I'm new to Analysis Services
My first cube has been deployed and it seems to work.
Dimension tables are ok and fact tables are ok.
My question is very simple : If I add a new record in the related datasource table,
Browsing the cube, I don't see the new record until process again the cube.
In my mind I think if new records are addedd, then cube must reflect the changes.
How to solve this issue? Do I need to reprocess the cube every time a new record is added? This is impossible of course.
You understand that essentially your cube represents a bunch of aggregated measures? That means that when the cube is processed it looks at all the data that is in your fact tables and processes the Measures (according to the dimensions).
The result of this is that you're able to access the data in the cube quickly and efficiently. The downside is as you have mentioned is that when new data is added to the fact table the cube isn't updated.
Typically there will be a daily batch job that will update the cube with the latest fact data, depending on the amount of data you have and the "real-time" requirements this could be done more than once p/day. A lot of people do this out of hours.
If you look closely in BIDS you will notice on the Partitions tab that for each partition it has a Storage Mode which you can define.
I would recommend you read this this article http://sqlblog.com/blogs/jorg_klein/archive/2008/03/27/ssas-molap-rolap-and-holap-storage-types.aspx
Basically, there are a few different modes you can use:
MOLAP (Multi dimensional Online Analytical Processing)
MOLAP is the most used storage type. Its designed to offer maximum query performance to the users. Data AND aggregations are stored in optimized format in the cube. The data inside the cube will refresh only when the cube is processed, so latency is high.
ROLAP (Relational Online Analytical Processing)
ROLAP does not have the high latency disadvantage of MOLAP. With ROLAP, the data and aggregations are stored in relational format. This means that there will be zero latency between the relational source database and the cube.
Disadvantage of this mode is the performance, this type gives the poorest query performance because no objects benefit from multi dimensional storage.
HOLAP (Hybrid Online Analytical Processing)
HOLAP is a storage type between MOLAP and ROLAP. Data will be stored in relational format(ROLAP), so there will also be zero latency with this storage type.
Aggregations, on the other hand, are stored in multi dimensional format(MOLAP) in the cube to give better query performance. SSAS will listen to notifications from the source relational database, when changes are made, SSAS will get a notification and will process the aggregations again.
With this mode it’s possible to offer zero latency to the users but with medium query performance compared to MOLAP and ROLAP.
To get the real-time reporting without having to reprocess your cube you will need to try out ROLAP, but beware, the performance will suffer (depending on the size of your cube and server!).
Related
We have a fairly large SSAS Tabular cube with many different tables (some which contain measures and dimensions, etc). On occasion we will run into scenarios where I have to optimize the cube partitions (break them into smaller parts) or cube structure so that not as much memory is consumed when it processes (daily). Occasionally we've had to increase the memory limits of the server just to make sure the job doesn't crash. One of our sql server consultants asked if we had considered changing the process mode on the scripted job to 'Default' rather than 'Full' (since every table in the script is set to full in the process mode). I said I hadn't considered this but my concern is that, it seems based on my research, that default won't actually update the data but will really only replenish the tables structure if it changes in some way. I need a processing mode that will just pull in any new rows (and update any rows that have changed) since the last time the partition was processed. Is there any mode which accomplishes this rather than Process full (which obviously wipes the current partition it's processing and rebuilds the entire thing = memory intensive)? Anything less memory intensive that will still pull in new rows and update outdated ones?
fyi, all the tables are based on sql queries
One option is doing a Process Data instead of a Process full on the tables in your tabular model. You may also want to consider implementing partitioning in your tables in order to take advantage of the ability for SSAS to process these in parallel. Since your tables are already based on SQL queries, you'll only need to modify the filters in the queries to make the data uniform across multiple partitions. Partitioning the tables will also allow for incremental processing using Process Add to incrementally update the partition. Looking into other ways to reduce unnecessary memory, such as removing unused columns and replacing calculated columns where possible (read about the cost calculated columns here) will also help the memory issues.
I was assigned a task to design a database schema using operational model, but unfortunately unable to get any information on what exactly the operational model mean.
I worked extensively on Star and snowflake schemas but management doesn't require these schema designs and stressing me to create a operational model.
If anyone can give some insight on what operational model and how can I get some knowledge would be great help.
Operational Model is a model which would help in daily operations and not just getting statistics about the data.
The management is looking for a daily Transactional Processing Database Systems whereas the snowflake and the star schema are great designs for Analytical Processing Systems and Warehouses but not for daily transactional Processing Systems. They are looking for an OLTP system than an OLAP System.
OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by a number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).
OLAP (On-line Analytical Processing) is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).
New to DW concepts and SSAS. I'm reading alot that normalized relational dbs are optimal for OLTP due to a typical workload of many one-transaction batches. And denormalization is generally better for DW/BI applications because the nature of queries used for reporting are more batch-based... there were other reasons that I don't recall right now.
