Data warehouse design

We have an OLTP DB which contains 5 tables:
Site (SiteID, SiteName)
Well (WellID, WellName, SiteID)
GasEngine (ID, Date, Time, , , , ,
SiteID)
GenSet (ID, Date,
Time, , , , , SiteID)
GasWell (ID, Date, Time, , , , ,
WellID)
We are considering using a single Star Schema with one Fact table (containing all fields from GasEngine, GenSet and GasWell), with a single Cube and 2 measure groups (Site Information, Well Information) and 3 dimensions, DimSite, DimWell and DimDate.
Does this seem suitable?
Or should we have 2 Fact tables, one for GasEngine and one for GasWell which use shared dimensions?
We will be using the Date and Time fields from each table for our time dimension on our Cube. These Date and Time values will be different as coming from
two separate data sources.
How can we handle this?
The hierarchy is:
A Site has many Wells
A Site has Gas/GenSet data
A Well has Gas data

Yes, I can use a view in my DSV for my cube.
However, I am now not sure what the PK of the fact table should be (as this should uniquely identify each row of the table).
We have data coming from 2 data sources - Gas data for the Site and Gas data for the Wells.
The fact table now has 4 FK's:
SiteKey (DimSite)
WellKey (DimWell)
SiteDateKey (DimDate)
WellDateKey (DimDate)
DimDate being a roll playing dimension.
Does this seem correct for the following types of queries:
Gas data for each Site
Gas data for each Well
Well data for each Site
And all Site and Well data can be rolled up and drilled down by Year, Quarter, Month, Week and Day

Similar Messages

Could you please recommend a book or two about data warehouse designing?

Want to read some books about data warehouse and how to build or deal with the problems during the data warehousing process.
Anyone could recommend any book regard to this?
I want the book to mainly talk about the common case scenarios in data warehouse area and the general solutions to those scenarios.
I am quite new in this area, so any recommendation would be highly appreciated.
Thanks.

Perhaps also these resources, if you've not already seen them
DW Best Practices Whitepaper
http://www.oracle.com/technetwork/database/features/bi-datawarehousing/twp-dw-best-practies-11g11-2008-09-132076.pdf
Greg Rahn on the core performance fundamentals of Oracle data warehousing
http://structureddata.org/2009/12/14/the-core-performance-fundamentals-of-oracle-data-warehousing-introduction/

Data warehouse modeling

i am stuck at some of the points and have no clue to what i should do. Please, if you could find it out from someone already there they should know somthing.
1. What do you do with flag indicators and different code attributes in your entity tables. I mean do you include them in your dimension.
2. How do you handle dependent or weak entities when transferring from ERD to dimensional star schema. For example, My Account table has dependent (Aggreement, Suitability,Qualification, Name Address) with one to many relationship how do i handle them. Should i include these entities inside account dimension or directly to the fact.
3. My dimensions are User, Account, Account activity, Time how do we identify which are slowly changing dimensions.
Because when an account goes through steps there are changes made to it. After being saved it can again be put into the cycle again for more changes. i mean very frequent changes. Where do i put the start date and end date if indeed it is a Slowly changing Dimension.
I would really appreciate your help.
plz reply at [email protected]

My 2 cents, I think Gopi is it about rigth on the OLTP side, but I have to disagreewith Gopi on some the data warehouse points.
Data warehouse is generally a broader concept than just OLAP /multi-dimensional model, that would be regarded as just a component of most DWs.
Data warehouses run SQL queries all the time. I would bet the overwhelming majority of BW queries are SQL queries, even for querying OLAP cubes, although MDX is starting to be used more. Operationally, SAP uses SQL to perform a lots of the procesess in BW - loading data, rollup, compress, etc.
The majority of data warehouses are perhaps in the hundreds of GBs, although large enterprises can easiy have TBs of data.
BW can incorporate real time data from R3 with remote cubes.
BW has transactional InfoCubes where users enter data for budgeting, forecasting etc.
You can google hese topics and finds lots of info on data warehouse design.

