The company wishes to store the data of its current customers in the database, where each customer has a unique customer ID, name, address, country and country international telephone code. The company also wishes to have a list of future customers, so the company can send them
information about recent special offers. These future customers are nominated by the company employees and can be identified by their emails or telephone numbers. Once a future customer rents his first car, the customer record should be removed from future customer list and added into the list current customer.
should I create two entity for current customer and future customer? or one is enough?
It is never a good idea to move data around, because it is inefficient, slow and unnecessary.
Instead, represent an attribute as a column and update the column.
Keep just 1 table for Customers including future customers (prospects). Identify whether a customer is an actual customer or a prospect, using a column : Cust_type ('A' or 'P' for example).
Note that a prospect may become a customer and the reverse may also be true (if a prospect places a purchase order and then cancels it, for instance.) You have to keep this status updated.
Keep a separate table for all Customer Orders.
Related
I'm creating a Motorcycle store system, i wondering if i need to create one table that contains orders, the sales orders and the purchases orders with a column that will be called OrderType that determine if its purchase order or sale order
the same with Payments, money that i pay to supplier and money that customer pay to me, should be in table that called payment and column that determine if its outgoing payment or income
is that ok? or i need to create other tables
I would consider against it... Purchases are from a vendor YOU get the products from to build/fix/manufacture something.
Sales orders would have a customer you are selling to and thus would be foreign keys to different tables... unless your customer table has your vendors too and that has some column to identify difference as Vendor vs Customer.
Additionally, as you expand your design development and queries, purchasing history, etc., it may be more beneficial to have them separate.
You can create a single table. Whether this is good design or unfortunate design depends on how you use the data. How many times do you ever want to query these two datasets as if they were one dataset? How many times to you query them separately?
I am using MS-Access database.
I am trying to make relationship with two tables, Old Customer table having data and Newly added coupon table.
As my client want to introduce new concept of coupon, where customer come with coupon instead of giving cash.
I have inserted Coupon code in coupon table in bulk.
Now, I am confused about what kind of relationship I should create with these two tables ?
I have to consider the below things...
customer can give either cash or coupon.
IF customer show the coupon, there will be an entry in CouponID column
as well in cash column (to know the value of that coupon.)
The CouponID should be unique in the customer table, Coupon Code should
not be repeated.
I am confused whether it should be one-To-One or One-To-Many ?
This image will help you to understand the problem.
I would not include "CouponID" in the customer table at all (nor "Cash" for that matter). The customer table models a customer, the coupon table models a coupon.
You need another table to model the transaction:
[CustomerTransaction]
id
date
customer_id
coupon_id
etc...
Every type of independent "thing" should be modeled by a discrete table. and "things" should be related to each other by other tables that create the 1:N relationship.
The relationship of customer to coupon is an optional (ie nullable) one-to-one; your data model looks good.
Some other comments:
The table would be better named sale rather than customer, since if the same customer comes back again, there will be a new row (but with the same name)
You could create a unique index on couponID that ignores nulls
You could rename Cash to Amount; the amount is either "cash" or coupon - the couponID column tells you the type of the amount
To create a unique index that ignores nulls:
CREATE UNIQUE INDEX idx1 ON customer (couponID) WITH IGNORE NULL;
I'm currently trying to redesign a Point of Sale database to make it more normalized, which will help tremendously with managing the data, etc. I'm a little bit unsure about the best design practices, based on the data I have to deal with. First of all there are basically two sets of measures, which share common keys. There is inventory data, units and dollars, and then point of sale data, units and dollars. Each of these is a customer, store, item and date level.
What I've done (mostly in theory at this point) is to create separates table for
Item level information
Item_ID,
Customer_ID
itemnumber
(and a few other item specific information).
Stores
Store_ID,
Customer_ID,
Store Number,
(and essentially address information)
Customer
Customer_ID,
Customer Number
(other customer specific information like name).
So in addition to those "support" tables, I have the
Main Inventory Data
Store_ID
Item_ID
I also have POS Data table, with the exact same ID's.
