Aren't we using star schemas or flocon schemas to create datamarts ?
So can we say that Datamarts are synonym of star schema?
Yes or no, I need justification please
No, you can't say that Data Mart is a synonym of a star schema - it is a broader concept.
Data Mart is a specialized data warehouse - it's a platform that consists of hardware, software and data.
Star Schema is a data structure optimized for querying. It's one of the components of a Data Mart, and not the only type of structures available (i.e, you can use a flat table instead).
Related
I am going to design a Datawarehouse (although its not an easy process). I am wondering through out the ETL process , how the data in the Datawarehouse is going to extract/transform to Data Mart ?
Are there any model design within Datawarehouse vs Datamart ? Also usually starschema or snowflake?so should we place the table like in the following
In Datawarehouse
dim_tableA
dim_tableB
fact_tableA
fact_tableB
And in Datamart A
dim_tableA (full copy from datawarehouse)
fact_tableA (full copy from datawarehouse)
And in Datamart B
dim_tableB (full copy from datawarehouse)
fact_tableB (full copy from datawarehouse)
is it something real life example which can demonstrate the model difference between datawarehouse and datamart ?
I echo with both Nick's responses and in more technical way following Kimball methodology:
In my opinion and my experience. At high level ,we have data marts like Service Analytics , Financial Analytics , Sales Analytics , Marketing Analytics ,Customer Analytics etc. These were grouped as below
Subject Areas -> Logical grouping(Star Modelling) ->Data Marts -> Dimension &Fact (As per Kimball’s)
Example:
AP Real Time ->Supplier, Supplier Transaction’s , GL Data -> Financial Analytics + Customer Analytics->Physical Tables
Data marts contain repositories of summarized data collected for analysis on a specific section or unit within an organization, for example, the sales department. ... A data warehouse is a large centralized repository of data that contains information from many sources within an organization.
Depending on their needs, companies can use multiple data marts for different departments and opt for data mart consolidation by merging different marts to build a single data warehouse later. This approach is called the Kimball Dimensional Design Method. Another method, called The Inmon Approach, is to first design a data warehouse and then create multiple data marts for particular services as needed.
An example: In a data warehouse, email clicks are recorded based on a click date, with the email address being just one of the click parameters. For a CRM expert, the e-mail address (or any other customer identifier) will be the entry point: opposite each contact, the frequency of clicks, the date of the last click, etc.
The Datamart is a prism that adapts the data to the user. In this, its keys to success depend a lot on the way the data is organized. The more understandable it is to the user, the better the result. This is why the titles of each field and their method of calculation must stick as closely as possible to the uses of the trade.
As far as I know, normalization is done to avoid inconsistency in the database.
By normalizing we:
reduce data redundancy, and
protect data integrity.
That's why most OLTP databases are in 3NF.
Different databases from OLTP come together in a data warehouse. (DWH, OLAP). DWHs are denormalized (1FN), and is obvious it has to be like that, because the main table of a DWH has hundreds of columns.
From that DWH we can build several data marts that we would later use for doing analysis with a BI reporting tool (Cognos, QlikView, BO .. )
The problem is that the data model for the BI report is not normalized.
Couldn't that be a problem for redundancy and data integrity for the report?
In OLAP systems (such as data warehouses), the key efficiency needs are in querying and data retrieval.
Therefore some of the design considerations are done in order to retrieve the information faster, even if the updates may be longer.
An example for such a model is a Star-Schema on which we denormalize data in a such way that all the data will be stored in a 1-join-hop distance.
Key elements such as transactions are located at the big table (Facts), with foreign keys pointing at the dimensions.
The dimension themselves are smaller, and may contain not-normalized data. For example an address dimension may store street, neighborhood and city data without normalizing it to 3NF.
There are for sure redundancy issues (you don't really have to store Day_of_Week per each date row) but it is insignificant (since storage is not a bottleneck in this scenario).
As per integrity - you face it only on updates (F.E. a less-realistic scenario of country change per State_Province in Dim_Store) , and in DWH update is a rare-case, where we allow ourselves to be inefficient.
Moreover - integrity is not enforced by the DB (or normalization) but by design and implementation of the ETL process.
Read more on Data Warehouses modeling
Regarding redundancy: some data warehouse engines like Amazon Redshift allow data compression that is very handy for denormalization. Let's say you have a table of sales events with 100M records and every sale has a city. In OLTP data model, you would have sales and cities with city_id connecting them. In OLAP data model with compression allowed it's much easier to have sales table with a text city attribute compressed. You'll be able to calculate sales by city without joining tables, and your city values won't occupy much disk space because they will be encoded.
More info about compression is in Amazon docs: Choosing a Column Compression Type
Regarding data integrity: you have to design your ETL routines to minimize the possibility of duplicate data and also run scheduled checks for duplicates based on criteria like this:
select count(*) from table;
select count(*) from (select distinct <expression> from table);
where is a list of columns which combination should be unique (your artificial primary key).
I'm trying to design a Data Warehouse for a single store of commonly required data ranging from finance systems, project scheduling systems and a myriad of scientific systems. I.e. many different data marts.
I have been reading up on Data Warehousing and popular methods such as Star Schemas and Kimball methods etc but one question I cannot find answer to is:
Why is it better to design your DW Data Mart as a star schema rather than a single flat table?
Surely having no joins between facts and attributes/dimensions is faster and simpler than having lots of small joins to all the dimension tables? Disk space is not a problem, we'll just throw more disks at the database if necessary. Is the star schema slightly outdated these days or is it still data architect dogma?
Your question is very good: the Kimball mantra for dimensional modelling is to improve performance and to improve usability.
