It's a tradeoff. Analytics databases are often filled with periodic dumps of transactional databases and this feels so dirty that it's easy to accidentally forget that it isn't just a hack, it's actually a structural workaround for the poor random-write performance of analytics DBs:
OLTP = more read amplification on analytics workflows, less write amplification of random insert
OLAP = less read amplification on analytics workflows, more write amplification of random insert
If that's too theoretical, the other day I saw 1-row updates of about 10kb data lead to 1GB of writes in Redshift: 1MB block size times 300 columns times a log+shuffle factor of about 3. That's a write amplification factor of 100000. Crazy stuff.
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Well yeah that's the sales pitch :)
It's a tradeoff. Analytics databases are often filled with periodic dumps of transactional databases and this feels so dirty that it's easy to accidentally forget that it isn't just a hack, it's actually a structural workaround for the poor random-write performance of analytics DBs:
OLTP = more read amplification on analytics workflows, less write amplification of random insert
OLAP = less read amplification on analytics workflows, more write amplification of random insert
If that's too theoretical, the other day I saw 1-row updates of about 10kb data lead to 1GB of writes in Redshift: 1MB block size times 300 columns times a log+shuffle factor of about 3. That's a write amplification factor of 100000. Crazy stuff.