![]() ![]() ![]() Redshift is great for data analysis, but we shouldn't use Redshift to power production-ready applications. Column-oriented Database Management Systems Redshift can scale outward almost infinitely which makes Redshift great for use cases where we need to query huge amounts of data in the realm of petabytes and beyond. Redshift can accommodate a variable number of machines in a cluster, thus making Redshift horizontally scalable, which is a key advantage. When data is disproportionally distributed across slices, this phenomenon is called skew (we'll touch on how to optimize against skew in a bit). ![]() Redshift performs best when slices have a close-to-equal distribution of data. Each slice is an individual partition containing a fraction of our dataset. Depending on the size of nodes in your cluster, each compute node might support anywhere between 2-32 slices. These nodes are managed by a leader node, which is responsible for managing data distribution and query execution amongst the other nodes.Įach compute node is actually split into multiple partitions themselves called slices. Nodes which perform computations are called compute nodes. ![]() Each of these machines working in parallel to save and retrieve data, which adds a ton of complexity to how we should work with data. Redshift ClusteringĮvery Redshift cluster is comprised of multiple machines, each of which only stores a fraction of our data. Thus, it's essential to understand the pros and cons of Redshift before making such a big architectural decision (this notion has already been articulated by people more intelligent than I am). Maintaining a Redshift cluster is a lot of work. When compared to traditional databases, data warehouses makes a ton of tradeoffs to optimize for the analysis of large amounts of data. Using Redshift effectively requires much more awareness of underlying database technologies than one would need to build a system which prioritizes ACID transactions. While Formula 1 machines might outperform your Honda by various metrics, the amount of upkeep and maintenance that goes into F1 racers would make it unsuitable for casual usage. If relational databases were Honda Civics, Redshift would be a Formula 1 race car. If you happen to be new to Redshift or data warehouses in general, you're in for a treat. While we save time on researching and comparing solutions, this might come at the cost of answering some vital questions, such as "why Redshift?," or "how do I even get started with this thing?" If you're looking for some easily-to-digest mediocre answers, you've come to the right place. Most of us are locked into Redshift by default by merely being AWS customers. namely, the knowledge gaps that come with defaulting to any de facto industry solution. While Redshift's rise to power has been deserved, the unanimous popularity of any service can cause problems. Regardless of whether you're in data science, data engineering, or analysis, it's only a matter of time before all of us work with the world's most popular data warehouse. It's nice to see good services flourish while clunky Hadoop-based stacks of yesterdecade suffer a long, painful death. Redshift is quickly taking its place as the world's most popular solution for dumping obscene amounts of data into storage. ![]()
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