Snowflake vs AWS Redshift: Data Warehousing Software Comparison

Data warehousing tools gather data into a central repository for use by business units in business intelligence software. Both Snowflake and AWS Redshift are top data warehousing software options that would work for companies with different data collection policies.

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The main purpose of ETL software is to move data from disparate sources into a central data repository so that analyzes can be performed on a holistic and consistent data collection. Typically, this centralized data is stored in a data warehouse. Data in the data warehouse can be in the form of a structured system of record data, or 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. Both Snowflake and AWS Redshift provide data warehousing software that can handle these tasks.

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What is the 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 batch and real-time workloads, and provides 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 frees 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 cloud-based data warehouse software built on the AWS cloud computing platform. It is ideal for businesses that host the majority of their data and applications on the AWS cloud platform, as it integrates well with other AWS products and tools. AWS Redshift processes both structured and unstructured data, in real time and in batch mode. It uses parallel processing to process very large data sets and has built-in automation and scaling, but it requires IT to install, configure, and manage. In return, AWS Redshift gives IT flexibility in designing and optimizing the workloads they want 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 sizing, scaling, and performing processing independently elsewhere. AWS Redshift does not separate data from storage, so from a cost perspective it may be cheaper to use Snowflake because you are only charged for the service when you are actively processing data. Since processing and data functions are separated, there is a way to see when you are processing data and when you are not. On the other hand, the AWS Redshift approach can have some advantages in terms of speed, which combines processing and data into a single, fully integrated operation.

SEE: Databricks vs Snowflake: ETL tools comparison (TechRepublic)

Automation vs Personalization

Snowflake avoids having to manually implement and manage much of the data warehousing and query processing operations. Although it uses a custom SQL query language, the language is still SQL, in which most organizations have resident expertise. Snowflake also fully handles data administration and automatically scales processing and storage for your jobs. This saves internal administration time and gives businesses a simple way to run a multitude of queries.

Like Snowflake, AWS Redshift has a lot of automation and uses SQL. But Redshift also gives enterprises choices about how they want to configure and manage data and processing. This can be useful when you have to handle high query loads and need to adapt to them. 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 are heavy users of the AWS Cloud, AWS Redshift is a good choice.

Cloud interoperability

Snowflake works well in a multi-cloud environment, so if your organization operates in many different clouds and needs to bring all that data together and query it, Snowflake is a great choice.

AWS Redshift is a data warehouse and query tool developed by AWS and is ideal for companies that host most of their data on AWS and want the best functionality and interoperability within the AWS Cloud. If your business is a heavy user of the AWS Cloud, AWS Redshift is a good choice.

TO SEE: Recruitment Kit: Cloud Engineer (TechRepublic Premium)

Data sharing

With a simple point-and-click, Snowflake allows users to copy databases and then share read-only access with others. It’s a fast, automated way to unlock the value of data. At the end of each data sharing, the user can deprovision the data. This secures the data in its original data structure and can also reduce costs.

AWS Redshift is not as automated when it comes to data aggregation and sharing. With Redshift, users (probably IT) have to use multiple ETL extracts of data from different sources to arrive at the final set of data they want to put into a data warehouse that can be made available to users. .

Choose Snowflake or AWS Redshift for Data Warehousing

Snowflake and AWS Redshift are proven data warehouse and processing software 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-cloud (AWS), and what the trade-offs are between highly automated software (with less customization options) and software that gives more flexibility to adapt it to your computing environment. From a cost perspective, Snowflake and AWS Redshift can be managed efficiently, so the choice really comes down to which software is the best platform for your organization.

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