Basically my questions are:
should I include the Customer ID in the Pos Data and Inventory Data tables, even though they are a part of both the stores and items tables?
My second question would be, if I do add the customer ID, if I would join all of these tables together,
would I join the customer ID from all of the tables (Pos Data, Stores and Items OR Inventory Data, Stores and Items) to the customers table or
would just joining from the Pos Data table be sufficient.
Let me give a few additional details, regarding the data. As an example, we have two Customers, CustomerA and CustomerB. CustomerA has several stores whose store numbers are 1000,1025, 1036 and 1037. CustomerB also has several stores, whose store numbers are 1025, 1030, and 1037. Store numbers 1025 and 1037 happen to be the same between customers, but the stores themselves are unique and completely different.
CustomerA's Store Number 1000 sells three of our items (this is a wholesale perspective), which are Items ABC, DEF and EFG. CustomerA's Store Number 1025 also sells three of our items, which are ABC, HIJ and XYZ.
Each of these items contains two import pieces of data, in regards to its relationship to its specific customer and store number, Point of Sale data and Inventory Data. Point of Sale data would be in the form of PosUnits, which would be the quantity of an item that were sold, and PosDollars, which would be the total Dollars of the item that were sold in that store (essentially the number of units times the price it was sold for). The Inventory Data would be in InventoryUnits, which is the quantity of an item that is in stock at a store. [one thing to note, I separated inventory and pos data into separate tables, because we don't always receive both pieces of data from every customer. Also inventory and POS data are generally analyzed separately].
So, back to my example, CustomerA's Store Number 1000, item ABC may have sold 100 units, which is $1245.00. CustomerA's Store Number 1025, may have sold only 10 units of the same item for $124.50.
Now if we go back to CustomerB, it just so happens this Customer also has an item named ABC that it sells in many of their stores. CustomerA's item ABC is a completely different product from CustomerB's item ABC. It's purely coincidental that they named them the same thing.
Let me add this last point of clarification, which I probably should have stated earlier. My perspective is as a wholesaler. When I say item, I'm speaking of the customers item number, not the wholesalers item number. There is a cross reference involved in getting to the wholesalers item and the customer may have more than one of their item number the reference the same wholesaler item number. I don't think it' necessary to delve into that, though.
Question #1: As part of the normalization rules, you should avoid to include redundant data in any table unless there are performance issues that require de-normalization. there are thousands of articles that will explain why avoiding redundancy.
As for Question #2: in the rules are only pick the columns that you need in your queries, if you need the Customer_ID pick it from where is cheaper for the database
Allow me to raise one more question
why do you have repeated Customer_ID in Stores and Item_level when you can join them thought the Main Inventory Data. this is another redundancy.
I'm creating a small pet shop database for a project
The database needs to have a list of products by supplier that can be grouped by pet type or product category.
Each in store sale and customer order can have multiple products per order and an employee attached to them the customer order must be have a customer and employee must have a position,
http://imgur.com/2Mi7EIU
Here are some random thoughts
I often separate addresses from the thing that has an address. You could make 1-many relationships between Employee, Customer and Supplier to an address table. That would allow you to have different types of addresses per entity, and to change addresses without touching the original table.
If it is possible for prices to change for an item, you would need to account for that somehow. Ideas there are create a pricing table, or to capture the price on the sales item table.
I don't like the way you handle the sales item table. the different foreign keys based on the type of the transaction is not quite correct. An alternative would be to replace SalesItem SaleID and OrderId with the SalesRecordId... another better option would be to just merge the fields from InStoreSale, SalesRecord, and CustomerOrders into a single table and slap an indicator on the table to indicate which type of transaction it was.
You would probably try to be consistent with plurality on your tables. For example, CustomerOrders vs. CustomerOrder.
Putting PositionPay on the EmployeePosition table seems off to... Employees in the same position typically can have different pay.
