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Redshift has the oldest architecture, being the first Cloud DW in the group. Its architecture wasn’t designed to separate storage & compute. While it now has RA3 nodes which allow you to scale compute and only cache the data you need locally, all compute still operates together. You cannot separate and isolate different workloads over the same data, which puts it behind other decoupled storage/compute architectures. Redshift runs as an isolated tenant per customer, and unlike other cloud data warehouses, it is deployed in your VPC.
Databricks was built by the founders of Spark as an analytics platform to support machine learning use cases. It leverages the Spark framework to process data residing in a data lake and is supported on AWS, GCP and Azure. Databricks coined the marketing term “Lakehouse '' architecture to illustrate the unification of data lake and data warehouse use cases. Customers still manage Spark clusters that process data residing in a Delta lake. Conversion of data to Delta Lake format is required to leverage the functionality of Delta Lake. Databricks Sql is a relatively new addition to simplify access to data stored in a data lake.
Redshift is limited in scale because even with RA3, it cannot distribute different workloads across clusters. While it can scale to up to 10 clusters automatically to support query concurrency, it can only handle a maximum of 50 queued queries across all clusters by default.
Databricks allow for autoscaling of clusters based on utilization. Additionally, increasing concurrency associated with a sql endpoint can be accomplished through the addition of clusters. Query concurrency per cluster is maxed at 10. However, scaling with additional clusters for concurrency is possible. Databricks provides a choice of instance types.
Redshift does provide a result cache for accelerating repetitive query workloads and also has more tuning options than some others. But it does not deliver much faster compute performance than other cloud data warehouses in benchmarks. Sort keys can be used to optimize performance, but their contribution is limited. There is no support for indexes, and low-latency analytics at large data volumes is hard to achieve. Because Redshift decoupling of storage & compute is limited compared to other cloud data warehouses, it doesn’t support isolating workloads, which means performance can degrade under pressure and competition for resources.
Databricks is designed to leverage the Spark framework for processing large volumes of data. It leverages compressed Parquet files in a Delta Lake. To reduce the amount of data processed, it uses data pruning on partitions and Parquet file metadata. Databricks does not provide any indexes.
Redshift was originally designed to support traditional internal BI reporting and dashboard use cases for analysts. As such, it is typically used as a general-purpose Enterprise data warehouse. With deep integrations into the AWS ecosystem, it can also leverage AWS ML service, making it also useful for ML projects. However, given the coupling of storage & compute, and the difficulty in delivering low-latency analytics at scale, it is less suited for operational use cases and customer-facing use cases like Data Apps. The coupling of storage and compute, together with the need to predefine sort & dist keys for optimal performance, make it challenging to use for Ad-Hoc analytics.
Databricks is a mature Spark based platform proven for processing streaming data. It is widely used for Machine Learning use cases by data scientists through the use of integrated notebooks. From a low latency query perspective, while it offers features like Delta Cache, it does not provide specialized indexes that can deliver low latency queries.