Streaming OLTP to a Data Warehouse

I am currently working on a project to use Streams to capture changes from a highly normalized OLTP DB and propagates them to a separate DB that will be in the form of a flat data warehouse design. Streams seems to provide great potential to make the reporting from the warehouse near real-time and to avoid the typical nightly ETL through staging tables. Conceptually, I�d like to have a capture process on the source, have an apply process with a DML handler (changes table name, owner, deletes unnecessary columns, and adds columns of queried data) that re-enqueues the LCR at the source, a propagation process that sends the user defined LCR generated by my DML handler package and finally an apply process at the destination site to populate the data.
I have several components of this process working but I can�t get it all to come together. The capture process and apply with the DML handler is no problem. But once the message is re-enqueued the trouble begins. It seems like the message is propagating based on the events and bytes propagated displayed in dba_queue_schedules but I never see the LCR hit the destination queue. Is there something specific that needs to be created since this is now a �user-defined� LCR? The apply process on the destination was created with the apply_captured parameter set to false so I though that this would be enough. Do I need to create a special agent to handle this or create a subscriber for the queue? Any help would be greatly appreciated.
Thanks,
Bill

Thanks for suggesting where to look, Patricia. I don�t have any errors in the error queue, I do have data being propagated as indicated by the DBA_QUEUE_SCHEDULES view, there are no errors associated with the propagation and my apply process is running on the destination side. However, I�m not seeing any messages being read from the queue on the destination side. The V$STEAMS_APPLY_READER view has a state of �DEQUEUE MESSAGES� but the total_messages_dequeued = 0. I guess this makes sense since I never see any data being populated in the strmadmin.streams_queue_table in the destination database. I assume that if the data was propagating correctly I�d see an entry here since my source apply process uses a DML handler to re-enqueue the lcr for propagation thus making it a non-captured event?
Any suggestions of what to look for next?

Architectural Design - New Data Warehouse

Hello All,
This is my first post to the oracle discussion forums and I'm looking forward to the interactions with other ODWB users.
I am just begining to implement a design for a new data warehouse, our team has already defined user requirements for a subset of the business (Sales/Marketing) and have committted a logical model to paper. We have installed our dev environment and are now ready to begin the work of creating our prototype.
I've read all the Oracle doc I can get my hands on regarding implementing your DW objects and have been pondering the approach. ROLAP or MOLAP.....
it seems to make sense that we should deploy into a ROLAP environment bringing in all our data from our staging area to create a stable relational data store. Then select most used or queried dimensions and facts to deploy in a MOLAP environment... has anyone used this approach? any lessons learned? do you have to choose one method or the other? or can you take a blended approach ? would you deploy both in the same database instance or seperate the two?
thx

I'm somewhat new to OWB coming from an Informatica background but in our environment, we are doing the same thing. Our Enterprise Data Warehouse will be based on ROLAP and I intend to use MOLAP for subsets of the EDW.
Dimensions in Oracle are somewhat interesting in that they are "leveled" and you can tie cubes or "fact tables" to any level of the dimension. This is a bit un-Kimball-like and has taken some getting used to. I think it is a powerful feature but I will have to experiment some until I understand it better.
One critical bug with 10.2 I've run into is with dimension roles - The time dimension for instance. Typically this is one table that is aliased many many times. If you exceed roughly 5 roles for the time dimension, the generation of the object fails since OWB generates a single anonymous PL/SQL block that exceeds 64k. Its a documented bug in development with no workaround according to metalink.
Other gotchas are that table changes always try to generate "create table" scripts even if you only add an index or change parallelism. We have had to do table maintenance outside OWB and then keep the metadata in sync up until now.
I haven't done any of the MOLAP yet but from what I read there are some restrictions - such as you can't have roles on dimensions for MOLAP and I believe you can't have SCDs in MOLAP. I don't know how Time dimensions are handled in MOLAP without roles! Do people really generate tables for every single time dimension in OWB???
Hope you share your experiences here!
- Mike Taylor

Designer Vs. Oracle Data warehouse builder

Dear all,
Currently I'm responsible of building a Data warehousing project using Oracle database. I'm trying to decide on a tool for modelling my datawarehouse. I have two options:
1) Designer: we have some experience with this tool and we are using it for our main OLTP application.
2) Oracle Data Warehouse builder: we are using this to design our ETL processes.
I want to get some advice on whether the OWB is capable of modelling my datawarehouse and of doing a retrofit action. also, I try to standardize on the tools that are using in the Data Warehouse department (currently we are using only OWB).
I will appreciate for any other advice to help in my selection process.
Best Regards,
Bilal

