Amazon Web Services (AWS), established in 2006, is focused on providing essential infrastructure services to businesses globally in the form of cloud computing. The key advantage offered through cloud computing, particularly via AWS, is its capacity to shift fixed infrastructure expenses into flexible costs. Businesses have been able to forgo extensive planning and procurement of servers and other Information Technology (IT) resources, owing to AWS. AWS seeks to provide businesses with prompt and cost-effective access to resources using Amazon's expertise and economies of scale, as and when their business requires. Currently, AWS offers a robust, scalable, economic infrastructure platform on the cloud powering an extensive array of businesses worldwide. It operates across numerous industries with data center locations in various parts of the globe including U.S., Europe, Singapore, and Japan.
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1. It is a part of the AWS ecosystem. So in order to involve other AWS products, you don't have to go through APIs, oAuth credentials, or anything of the sort. The integration is much more streamlined. For example, depending on how your databases are setup, you can reference data outputs from S3 or Lambda logs easily. 2. The cost is much lower when compared to competitors and the tooling Redshift provides allows you to predict cost much easier than its competitors. 3. Since it's an analytical database, using Redshift as the source for dashboards allows for faster loading of data and running of queries. Since we do the majority of our transformations in dbt, a lot of our queries are synthesized into tables which means that we use Redshift for semantic layer transformations which is what most users are frustrated with when they refer to long loading times for dashboards. Using Redshift makes this not as much of an issue.
ease of connection
Three areas that I have appreciated in Amazon Redshift are: 1. It is easy to query data directly, saving considerable time and effort in having to search and clean up desired datasets. 2. There has been easy integration of Redshift into our business intelligence tools, streamlining the process of creating visualizations and other 3. The data warehouse updates quickly based on the systems it is linked to, thereby making it easy to fulfill data requests in a shorter timeframe.
1. Creating and updating incremental tables can be very difficult due to Redshift's table lockout mechanism. 2. Navigating the UI (in terms of schemas, columns, CRUD functions) can feel outdated and frustrating as it is not intuitive. 3. Permissions and access can be a bit unwieldy as it's not intuitive.
rbac across late binding views adding sort keys to large existing tables recreating large tables to update dist keys
Some areas of improvement I have noticed in Amazon Redshift have been: 1. For some reason, attempting to process individual statements can be slower than processing statements for larger batches of data. 2. Since there is no enforcement of unique keys, there is a risk of duplicate data accidentally being inserted and therefore requiring extra cleanup time. 3. Concurrent logins into Redshift can sometimes cause queries to get backed up, slowing down the process.