But I don't think it is outdated, or dogma- it is a reasonable, practical approach for many situations and platforms.
The way relational DBs store data means there's a balancing act to be struck between the numbers and types of tables, the routes in to the data for typical queries, easy maintainability and description of relationships between data, the numbers of joins, the way the joins are constructed, the indexability of columns, etc.
3NF (or further) is one end of the spectrum, suiting OLTP systems, and a single table is the other end of the spectrum. Dimensional models are in the middle and appropriate for reporting, at least when using certain technologies.
Performance isn't all about 'number of joins', although a star schema performs better for reporting workloads than a fully normalised database, in part because of a reduce number of joins. Dimensions are typically very wide. If you are including all those dimension fields in every row of every fact, you have very large rows indeed, and finding your way into those rows will perform very badly for typical queries.
Facts are numerous, so if you can make those tables compact, with the 'wordier' dimensions filterable, you hit a sweet spot of performance that a single table isn't going to match, unless heavily indexed.
And yes a single table for a fact is simpler in terms of numbers of tables but is it really easier to navigate? Dimensions and facts are easy concepts to understand, and what if you want to cross you queries across facts? You've got many different data marts but one of the benefits of having a data warehouse in the first place is that these aren't distinct- they're related and can be reported across. Conformed dimensions enable this.
If you combine your fact and dimensions into a single table, you'll either lose the visibility into dimension attributes that have never been used, or your measures will be thrown off by inclusion of a dummy event for the unused dimension attribute.
For example, a restaurant menu is a dimension and the purchased food is a fact. If you combined these into one table, how would you identify which food has never been ordered? For that matter, prior to your first order, how would you identify what food was available on the menu?
The dimension represents the possibilities, the fact represents the realization of the possibilities.
Combining facts and dimensions in the same table limits the scalability and the flexibility.
Suppose that one day the business decides to change a dimension description ( for example the product name ). Dimension tables aren't as deep as the fact tables and the update process or SCD management should be easier and less resource intensive.
I trying to know the difference between Data Mart and DSS
When I check the info in Internet about DSS vs DWH. I found that .
"Data warehouse is often the componet taht stores data for a DSS".
The problem is that as long as i know DWH is too the componet that stores data for a Data Mart.
so
What is the difference between a DSS and a Data Mart?
Thanks in advance , Enrique
More appropriate question would be: What is similar with Data Mart and DSS?
Data mart is subject oriented set of related tables where you have one fact table (transactions) and multiple dimension tables (categories). Example: Data mart of sales. Fact table (salesID,agentID,categoryID,dateID,amount,quantity). Dimension Agent (AgentID, AgentName, AgentType, etc)
Data Warehouse (it's database) is centralised repository of aggregated data from one or multiple source aimed to serve for reporting purpose. It's usually denormalized. It could be based on data marts or one logical data model in 3rn normalisation form.
DSS is information system, it's not database neither entity. It lies on data, but it also have it own's model and user interface. Model is critical for decision recommendation engine.
What may led you to misunderstands is because some of DSS lies on DWHs, specifically on Kimball (Data Marts) types of DWHs.
I have a relational database (about 30 tables) and I would like to transpose it in a neo4j graph database, and I don't know where to start...
Is there a general way to transpose tables and/or tuples into a graph model ? (relations properties, one or more graphs ?) What are the best sources of documentation ?
Thanks for any help,
Best regards
First, if at all possible, I'd suggest NOT using your relational DB as your "reference" for transposing to a graph model. All too often, mistakes and pitfalls from relational modelling get transferred over to the graph model and introduce other oddities. In fact, if you have a source ER diagram, that might be an even better starting point as it's really already a graph. And maybe even consider a re-modelling exercise for your domain!
That said, from a basic point of view, you can think of most tables as representing a node type (e.g. "User" or "Movie") with join tables and keys representing relationship types.
A great starting point, from my perspective anyway, is to determine some questions your graph/data source should answer. Write those questions down, and try to come up with Cypher queries that represent the questions. Often times, a graph model naturally arises from such an effort, and it's really not that difficult.
If you haven't already, I'd strongly recommend picking up a (free) copy of the Graph Databases ebook from here: http://graphdatabases.com/
It's jam-packed with a lot of good info on where to start with modelling your domain and even things to consider when you're used to doing things in a relational manner. It also contains some material on Cypher, although the Neo4j site (neo4j.org) has a reference manual with plenty of up-to-date info on Cypher.
Hope this helps!
There's not going to be a one-stop-shop for this kind of conversion, as not all data models are appropriate for graph modeling, and every application is a unique special snowflake...but with that said.....
Generally, your 'base' tables (e.g. User, Role, Order, Product) would become nodes, and your 'join tables' (a.k.a. buster tables) would be candidates for your relationships (e.g. UserRole, OrderLineItem). The key thing to remember that in a graph, generally, you can only have one relationship of a given type between two specific nodes - so in the above example, if your system allows the same product to be in an order twice - it would cause issues.
Foreign keys are your second source of relationships, look to them to see if it makes sense to be a relationship or just a property.
Just keep in mind what you are trying to solve by your data model - if it's traversing your objects to find relationships and distance, etc... then graphs may be a good fit. If you are modeling an eCommerce app, where you are dealing with manipulating a single nested object (e.g. order -> line item -> product -> sku), then a relational model may be the right fit.
Hope my $0.02 helps...
As has been already said, there is no magical transformation from a relational database model to a graph database model.
You should look for the original entities and how they are related in order to find your nodes, properties and relations. And always keeping in mind what type of queries you are going to perform.
As BtySgtMajor said, "Graph Databases" is a good book to start, and it is free.