Hi,
In my experience this choice depends on the implementation of the datawarehouse. If you are building a "pure" Kimball style dimensional data warehouse you should be able to do this using OWB. I have architected such a DW in the past using only OWB, so I am speaking from experience.
If on the other hand you are planning to implement an Inmon style CIF, if your requirements includes an operational data store (ODS), or if you for any other reason anticipate that you are going to be doing a lot of ER modeling, then I would not recommend using the current release of OWB for modelling. (Note however that there are significant improvements to the modelling capabilities in the Paris release of OWB, so this may change in the future)
The advantage of improved maintainability when using a single tools needs to be weighted against the improved functionality if you choose a combination of the two. In the "two tool" scenario strict development and deployment routines need to be enforced to avoid that the model in Designer comes out of sync with the metadata in OWB. (Consider the effect of a developer making a change to a table definition in OWB and deploying it directly to the database without updating the model in Designer.)
Hope this helps.
Regards,
Roald

Design the data warehouse around the reporting system?

Hi All,
A Jr. data warehouse developer resisted my suggestion to flatten out activity tables of differing grains into a single fact table. (Think sales order header, sales order detail, and even a 3rd level of details to each sales order detail.) Although
he agreed that flattening out the fact tables into a single fact would be proper for a data warehouse, he's concerned that report developers will have an easier time querying the data warehouse with the 3 separate fact tables. I'm not sure if it's because
the report developers don't like learning new schemas or if their reporting tool is just severely limited, mainly because I've never used Cognos. I assured him that a properly-designed data warehouse will save on query execution time, but he's concerned
about the reporting tool and how it may not work so well with the data warehouse.
Did I give him the proper advice? It seems like a data warehouse should be built properly regardless of reporting tool shortcomings. Assuming this tool is lousy, maybe they need a new reporting system for their new data warehouse.
Thanks,
Eric

Hi Eric,
one of the hard and fast rules of building a data warehouse is that from a logical point of view the fact table presents data at a certain level of granularity and that you do not mix facts in fact tables. This is data warehousing 101.
From your comment you seem to be suggesting mixing data of different granularity in the one table.
Now, we have ways and means of co-habiting data that will appear as different fact tables in the one physical table. We control the physical placement of data in fact tables. But on SQL Server we would never mix facts at different granularities or representing
different data in the one fact table. SQL Server supports that quite poorly.
It is sad that in 2015 people are still messing up data warehouse project from pure ignorance of what is available. We have data warehouse data models that are extremely extensive but people just have to start from scratch and reinvent the wheel and fail over
and over again. Sad but true.
Best Regards
Peter Nolan

How do I design this in data warehouse?

I am working on building a data warehouse for insurance quote data.
Each quote will have an applicant and can have an optional co-applicant. Each applicant and co-applicant will have prior auto insurance history, prior home insurance history, current auto insurance information and current home insurance information.
So do I create Applicant and Insurance dimensions here?

Hi Ashan,
Just so you know.
I completely reworked our methodology of building data warehouses back in 2012. The new way of building data warehouses is quite different to the old way. The way you listed.
The methodology presentation is on this link.
https://www.youtube.com/watch?v=Df4CgOtrFq8
Video channels are here. http://www.instantbi.com/videos/
Downloads are here: http://www.instantbi.com/company/downloads/
I have been doing BI since 91 and what we have done now is industry leading.
I am an MSDN so we do our development on MSFT first and then deploy where ever our clients want us to deploy.
Best Regards
Peter Nolan

Best practice of metadata table in data warehouse environment ?

Hi guru's,
In datawarehouse, we have 1. Stage schema 2. DWH(Data warehouse reporting schema). In stageing we have about 300 source tables. In DWH schema, we are creating the tables which are only required from reporting prespective . some of the tables in stageing schema, have been created in DWH schema as well with different table name and column names. The naming convention for these same tables and columns in DWH schema is more based on business names.
In order to keep track of these tables we are creating metadata table in DWH schema say for example
Stage                DWH_schema
Table_1             Table_A
Table_2             Table_b
Table_3             Table_c
Table_4              Table_DMy question is how do we handle the column names in each of these tables. The stage_1, stage_2 and stage_3 column names have been renamed in DWH_schema which are part of Table_A, Table_B, Table_c.
As said earlier, we have about 300 tables in stage and may be around 200 tables in DWH schema. Lot of the column names have been renamed in DWH schema from stage tables. In some of the tables we have 200 column's
so my concern is how do we handle the column names in metadata table ? Do we need to keep only table names in metadata table not column names ?
Any idea will be greatly appriciated.
Thanks!

