The storage argument is not significant because it is only 7 bits 1 bit vs 1 byte. Int is only up to 2. For the FK of periodic transaction table, we should use int as it is almost always less than 2. Not only this is slower than native SQL data type I have not tested it, but I think it is potentially slower , but also it is more confusing, e.
We should just simply use Int. The second case is when the transaction ID is often used to join to other table. For example, consider Order table and Delivery table.
They are often joined on Order ID column. In this case it is worth considering making OrderId as the clustered index of the Order table. But if the users query the Order table by Order Date more often than joining Order table to Delivery table, then we should make Order Date as the clustered index. There are no ifs or buts in this case. For transaction and periodic transaction tables, we should index the IDs to the main reference tables, e.
These main reference tables are usually put in the first 5 columns. We should set these indices as nonclustered. Remember that if the table is partitioned, the first indexing key must be the partition key. A transaction table be it snapshot or not is mostly partitioned by date. So for Order table, the partitioning key should be OrderDate, and all indices must use OrderDate as their first column. Simply divide number of dates into months, quarters or years.
For example: — Partition 1 is Jan , partition 2 is Feb , and so on. Even when the table is partitioned, we still need to put the date column as the clustered index. This is because partitioning 1 contains 1 month worth of date or 2 months or 1 quarter or even 1 year. By specifying the date column as the clustered index, the table will be physically ordered by date column so the query can quickly locate a particular date. Like this: Like Loading Related Comments 3 3 Comments » Hi,.
I would like to know in which cases are recommended tu use a datawarehouse appliance solution like IBM Netezza or Oracle Exadata. I know that this kind of solutions are very expensive and used in a very large datawarehoses, but I would like to know if there are more criteria in order to assess if you need a solution like that or yo can go with commun databases, servers, etc..
Comment by Juan V. Linear means that the computing power of 20 nodes is 10x the computing power of 2 nodes. Each node has their own processor, memory and storage. That said, MPP is not truly linear straight line because there is inefficiencies caused by connectivity between nodes. In theory, the MPP has no limit on its capacity. Meaning that we can create an MPP with 20, nodes, creating a system with huge computing power. The limit on SMP is very obvious. Comment by Vincent Rainardi — 22 March am Reply.
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You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Blog at WordPress. Table names We need to adhere the convention on table names within that database, or probably within the company.
Partition or not Should we partition the table? Do we need to create a new table? Which column as a PK? Not Null A primary key must be not nullable. Primary Key or Unique Index? Data dictionary Yes you will need to document each column in the data dictionary, i. Business knowledge To be able to specify the correct columns on the table, we need domain knowledge.
Types of columns There are 2 types of columns in a reference table: key and attributes. Storing in 2 places It is the basic principle of a normalised database design that data should only be stored in one place. Order of columns Put the key column first, then FK columns, then measures, then current-level attributes, then lower level attributes, then flags, then system columns. Not Null Key columns should be made not null. Abbreviations Should we shorten the column name by abbreviating a few words?
Same as the source The best practice is to set the data type in the target database the same as the data type in the source. Date or DateTime An exception to this rule is datetime, i. Float or Decimal If the source data is Float then we need to use Float, because if we use decimal we will lose precision. The Logical Data Model is used to define the structure of data elements and to set relationships between them. The logical data model adds further information to the conceptual data model elements.
The advantage of using a Logical data model is to provide a foundation to form the base for the Physical model. However, the modeling structure remains generic.
At this Data Modeling level, no primary or secondary key is defined. At this Data modeling level, you need to verify and adjust the connector details that were set earlier for relationships.
A Physical Data Model describes a database-specific implementation of the data model. It offers database abstraction and helps generate the schema. This is because of the richness of meta-data offered by a Physical Data Model. The physical data model also helps in visualizing database structure by replicating database column keys, constraints, indexes, triggers, and other RDBMS features.
Characteristics of a physical data model: The physical data model describes data need for a single project or application though it maybe integrated with other physical data models based on project scope.
Data Model contains relationships between tables that which addresses cardinality and nullability of the relationships. Developed for a specific version of a DBMS, location, data storage or technology to be used in the project. Columns should have exact datatypes, lengths assigned and default values. When we use compare and merge to update the PDM from the LDM within a single dm1 file , the attributes and columns don't always match up. I know we can force-match them, but we've found that they match up if we make sure that the 'physical name' on an attribute is the same as the actual name of the column.
I don't remember if this is available in XE4, but once you force match these objects together there is an option to save the matches.
On page 5 of the compare wizard you should see a checkbox that looks like this:. If you check it and then click on the Once you do that the next time you want to run a compare and merge, those matches you've forced should always match up.
You can also modify the where used to match up the objects but this usually only works on models in the same dm1 file. Hopefully this answers some of your questions. There isn't an easy way to match up columns in different models if they don't have a "history" with each other ie, merged or generated from a model. You have to do the force matching manually but this option allows you to save those force matches so that at a later date you won't need to force match again.
Idera uses cookies to improve user experience. By using our community you consent to all cookies in accordance with our Cookie policy. After you create entities in the Logical model, you then click the down arrow on the top middle of the ERWin GUI to select Physical, and ERWin changes to the physical model and the logical entities you created in the local model are automatically in the physical model - unless you tagged the logical model entities to only be in the logical model.
Another difference may be that in the logical model a many-to-many relationship may not contain the node table but in the physical model the many-to-many relationship should contain the node table.
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