Is the PetType structured with enough complexity? Can't you have items that apply to more than one pet type? For example, a fishtank can be used for fish or lizards? If so, you will need a many-to-many join table there.
Hope this helps!
Preface:
I was thinking the other day about a new database structure for a new application and realized that we needed a way to store historical data in an efficient way. I was wanting someone else to take a look and see if there are any problems with this structure. I realize that this method of storing data may very well have been invented before (I am almost certain it has) but I have no idea if it has a name and some google searches that I tried didn't yield anything.
Problem:
Lets say you have a table for orders, and orders are related to a customer table for the customer that placed the order. In a normal database structure you might expect something like this:
orders
------
orderID
customerID
customers
---------
customerID
address
address2
city
state
zip
Pretty straightforward, orderID has a foreign key of customerID which is the primary key of the customer table. But if we were to go and run a report over the order table, we are going to join the customers table to the orders table, which will bring back the current record for that customer ID. What if when the order was placed, the customers address was different and it has been subsequently changed. Now our order no longer reflects the history of that customers address, at the time the order was placed. Basically, by changing the customer record, we just changed all history for that customer.
Now there are several ways around this, one of which would be to copy the record when an order was created. What I have come up with though is what I think would be an easier way to do this that is perhaps a little more elegant, and has the added bonus of logging anytime a change is made.
What if I did a structure like this instead:
orders
------
orderID
customerID
customerHistoryID
customers
---------
customerID
customerHistoryID
customerHistory
--------
customerHistoryID
customerID
address
address2
city
state
zip
updatedBy
updatedOn
please forgive the formatting, but I think you can see the idea. Basically, the idea is that anytime a customer is changed, insert or update, the customerHistoryID is incremented and the customers table is updated with the latest customerHistoryID. The order table now not only points to the customerID (which allows you to see all revisions of the customer record), but also to the customerHistoryID, which points to a specific revision of the record. Now the order reflects the state of data at the time the order was created.
By adding an updatedby and updatedon column to the customerHistory table, you can also see an "audit log" of the data, so you could see who made the changes and when.
One potential downside could be deletes, but I am not really worried about that for this need as nothing should ever be deleted. But even still, the same effect could be achieved by using an activeFlag or something like it depending on the domain of the data.
My thought is that all tables would use this structure. Anytime historical data is being retrieved, it would be joined against the history table using the customerHistoryID to show the state of data for that particular order.
Retrieving a list of customers is easy, it just takes a join to the customer table on the customerHistoryID.
Can anyone see any problems with this approach, either from a design standpoint, or performance reasons why this is bad. Remember, no matter what I do I need to make sure that the historical data is preserved so that subsequent updates to records do not change history. Is there a better way? Is this a known idea that has a name, or any documentation on it?
Thanks for any help.
Update:
This is a very simple example of what I am really going to have. My real application will have "orders" with several foreign keys to other tables. Origin/destination location information, customer information, facility information, user information, etc. It has been suggested a couple of times that I could copy the information into the order record at that point, and I have seen it done this way many times, but this would result in a record with hundreds of columns, which really isn't feasible in this case.
When I've encountered such problems one alternative is to make the order the history table. Its functions the same but its a little easier to follow
orders
------
orderID
customerID
address
City
state
zip
customers
---------
customerID
address
City
state
zip
EDIT: if the number of columns gets to high for your liking you can separate it out however you like.
If you do go with the other option and using history tables you should consider using bitemporal data since you may have to deal with the possibility that historical data needs to be corrected. For example Customer Changed his current address From A to B but you also have to correct address on an existing order that is currently be fulfilled.
Also if you are using MS SQL Server you might want to consider using indexed views. That will allow you to trade a small incremental insert/update perf decrease for a large select perf increase. If you're not using MS SQL server you can replicate this using triggers and tables.
When you are designing your data structures, be very carful to store the correct relationships, not something that is similar to the correct relationships. If the address for an order needs to be maintained, then that is because the address is part of the order, not the customer. Also, unit prices are part of the order, not the product, etc.