hi
seems quite a buzzing question.
In our project we designed a hub and spoke like architecture.
Thus we have 3 layer, L0 is the one closest to the source and L0 table's name are linked to the corresponding sources names by mean of naming standard (like tabA EXT_tabA tabA_OK1 so on based on implementation of load procedures).
At L1 we have the ODS , normalized model , we use business names for table there and standard names for temporary structures and artifacts
Both L0 an L1 keep source's column names as general rule, new columns like calculated one are business driven and metadata are standard driven.
Datamodeler fits perfect for modelling L1 purpose.
L2 is the dimensional schema business names take place for tables and columns eventually rewritten at presentation layer ( front end tool )
hope this helps D.

Unread      Implementing heirarichal structure in data warehouse

I want to create a data warehouse for credit card application. Each user can have a credit card and multiple supplementary credit cards. Each credit card has a main limit, which can be sub-divided into sub-limits to supplementary credit cards as requested by the user. Let us consider the following example:
User “A” has a credit card “CC” with Limit “L” and its limit is $100,000.
User “A” requested for a supplementary credit card “CC1” which is assigned limit
“L1” = $50,000. He requests for another supplementary credit card “CC2” which is assigned limit “L2” = $100,000.
Source tables contain data like this:
1. src_client_card_trans: contains transaction data of client/user credit card usage (client_id, credit_card_number, balance_acquired)
Client_id     Credit_card_number     Balance_acquired
A     CC1     $20,000
A     CC2     $50,000
A     CC     $70,000
2. src_card_limits: contains client’s credit cards linked to credit limits.
Credit_card_number     Limit_id
CC1     L1
CC2     L2
CC     L
3. src_limit_structure: contains the relationship of limits and sub-limits.
Limit_id     Sub_Limit_id
L     L1
L     L2
I have designed two dimensions and one fact table. Dimensions are:
1. LIMITS: contains the limit_id.
2. CLIENTS: contains credit card user’s information.
And fact table is LIMIT_BALANCES_FACT, which have some fact columns with the above dimensions.
How can I implement the above scenario of limit hierarchy in data warehouse? Need your suggestions.
Thanks in advance

Much depends on how you want to analyze the data and there are a few options:
1) Use credit limit as an attribute of the customer dimension. This would allow you to create query filters that can just show those customers with a $100,000 credit limit. This would return a list of credit cards (since the attribute would be assigned to each credit card) and then you can simply add or just keep the parents of that result set.
However, this assumes you do not want to measure data specifically relating to credit card limit. For example it would not be possible to view a total amount spent by all customers who had a credit-limit of $100,000.
In this case the attribute, credit limit, is simply used to filter a result set
2) Create a separate dimension called Credit Limit and create three levels:
All
Range
Credit Limit
The level Range would contain groupings of credit limits such as 100-500, 501-1200, 1201-1,000 etc etc.
This would allow you to analyse your data by customer and by credit limit over time. Allowing you to slice and dice quickly and easily.
3) A second customer hierarchy could be added to the customer dimension. This would allow you to drill-down through different credit limits to customers to individual credit cards. It would be advisable to follow the same approach as option 2 and create some groupings for the credit limits to make the drill down easier for your business users to navigate:
All
Range
Credit Limit
Customer
Credit Card
Hope this helps
Keith Laker
Oracle EMEA Consulting
BI Blog: http://oraclebi.blogspot.com/
DM Blog: http://oracledmt.blogspot.com/
BI on Oracle: http://www.oracle.com/bi/
BI on OTN: http://www.oracle.com/technology/products/bi/
BI Samples: http://www.oracle.com/technology/products/bi/samples/

