Autoscaling in Microsoft Azure
The ability to scale up and down without maintaining extra hardware is one of the best cloud computing features. In this article, Mahendran Purushothaman explains autoscaling in Microsoft Azure.
2021-07-14
The ability to scale up and down without maintaining extra hardware is one of the best cloud computing features. In this article, Mahendran Purushothaman explains autoscaling in Microsoft Azure.
2021-07-14
In this article we will explore how to get started with Azure Purview and then explore some of the features within Purview Studio.
2021-06-28
Azure Databricks has a number of Access Controls and Row Level Security options that we will explore in this article.
2021-06-18
In this article we explore schema evolution capabilities and limitations in Databricks with regular parquet format and explore schema evolution features and capabilities through delta format with inserts, appends, and overwrites.
2021-06-04
In this article we will look at how to create deep and shallow clones of source data when using Azure Databricks.
2021-05-26
In this article we cover how to get started with MLflow using Azure Databricks which manages the end-to-end machine learning lifecycle.
2021-05-07
2021-05-26 (first published: 2021-04-30)
8,891 reads
create a sql server polybase scale out group in azure for free
2021-06-23 (first published: 2021-04-12)
2,054 reads
In this article we look at how you can user the Kusto Query Language (KQL) for querying Azure SQL Database audit data.
2021-04-05
In this article we explore additional capabilities of Azure Synapse Spark and SQL Serverless External Tables.
2021-03-25
By Steve Jones
Redgate is a for-profit company. We look to make money by building and selling...
If you've ever loaded a 2 GB CSV into pandas just to run a...
By James Serra
What problem is Fabric Ontology trying to solve? For years, most data conversations have...
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Comments posted to this topic are about the item The New Software Team
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