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What is a delta table in SAP HANA databases?
From initial googling, I understood that these tables are some kind of intermediate tables to help data go from one state to another. But when exactly are they used? How are they different from "normal" tables in HANA databases?
Delta tables are a SAP-HANA specific technique to speed up write operations in the database.
Tables in SAP HANA usually use the column store, which is read-optimized. When writing data to a column-store table, this data is first stored in the delta-space for that table; this delta-space is periodically merged into the column store.
See e.g. https://cookbook.experiencesaphana.com/bw/operating-bw-on-hana/hana-database-administration/system-configuration/delta-merge/column-store/ for more details.
"Delta" is commonly used to mean "difference". A delta table would show only the differences between two tables, the records that were added/deleted/changed during the new process. It's a way to test new code to see what changes it caused.
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I merged two databases for analyses purposes. One of these databases is out of production, so the data is not changing. The other db is live and continuously growing.
What's the best practice in terms of data management/storage? Do I have to delete all rows and reload + union data from both databases or is there a better way to manage this?
Thanks in advance
Sam
If you know the SSIS then make a package which will check keys and based on that it will inserted only unique rows.
You can easily apply lookup via ssis in source and destination.
Let me know if you need any help
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I am trying to determine which is faster in a SSIS package. I have multiple (100's) csv files that have about 150 columns of scientific precision data (38,20) for different types of scenarios. The table is layed out the same way with some other columns. The data sometimes comes in with whitespace/empty data and I have to change this to null for a data conversion at a later time.
I am trying to determine if I should go with derived columns (150) to turn the data into nulls or use a massive sql update statement for updating each column.
Thoughts? Thank you.
Generally speaking, any time you have a choice of doing a transformation in either the SSIS dataflow, or in TSQL, the TSQL way is usually faster.
However, that's only generally speaking, and the only way to know if it holds true in any specific case is to test it.
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My question is that I want to learn nodejs/express, and make a super simple web project. It would be a database with tables : users, video_games, categories.
The web site will just show list of games (just an example).
In this typical case, what would be more efficient : Mysql or MongoDB (SQL or NoSql) ?
In this particular case were you want to show only list ( you don't want to actual store videos, doc, texy, etc..) SQL database will be a good choice.
Another reason to use SQL database is that your data is relational ( I am assuming that the data i.e video_games, category...etc are linked to users) were SQL database suits more.
You should go to nosql database only when there is to relationship between your data ( well this is not the only case, but for beginners were your aim is to simply pick the right database this suffice)
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I have to optimize a bunch of many table and data included SQL queries, and I would like to ask some questions:
1) There are situations, when the same functions are needed in different queries. Which results lower processor time: if the functions are calculated separately in every queries or if it is calculated in one query, and they are linked through each other?
2) When creating queries, which is better in giving table relationships? If I have 1 main query, and all of the other queries are related to that, or if there is a serial connection between all the tables?
The 2 main tables are relatively big (~30MB) Excel-tables.
Thank you in advance.
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A version control system compares the scripted definition of a table to the checked in state. So I guess many cvs will see column reordering of a table as a change.
Since tsql does not support to add a new column in the middle of a table and because in a relational DB the ordering should not matter, what are good practices for version control of table definitions if the column-order could change.
Sometimes you could need to redo a drop column in the middle of a table.
You should be storing scripts to setup your database in source control, not trying to have something reverse-engineer those scripts from the state of the database. Column-order then becomes a non-issue.
Specifically, I've seen two schemes that work well. In the first, each database schema update script is given a sequential number, and the database tracks which sequence number is the last applied. In the second, each database schema update script is given a UUID, and the database tracks all UUIDs that have been applied.
I would checkout the book Refactoring Databases for more details and examples of how to manage database changes.