Try an arrangement like this:
Customer
--------
CustomerId (PK)
Name
AddressId (FK)
PhoneNumber
Email
Order
-----
OrderId (PK)
CustomerId (FK)
ShippingAddressId (FK)
BillingAddressId (FK)
TotalAmount
Address
-------
AddressId (PK)
AddressLine1
AddressLine2
City
Region
Country
PostalCode
OrderLineItem
-------------
OrderId (PK) (FK)
OrderItemSequence (PK)
ProductId (FK)
UnitPrice
Quantity
Product
-------
ProductId (PK)
Price
etc.
If you truly need to store history for something, like tracking changes to an order over time, then you should do that with a log or audit table, not with your transaction tables.
Normally orders simply store the information as it is at the time of the order. This is especially true of things like part numbers, part names and prices as well as customer address and name. Then you don;t have to join to 5 or six tables to get teh information that can be stored in one. This is not denormalization as you actually need to have the innformation as it existed at the time of the order. I think is is less likely that having this information in the order and order detail (stores the individual items ordered) tables is less risky in terms of accidental change to the data as well.
Your order table would not have hundreds of columns. You would have an order table and an order detail table due to one to many relationships. Order table would include order no. customer id 9so you can search for everything this customer has ever ordered even if the name changed), customer name, customer address (note you don't need city state zip etc, put the address in one field), order date and possibly a few other fields that relate directly to the order at a top level. Then you have an order detail table that has order number, detail_id, part number, part description (this can be a consolidation of a bunch of fields like size, color etc. or you can separate out the most common), No of items, unit type, price per unit, taxes, total price, ship date, status. You put one entry in for each item ordered.
If you are genuinely interested in such problems, I can only suggest you take a serious look at "Temporal Data and the Relational Model".
Warning1 : there is no SQL in there and almost anything you think you know about the relational model will be claimed a falsehood. With good reason.
Warning2 : you are expected to think, and think hard.
Warning3 : the book is about what the solution for this particular family of problems ought to look like, but as the introduction says, it is not about any technology available today.
That said, the book is genuine enlightenment. At the very least, it helps to make it clear that the solution for such problems will not be found in SQl as it stands today, or in ORMs as those stand today, for that matter.
What you want is called a datawarehouse. Since datawarehouses are OLAP and not OLTP, it is recommended to have as many columns as you need in order to achieve your goals. In your case the orders table in the datawarehouse will have 11 fields as having a 'snapshot' of orders as they come, regardless of users accounts updates.
Wiley -The Data Warehouse Toolkit, Second Edition
It's a good start.
Our payroll system uses effective dates in many tables. The ADDRESSES table is keyed on EMPLID and EFFDT. This allows us to track every time an employee's address changes. You could use the same logic to track historical addresses for customers. Your queries would simply need to include a clause that compares the order date to the customer address date that was in effect at the time of the order. For example
select o.orderID, c.customerID, c.address, c.city, c.state, c.zip
from orders o, customers c
where c.customerID = o.customerID
and c.effdt = (
select max(c1.effdt) from customers c1
where c1.customerID = c.customerID and c1.effdt <= o.orderdt
)
The objective is to select the most recent row in customers having an effective date that is on or before the date of the order. This same strategy could be used to keep historical information on product prices.
I myself like to keep it simple. I would use two tables: a customer table and a customer history table. If you have the key (e.g. CustomerID) in the history table there is no reason to make a joining table, a select on that key will give you all records.
You also don't have audit information (e.g. date modified, who modified etc) in the history table as you show it, I expect you want this.
So mine would look something like this:
CustomerTable (this contains current customer information)
CustomerID (distinct non null)
...all customer information fields
CustomerHistoryTable
CustomerID (not distinct non null)
...all customer information fields
DateOfChange
WhoChanged
The DateOfChange field is the date the customer table was changed (from the values in this record) to the values in a more recent record of the values in the CustomerTable.
You orders table just needs a CustomerID if you need to find the customer information at the time of the order it is a simple select.