The main goal of ETL software is to move data from disparate sources into a central data repository so analytics can be performed across a holistic and consistent collection of data. Commonly, this centralized data is stored in a data warehouse. The data in the data warehouse may be in the form of structured system of record data, or it may come in the form of unstructured or semi-structured big data. The data warehouses that store this aggregated mix of data are increasingly located in the cloud. Snowflake and AWS Redshift both provide data warehousing software that can manage these jobs.
What is Snowflake?
Snowflake is a fully managed SaaS (software as a service) that provides a single platform that can accommodate data warehouses, data lakes, and data application development. It automatically scales processing and storage to meet user needs, processes data in both batch and real- time workloads, and provides for the secure sharing and consumption of batch, real-time and shared data. Architecturally and programmatically, Snowflake uses SQL language and data structures. It works well in multi-cloud environments, offers an extremely user-friendly and robust SQL interface, and relieves staff from having to install, configure, or manage the underlying warehouse platform, including hardware and software.
SEE: Dremio vs Snowflake: Comparing two of the best ETL tools (TechRepublic)
What is AWS Redshift?
AWS Redshift is a cloud-based data warehouse software that is built on top of the AWS cloud computing platform. It’s ideal for companies that host a majority of their data and applications on the AWS cloud platform, since it integrates well with other AWS products and tools. AWS Redshift processes both structured and unstructured data, in real time and batch modes. It uses parallel processing to process very large data sets and has built-in automation and scaling, but it does require some IT intervention in its installation, configuration and management. In return, AWS Redshift gives IT flexibility in designing and optimizing the workloads that it wants to run.
Architecture in Snowflake vs. AWS Redshift
Snowflake separates storage from processing. It does this by storing data in a separate data repository, and independently sizing, scaling and executing processing elsewhere. AWS Redshift does not separate data from storage, so from a cost standpoint, it can be less expensive to use Snowflake because you are only charged for service when you actively process data. Since the processing and data functions are segregated, there is a way to see when you are processing data and when you are not. On the flip side, there can be some speed advantages from the AWS Redshift approach, which combines processing and data into a single, wholly integrated operation.
SEE: Databricks vs. Snowflake: ETL tool comparison (TechRepublic)
Automation vs. customization
Snowflake takes the pain out of having to manually implement and manage much of the data warehousing and query processing operation. While it does use a custom SQL query language, the language is still SQL, which most organizations have resident expertise in. Snowflake also completely manages data administration and automatically scales processing and storage for your jobs. This saves internal administration time and gives companies an easy way to execute a multitude of queries.
Like Snowflake, AWS Redshift has a great deal of automation and it uses SQL. But Redshift also offers companies choices for how they want to configure and manage data and processing. This can be useful at times when you have to manage high query loads, and must adjust for that. Data can be manually partitioned and distributed as needed, and security can be customized to meet your organization’s security and governance requirements. For organizations that prefer more direct control over data and processing and that are heavy AWS cloud users, AWS Redshift is a good choice.
Snowflake operates well in a multi-cloud environment, so if your organization operates in many different clouds and needs to bring all of this data together and query it, Snowflake is a great choice.
AWS Redshift is a data warehouse and query tool developed by AWS and is ideally suited for companies that host most of their data on AWS, and desire optimum functionality and interoperability within the AWS cloud. If your company is a heavy AWS cloud user, AWS Redshift is a nice fit.
SEE: Hiring Kit: Cloud Engineer (TechRepublic Premium)
With a simple point and click, Snowflake allows users to copy databases and then share read-only access with others. This is a quick and automated way to leverage data value. At the end of each data share, the user can de-provision the data. This secures the data in its original data structure and can also save on costs.
AWS Redshift is not as automated when it comes to data aggregation and sharing. With Redshift, users (likely IT) must use multiple ETL extracts of data from different sources to arrive at the final set of data that they want to place into a data warehouse that can be available to users.
Choosing Snowflake vs. AWS Redshift for data warehousing
Both Snowflake and AWS Redshift are proven data warehouse and processing softwares that can be deployed with ETL tools as part of the data transformation and transfer process. When evaluating these two data warehousing and processing packages, sites should consider whether they are primarily multi-cloud or single (AWS) cloud, and what the tradeoffs are between software that is highly automated (with fewer options for customization), and software that gives you more flexibility to customize it to your IT environment. From a cost standpoint, both Snowflake and AWS Redshift can be managed efficiently, so the choice really depends upon which software is the best platform for your organization.