From the course: Amazon Web Services: Data Analytics
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Query Redshift Spectrum - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services: Data Analytics
Query Redshift Spectrum
- [Instructor] So, a new service that we're going to take a minute to discuss is something called Redshift Spectrum. This is interesting in it's implementation. In the previous movie, we looked at working with a very large table in our fact table, our load orders. We had millions of rows in it. In traditional data warehousing, in order to make tables of that size more manageable, they were commonly partitioned or split into smaller tables which then would have to be rejoined. Not only is this inefficient, it also adds to the management overhead. So, what Redshift Spectrum does is it allows for a new kind of partitioning strategy. Rather than creating separate tables and keeping the size of the Redshift Cluster so large, you can now move the archive tables, or the archive sections of the table, into the much cheaper, much easier to use S3 blob storage. So, you can see conceptually you have N number of Redshift clusters, or you have what's called your warm or your hot data warehouse…
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Contents
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Setup for AWS analytics7m 29s
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(Locked)
Query Athena using SQL query on S35m 44s
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(Locked)
Query DynamoDB for NoSQL7m 19s
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(Locked)
Set up Kinesis for input streams9m 17s
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(Locked)
Query Kinesis Analytics8m 38s
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(Locked)
Query CloudSearch and Elasticsearch5m 17s
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(Locked)
Query AWS IoT7m 27s
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(Locked)
Set up EMR, RDS, and Redshift3m 49s
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(Locked)
Query RDS with ANSI SQL3m 38s
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(Locked)
Query Redshift for RDBMS8m 36s
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(Locked)
Query Redshift Spectrum2m 25s
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(Locked)
Query EMR with Apache Spark5m 5s
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