It sounds like the advice says to create a denormalized model and populate it from the base relationship model and then build your cubes off the denormalized model. Assuming you're using MOLAP storage type, your cube will store and incrementally update your data in a multidimensional model that it builds behind the scenes.
So now we have essentially the same data stored three times!
Am I reading that right? Why do we even need that intermediate denormalized table? It can't be to optimize report queries because those are being run against the multidimensional SSAS data store. Why not just build your cubes against a dsv whose definition is basically a view of the relational db?
The multidimensional model needs the relational model to be available in star schemas (that is what you call "denormalized model") for loading the data. And in many cases, there is some processing like combining data from different sources, keeping the data for reporting longer than it is needed in the OLTP world, keeping historical views like old regional or department structures available for analyzing which are not necessary and hence overwritten in the OLTP world. Hence, this intermediate step makes sense in many cases. You might also want to have clear cut of times, i. e. always report data for complete days (or, in some cases, months), and not have some data for the last day available and some not, which makes comparison of numbers for a day easier than comparing e. g. the sales of today containing only the data up to 10 o'clock with the sales of the whole day yesterday.
In some simple cases, the intermediate relational data structure need not be available physically. A few days ago I prepared a prototype cube where the star schema was just a set of views on the source data. In this case, of course, the data was only physically available in the original source form and in the cube. The structure of the source data did not make the views that inefficient, and thus data loading to the cube was fast enough for the prototype.
I'm starting to study SQL Server Analysis Services and I'm working my way through the training book, as well as the Developer Training Kit. In both, I find suggestions that the number of tables used in an OLAP database (ideally, star schema) is greatly reduced from the production OLTP database.
From the training kit:
We followed the data dimensional methodology to architect the data mart schema. From some 200 tables in the operational database, the data mart schema contained about 10 dimension tables and 2 fact tables.
From what I understand, the operational databases are usually (somewhat) normalised and the data mart schemas are heavily denormalised. I also believe that denormalising data usually involves adding more tables, not less.
I can't see how you can go from 200 tables to 12, unless you only need to report on a subset of data. And if you do only need to report on a subset of data, why can't you just use the appropriate tables in the operational database (unless there are significant performance gains to be made by using a denormalised star schema)?
Denormalizing is exactly the opposite of Normalizing a database. In a normalized database everything is spit apart into different tables to support concurrent writes to the data. This also has the side effect of generating any given subset of data exactly once (In an ideal 3rd normal form data structrure). A draw back of normalizing is that reads take a lot longer because of the fact that the data is scattered and we need to join tables to make sense of it again (Joins are pretty expensive operations).
When we denormalize, we are taking the data from multiple tables and merging them in to one table. So now we have repeating data in these tables. The repeating data is useful because we don't have to make joins to any other table to get it anymore. Writing to the data store is normally a bad idea because it would mean alot of writes to change all of the data in a table, whereas it would only take one in a normalized database.
OLTP stands for Online Transactional Processing, notice the word Transactional. Transactions are write operations and the OLTP model is optimiized for this. OLAP stands for Online Analytical Processing, Analysis being the keyword meaning lots of reads.
Going from 200 tables to 12 in an OLTP to OLAP process will suprisingly hold nearly all of the data in the OLTP database plus more. The OLTP is unable to record all of the changes over time, but OLAP specializes in this so you get all of your historical data as well as current data.
The star schema is probably the most common for OLAP data stores, the snowflake schema is also pretty common. You should learn about both and how to properly use them. It's just another great tool in your arsenal.
These two books from IBM will answer your questions much more thouroughly and they are free pdf's.
http://www.redbooks.ibm.com/abstracts/sg247138.html
http://www.redbooks.ibm.com/abstracts/sg242238.html
I need to extract some management information (MI) from data which is updated in overnight batches. I will be using aggregate functions to generate the MI from tables with hundreds of thousands and potentially millions of rows. The information will be displayed on a web page.
The critical factor here is the efficiency of SQL Server's handling of aggregate functions.
I am faced with two choices for generating the data:
Write stored procs/views to generate the information from the raw data which are called every time someone accesses a page
Create tables which are refreshed daily and act as a cache for the MI
What is the best approach to take?
Cache the values during your nightly load if the data doesn't change throughout the day. It will make retrieval much faster. I'm a big fan of summary tables when necessary. In your case, they're necessary!
One thing you may want to look into, since you own SQL Server, is Analysis Services. By creating a Multidimensional Database, or a cube, these aggregations all happen automagically, and you can drill down and across your data to find numbers at the speed of thought, instead of trying to write reports that capture all of those numbers. Spend 10 minutes and watch the intro video of it, and I think you'll garner a real appreciation for SSAS's power.
It sounds to me like an Analysis Services Cube would actually be the best fit to your problem. The cube processesing can be run after the data loads occur to aggregate the data for later use.
However, you could also possibly use an indexed view, which if designed correctly and used in conjunction with the NO EXPAND table hint can provide a significant performance increase.
SQL 2005 Indexed Views
SQL 2008 Indexed Views