to refactor, so take advantage of this an define some functional steps, and for each of them,
make them work, make them right and make then fast (Kent Beck ?)
For the last step you can get some help from the profiling tools such as fproof
Courage :o)
I think Learn You Some Erlang is making a bit of a premature optimization blunder here. You should use which ever abstraction makes the most sense to you, measure any problems, and refactor if necessary. Personally, I believe modeling each particle as its own actor would be the easiest to deal with, and is also the most idiomatic approach for the Actor model. Practically, however, you should do whatever floats your boat.
I have a website backed by a relational database comprised of the usual e-commerce related tables (Order, OrderItem, ShoppingCart, CreditCard, Payment, Customer, Address, etc...).
The stored proc. which returns order history is painfully slow due to the amount of data + the numerous joins which must occur, and depending on the search parameters it sometimes times out (despite the indexing that is in place).
The DB schema is pretty well normalized and I believe I can achieve better performance by moving toward something like a data warehouse. DW projects aren't trivial and then there's the issue of keeping the data in sync so I was wondering if anyone knows of a shortcut. Perhaps an out-of the box solution that will create the DW schema and keep the data in sync (via triggers perhaps). I've heard of Lucene but it seems geared more toward text searches and document management. Does anyone have other suggestions?
How big is your database?
There's not really any shortcuts, but dimensional modelling is really NOT that hard. You first determine a grain and then need to identify your facts and the dimensions associated with the facts. Then you divide the dimensions into tables which allow you to have the dimensions only grow slowly over time. The choice of dimensions is completely practical and based on the data behavior.
I recommend you have a look at Kimball's books.
For a database of a few GB, it's certainly possible to update a reporting database from scratch several times a day (no history, just repopulating from a 3NF for a different model of the same data). There are certain realtime data warehousing techniques which just apply changes continuously throughout the day.
So while DW projects might not be trivial, the denormalization techniques are very approachable and usable without necessarily building a complete time-invariant data warehouse.
Materialized Views are what you might use in Oracle. They give you the "keeping the data in sync" feature you are looking for combined with fast access of aggregate data. Since you didn't mention any specifics (platform, server specs, number of rows, number of hits/second, etc) of your platform, I can't really help much more than that.
Of course, we are assuming you've already checked that all your SQL is written properly and optimally, that your indexing is correct, that you are properly using caching in all levels of your app, that your DB server has enough RAM, fast hard drives, etc.
Also, have you considered denormalizing your schema, just enough to serve up your most common queries faster? that's better than implementing an entire data warehouse, which might not even be what you want anyway. Usually a data warehouse is for reporting purposes, not for serving interactive apps.