The question is very generic, but I'll try to outline a few different scenarios, as there are many parameters in play here. One of them is cost, which on the cloud it can quickly build up. Of course, the size of data is also important.
These are a few things you should consider:
- batch vs streaming: do the updates flow continuously, or the process is run on demand/periodically (sounds the latter rather than the former)
- what's the latency required ? That is, what's the maximum time that it would take an update to propagate through the system ? Answer to this question influences question 1)
- how much data are we talking about ? If you're up the Gbyte size, Tbyte or Pbyte ? Different tools have different 'maximum altitude'
- and what format ? Do you have text files, or are you pulling from relational DBs ?
- Cleaning and deduping can be tricky in plain SQL. What language/tools are you planning on using to do that part ? Depending on question 3), data size, deduping usually requires a join by ID, which is done in constant time in a key value store, but requires a sort (generally O(nlogn)) in most other data systems (spark, hadoop, etc)
So, while you ponder all this questions, if you're not sure, I'd recommend you start your cloud work with an elastic solution, that is, pay as you go vs setting up entire clusters on the cloud, which could quickly become expensive.
One cloud solution that you could quickly fire up is amazon athena (https://aws.amazon.com/athena/). You can dump your data in S3, where it's read by Athena, and you just pay per query, so you don't pay when you're not using it. It is based on Apache Presto, so you could write the whole system using basically SQL.
Otherwise you could use Elastic Mapreduce with Hive (http://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive.html). Or Spark (http://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark.html). It depends on what language/technology you're most comfortable with. Also, there are similar products from Google (BigData, etc) and Microsoft (Azure).