is it possible to get this output from select Query
I tried the below query
select monthly + savings as monthly savings from table
but the resultant data is under one column
is there any solution to get more than one column under same heading
There is no way to retrieve the information from SQL Server with merged headers, at least not with the most widely used clients.
SQL Server is a relational database and it's fundation is based on sets of data arranged in tables with columns, rows and relationships between them. Suppresing a header would mean breaking the column-value link. If you want to manipulate them, you will have to do so after retrieving the data from the database, maybe on your display layer or in a helper process between the database and your presentation, as Tim suggested.
Related
I am struggling in figuring out how to create a star schema from multiple source tables. I work at a trading firm so the data is related to user trading activity. The issue I am having is that our datasets do not have primary ids for every field that could be a dimension. Instead, we usually relate our data together using the combination of date and account number. Here is an example of 3 source tables...
I would like to turn this into a star schema, something that looks like ...
Is my only option to denormalize my source tables into one wide table (joining trades to position on account number and date, and joining the users table on account number), create keys for each dimension, then re normalizing it into the star schema? Are star schema's ever built from multiple source tables?
Star schemas are almost always created from multiple source tables.
The normal process is:
Populate your dimension tables
Create a temporary/virtual fact record using your source data
Using this fact record, look up the relevant dimension keys
Write the actual fact record to your target fact table
Data-warehousing is about query speed. The data-warehouse should not be concerned with data integrity. IT SHOULD NOT CLEAN OR CORRECT BAD DATA. It only needs to gather all the data together into a single record to present to the model for analysis. Denormalizing the data is how this is done.
In a star schema, dimensions do not know about each other and have no relationships with other dimensions. In a snowflake, dimensions are related to other dimensions. That is the primary difference between star and snowflake.
All the metadata options for events are rolled up into dimensions and used for slicing/filtering. All the measurable/calculation data for an event are in the event fact, along with a reference to the dimension(s) containing the relevant metadata. The Metadata/Dimension is reused across multiple fact records.
Based on the limited example you've provided, I'd suggest you research degenerate dimensions and junk dimensions. Your Trade and Position data may need to be turned into a fact and a dimension (degenerate), and some of your flag attributes may be best placed into a junk dimension.
You should also make sure your dimension keys are clear. You should not have multiple paths to a dimension (accountnumber: trade -> position -> user & trade -> user ) as that will cause inconsistent results when querying depending on which relationship you traverse.
I need to move data between two databases and wanted to see if SSIS would be a good tool. I've pieced together the following solution, but it is much more complex than I was hoping it would be - any insight on a better approach to tackling this problem would be greatly appreciated!
So what makes my situation unique; we have a large volume of data, so to keep the system performant we have split our customers into multiple database servers. These servers have databases with the same schema, but are each populated with unique data. Occasionally we have the need to move a customer's data from one server to another. Because of this, simple recreating the tables and moving the data in place won't work as in the database on server A there could be 20 records, but there could be 30 records in the same table for the database on server B. So when moving record 20 from A to B, it will need to be assigned ID 31. Getting past this wasn't difficult, but the trouble comes when needing to move the tables which have a foreign key reference to what is now record 31....
An example:
Here's a sample schema for a simple example:
There is a table to track manufacturers, and a table to track products which each reference a manufacturer.
Example of data in the source database:
To handle moving this data while maintaining relational integrity, I've taken the approach of gathering the manufacturer records, looping through them, and for each manufacturer moving the associated products. Here's a high level look at the Control Flow in SSDT:
The first Data Flow grabs the records from the source database and pulls them into a Recordset Destination:
The OLE DB Source pulls from the source databases manufacturer table while pulling all columns, and places it into a record set:
Back in the control flow, I then loop through the records in the Manufacturer recordset:
For each record in the manufacturer recordset I then execute a SQL task which determines what the next available auto-incrementing ID will be in the destination database, inserts the record, and then returns the results of a SELECT MAX(ManufacturerID) in the Execute SQL Task result set so that the newly created Manufacturer ID can be used when inserting the related products into the destination database:
The above works, however once you get more than a few layers deep of tables that reference one another, this is no longer very tenable. Is there a better way to do this?
You could always try this:
Populate you manufacturers table.
Get your products data (ensure you have a reference such as name etc. to manufacturer)
Use a lookup to get the ID where your name or whatever you choose matches.
Insert into database.
This will keep your FK constraints and not require you to do all that max key selection.
My system collects a lot of data from different resources (each resource has text-ID), and send it to client bounded together in predefined groups. there some hundreds of different resources, each might set record in period of second up some hours. there less then hundred "view groups".