Implementing heirarichal structure in data warehouse

I want to create a data warehouse for credit card application. Each user can have a credit card and multiple supplementary credit cards. Each credit card has a main limit, which can be sub-divided into sub-limits to supplementary credit cards as requested by the user. Let us consider the following example:
User “A” has a credit card “CC” with Limit “L” and its limit is $100,000.
User “A” requested for a supplementary credit card “CC1” which is assigned limit
“L1” = $50,000. He requests for another supplementary credit card “CC2” which is assigned limit “L2” = $100,000.
Source tables contain data like this:
1. src_client_card_trans: contains transaction data of client/user credit card usage (client_id, credit_card_number, balance_acquired)
Client_id     Credit_card_number     Balance_acquired
A     CC1     $20,000
A     CC2     $50,000
A     CC     $70,000
2. src_card_limits: contains client’s credit cards linked to credit limits.
Credit_card_number     Limit_id
CC1     L1
CC2     L2
CC     L
3. src_limit_structure: contains the relationship of limits and sub-limits.
Limit_id     Sub_Limit_id
L     L1
L     L2
I have designed two dimensions and one fact table. Dimensions are:
1. LIMITS: contains the limit_id.
2. CLIENTS: contains credit card user’s information.
And fact table is LIMIT_BALANCES_FACT, which have some fact columns with the above dimensions.
How can I implement the above scenario of limit hierarchy in data warehouse? Need your suggestions.
Thanks in advance

Much depends on how you want to analyze the data and there are a few options:
1) Use credit limit as an attribute of the customer dimension. This would allow you to create query filters that can just show those customers with a $100,000 credit limit. This would return a list of credit cards (since the attribute would be assigned to each credit card) and then you can simply add or just keep the parents of that result set.
However, this assumes you do not want to measure data specifically relating to credit card limit. For example it would not be possible to view a total amount spent by all customers who had a credit-limit of $100,000.
In this case the attribute, credit limit, is simply used to filter a result set
2) Create a separate dimension called Credit Limit and create three levels:
All
Range
Credit Limit
The level Range would contain groupings of credit limits such as 100-500, 501-1200, 1201-1,000 etc etc.
This would allow you to analyse your data by customer and by credit limit over time. Allowing you to slice and dice quickly and easily.
3) A second customer hierarchy could be added to the customer dimension. This would allow you to drill-down through different credit limits to customers to individual credit cards. It would be advisable to follow the same approach as option 2 and create some groupings for the credit limits to make the drill down easier for your business users to navigate:
All
Range
Credit Limit
Customer
Credit Card
Hope this helps
Keith Laker
Oracle EMEA Consulting
BI Blog: http://oraclebi.blogspot.com/
DM Blog: http://oracledmt.blogspot.com/
BI on Oracle: http://www.oracle.com/bi/
BI on OTN: http://www.oracle.com/technology/products/bi/
BI Samples: http://www.oracle.com/technology/products/bi/samples/

Permanent Job Opportunity - Oracle BI Data Warehouse Developer Chicago, IL

Submit Resumes to [email protected]
The Business Intelligence Specialist will play a critical role in designing, developing, deploying, and supporting data warehouse/data mart applications. In this role, the person will be responsible for all BI aspects of a data warehouse/data mart application. Primary duties will be to create reporting standards, as well as coach and support power users with selected Oracle tool. The ideal candidate will have 3+ years demonstrated experience in data warehousing and Business Intelligence tools. Must also possess excellent communication skills and an outstanding track record with the user.
Principal Duties:
Participates with internal clients to define software requirements for development, maintenance and/or improvements
Maintains accuracy, integrity, and availability of the data warehouse
Tests, monitors, manages, and validates data warehouse activity, including data extraction, transformation, movement, loading, cleansing, and updating processes
Designs and optimizes data mart models for Oracle Business Intelligence Suite.
Translates the reporting requirements into data analysis and reporting solutions.
Reviews and sign off on project plan(s).
Reviews and sign off on technical design(s).
Defines and develops BI reports for accessing/analyzing data in warehouse.
Customizes BI tools and data sets for different types of users.
Designs and develop UAT (User Acceptance Testing).
Drives improvement of BI system architecture and development process.
Develops and maintains internal relationships. Actively champions teamwork. Uses internal resources to enhance knowledge and expertise of industry, research, products and services. Provides information and support to others in the company.
Required Skills:
Education and Experience:
BS/MS in Computer Science or equivalent.
3+ years of experience with Oracle, PL/SQL Development and Data Warehousing.
Experience Oracle Business Intelligence Suite and Crystal Reports is a plus.
2-3 years dimensional modeling experience.
Demonstrated hands on experience with Unix/Linux, SQL required.
Demonstrated hands on experience with Oracle reporting tools.
Demonstrated experience with translating business requirements into data analysis and reporting solutions.
Experience in training programs/teach users to use tools.
Expertise with software development process.
Effective mediator - able to facilitate constructive and productive discussions with internal customers, external clients, and development personnel pertaining to feature definition, project scope, and status
Problem solving*identifies and resolves problems in a timely manner, gathers and analyzes information skillfully and maintains confidentiality.
Planning/organizing*prioritizes and plans work activities and uses time efficiently. Work requires continual attention to detail in composing and proofing materials, establishing priorities and meeting deadlines. Must be able to work in a fast-paced environment with demonstrated ability to juggle multiple competing tasks and demands.
Quality control*demonstrates accuracy and thoroughness and monitors own work to ensure quality.
Adaptability*adapts to changes in the work environment, manages competing demands and is able to deal with frequent change, delays or unexpected events.
Benefits/Compensation:
Employees enjoy competitive compensation. We have a full benefits package including medical and dental insurance, long-term disability and life insurance and a 401(k) plan.
The client operates within the healthcare industry.
This is a permanent full-time position. After ensuring your availability and qualifications we will put you in direct contact with the client to move forward in the process.

