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UNION (Azure Stream Analytics) - Stream Analytics Query
UNION (Azure Stream Analytics) - Stream Analytics Query
Combines the results of two or more queries into a single result set that includes all the rows that belong to all queries in the union.
The following are basic rules for combining the result sets of two queries by using UNION: The number and the order of the columns must be the same in all queries. The data types must be compatible. Streams must have the same partition and partition count
·docs.microsoft.com·
UNION (Azure Stream Analytics) - Stream Analytics Query
Distributed tables design guidance - Azure Synapse Analytics
Distributed tables design guidance - Azure Synapse Analytics
Recommendations for designing hash-distributed and round-robin distributed tables using dedicated SQL pool.
Has many unique values.
Does not have NULLs, or has only a few NULLs.
Is not a date column
Is not used in WHERE clauses.
Is used in JOIN, GROUP BY, DISTINCT, OVER, and HAVING clauses.
·docs.microsoft.com·
Distributed tables design guidance - Azure Synapse Analytics
Spark Streaming - Different Output modes explained - Spark by {Examples}
Spark Streaming - Different Output modes explained - Spark by {Examples}
This article describes usage and differences between complete, append and update output modes in Apache Spark Streaming. outputMode describes what data is written to a data sink (console, Kafka e.t.c) when there is new data available in streaming input (Kafka, Socket, e.t.c)
Use complete as output mode outputMode("complete") when you want to aggregate the data and output the entire results to sink every time.
It is similar to the complete with one exception; update output mode outputMode("update") just outputs the updated aggregated results every time to data sink when new data arrives.
Use append as output mode outputMode("append") when you want to output only new rows to the output sink.
·sparkbyexamples.com·
Spark Streaming - Different Output modes explained - Spark by {Examples}