This white paper illustrates how Microsoft SQL Server Integration Services (SSIS) can be used in conjunction with commodity hardware or virtual machines running in Microsoft Azure to build elastic, highly available, and highly scalable ETL solutions.
2018-06-27
2,592 reads
2018-06-21
781 reads
An efficient and reliable method for reading CSV files with variable columns.
2018-06-12
28,523 reads
I am presenting an MS Excel VBA project which automates the display of SQL Server data tables.
2018-05-17
3,171 reads
A tip to auto detect data types for flat file sources in SSIS. Never manually enter them ever again!
2018-05-14
11,565 reads
This article shows how DefaultBufferMaxRows and DefaultBufferSize properties can be used to improve dataflow task performance.
2018-03-22
36,827 reads
In this article, we discuss how schema swapping method can be used to keep tables online during the load process.
2019-08-02 (first published: 2017-12-05)
7,592 reads
Run multiple instances of your package in parallel so that each instance processes a portion of the total number of files
2019-08-23 (first published: 2017-11-07)
8,084 reads
It is possible for a field in a character-delimited text file to contain a list of further-delimited values instead of the customary single value. This article demonstrates how to load such a file into a staging table, then use a CROSS APPLY query to parse the list of values into a related table.
2019-06-07 (first published: 2017-10-30)
13,132 reads
SQL Server Integration Services (SSIS) is the integration and ETL (extract – transform – load) tool in the Microsoft Data Platform stack. SSIS is typically used in data warehousing scenarios, but can also be used in common data integration use cases or just to move data around. SSIS is used behind the scenes in the Maintenance Plans of SQL Server and in the Import/Export wizard.
2017-10-06
4,301 reads
By Steve Jones
Redgate is a for-profit company. We look to make money by building and selling...
I’ve uploaded the slides for my Techorama session Microsoft Fabric for Dummies and my...
If you've ever loaded a 2 GB CSV into pandas just to run a...
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We create the following table and then insert some records in it:
create table t1 ( id int primary key, category char(1) not null, product varchar(50) ); insert into t1 values (1, 'A', 'Product 1'), (2, 'A', 'Product 2'), (3, 'A', 'Product 3'), (4, 'B', 'Product 4'), (5, 'B', 'Product 5');What happens if we execute the following query in both Sql Server and PostgreSQL?
select id,
category,
string_agg(product, ';')
over (partition by category order by id
rows between unbounded preceding and unbounded following) as stragg
from t1; See possible answers