FORWARD THE UPDATED RESUME AS SOON AS POSSIBLE.

Database and Data Warehouse, SAP BW Vs Oracle

Hello Gurus,
I would like to know the differences between Database and Data Warehouse.
Oracle acts as a Database for SAP BW. I understand it this way, that all the data is stored in Oracle and BW tell the Database how to store, with all the links etc.
Please tell me whether I am correct.
Its my pleasure to award points,
Thanks and best wishes,
i-bi

hi,
A data warehouse is, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favour efficient data analysis and reporting (especially OLAP). Data warehousing is not meant for current, "live" data.
A database is a collection of information stored in a computer in a systematic way, such that a computer program can consult it to answer questions. The software used to manage and query a database is known as a database management system (DBMS). The properties of database systems are studied in information science.
http://www.webopedia.com/TERM/D/data_warehouse.html
Hope this helps.
Regards,
yunus

Data Warehousing Design

Hello People,
Now,I am engaging in a new data warehousing project.It is the first time for me building a data warehouse, so I've encountered a lot of difficulties.I hope that I can get much help from the accommodating people here.
The source data of this data warehousing comes from a sms(cell phone short message)gateway's log files.The log files recorded every piece of the short messages,which were sent to a certain service-processing application from a user's cell phone(kind of MO message),or were sent to a user's cell phone from a service-processing application(kind of MT message).Every record of the log fils have many properties of the short message, including sending time,user's phone number,service-provider's code,charge value,the id of the short message and so on.
Now,we hope to build a data warehousing over these data. Our plan was devided into two parts,first is to build the data warehousing and realize some simple statistic function and OLAP function on the data warehousing, second is to do something with data mining technology.I am now facing the designing work,but I have something uncertain.
1.How to design the dimention?We difined three dimentions,time,district and service using the OWB tool.Every dimention has some levels and some levels form a hierarchy.In the OWB,every level need some attributes to be defined, I am uncertain which attributes are necessary or not,and uncertain the data type of
every attribute.
2.How many fact tables are necessary?According to our source data, two kinds of data are available,the MO messages and the MT messages,so I create two fact tables.I don't know whether it is appropriate.
3.What colomns should a fact table contain?Our first phase of our plan is to realize some statistic functions.One is counting the flux of short message within one hour,one day or a certain spell.One is counting the sum of the users, who had used certain service within a spell.One is counting the profits,for every record of the short message includes the charge value.So, I created the fact tables including every field of the log files.I know it is not good,but I don't know what the good method is.
4.How to plan the materialized views?I know the materialized view is the key part to improve query performance.
Above are the difficulties that I am encountering now.As you seen, I am definitely a tyro, I eagerly need help.I am longing for your email, thank you!

Hi there,
There is an active data warehousing communite at www.datawarehousing.com, where you can send an email to the dw-list.
Cheers
A. Elliott

What are the Disadvantages of Management Data Warehouse (data collection) ?

Hi All,
We are plan to implement Management Data Warehouse in production servers .
could you please explain the Disadvantages of Management Data Warehouse (data collection) .
Thanks in advance,
Tirumala

>We are plan to implement Management Data Warehouse in production servers
It appears you are referring to production server performance.
BOL: "You can install the management data warehouse on the same instance of SQL Server that runs the data collector. However, if server resources or performance is an issue on the server being monitored, you can install the management data warehouse
on a different computer."
Management Data Warehouse
Kalman Toth Database & OLAP Architect
SQL Server 2014 Database Design
New Book / Kindle: Beginner Database Design & SQL Programming Using Microsoft SQL Server 2014

Data warehouse design

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