The data collector is single-threaded.
what is the best method to organize the data?
a. make different table for each source, where the name of the table is based on the source id?
b. make single table and add the source id as text-field (key if possible)?
c. table for each predefined display group, with the source id as text-field?
each record has value (float) and date (date). the query will be something like select * from ... where date < d1 and date > d2. In case of single table, it will be "and sourceId in(...)"
database type is unknown yet, it might be lightsql, postgres, mysql, mssql ...
I have a Fact Table Service1 (open or latest data) from Mart1 and another Fact Table Service2 (historic data) from Mart2. These tables share few common measures and dimensions but the underlying dataset is mutually exclusive.
Now the business wants to merge these two facts into one table in Tabular model to do Year over Year comparison.
Is it possible to combine these two facts, if so, what should be the approach.
Alternatively, do we have to achieve this.
Things to note down are,
Records in Fact table Service2 will never change
The Dimension keys between Mart1 and Mart2 is not guaranteed to be same
Are these Data Marts different databases? If so, you can create a calculated table that brings the two tables together. To do this, in 2016, on the bottom of the designer, there is a little plus sign on the far right next to the last table tab defined. When you hover over it, it will say "Create new table from DAX formula". Create the DAX that selects from the first table union the second table.
If the marts are in the same database you can create a partition for each on the table properties. In order to do this you would create a tabular model, open a connection to data source and bring in the changing data. Then click on table, partitions, click New, then grab the archived data. You would have to make sure the column definitions are in line in order to do this.
As far the other issues that you describe, it sounds as though you are using the Data Marts as your warehouses. Do you have access to the data before it was transformed into the Data Mart where the surrogate keys were applied? I generally keep a Persisted Staging Area around for these cases. If you employ a Data Vault (http://learndatavault.com/) prior to your Data Mart creation, you could simply create a new Data Mart with both sets of data and all of the Dimension keys will be intact.
I am using SQL Server 2005 Express and Visual Studio 2008.
I have a database which has a table with 400 Columns. Things were (just about manageable) until I had to perform bi-directional sync between several databases.
I am wondering what arguments are for and against using 400 column database or 40 table database are?
The table in not normalised and comprises of mainly nvarchar(64) columns and some TEXT columns. (there are no datatypes as it was converted from text files).
There is one other table that links to this table and is a 1-1 relationship (i.e one entry relates to one entry in the 400 column table).
The table is a list files that contained parameters that are "plugged" into a application.
I look forward to your replies.
Thank you
Based on your process description I would start with something like this. The model is simplified, does not capture history, etc -- but, it is a good starting point. Note: parameter = property.
- Setup is a collection of properties. One setup can have many properties, one property belongs to one setup only.
- Machine can have many setups, one setup belongs to one machine only.
- Property is of a specific type (temperature, run time, spindle speed), there can be many properties of a certain type.
- Measurement and trait are types of properties. Measurement is a numeric property, like speed. Trait is a descriptive property, like color or some text.
For having a wide table:
Quick to report on as it's presumably denormalized and so no joins are needed.
Easy to understand for end-consumers as they don't need to hold a data model in their heads.
Against having a wide table:
Probably need to have multiple composite indexes to get good query performance
More difficult to maintain data consistency i.e. need to update multiple rows when data changes if that data is on multiple rows
As you're having to update multiple rows and maintain multiple indexes, concurrent performance for updates may become an issue as locks escalate.
You might end up with records with loads of nulls in columns if the attribute isn't relevant to the entity on that row which can make handling results awkward.
If lazy developers do a SELECT * from the table you end up dragging loads of data across the network, so you generally have to maintain suitable subset views.
So it all really depends on what you're doing. If the main purpose of the table is OLAP reporting and updates are infrequent and affect few rows then perhaps a wide, denormalized table is the right thing to have. In an OLTP environment then it's probably not and you should prefer narrower tables. (I generally design in 3NF and then denormalize for query performance as I go along.)
You could always take the approach of normalizing and providing a wide-view for readers if that's what they want to see.
Without knowing more about the situation it's not really possible to say more about the pros and cons in your particular circumstance.
Edit:
Given what you've said in your comments, have you considered just having a long & skinny name=value pair table so you'd just have UserId, PropertyName, PropertyValue columns? You might want to add in some other meta-attributes into it too; timestamp, version, or whatever. SQL Server is quite efficient at handling these sorts of tables so don't discount a simple solution like this out-of-hand.