Successful, thriving businesses rely on sound intelligence. As their decisions become increasingly driven by data, it is essential for all gathered data to reach the right destination for analytics. A high-performing cloud data warehouse is indeed the right destination.
Data warehouses form the basis of a data analytics program. They help enhance speed and efficiency of accessing various data sets, thereby making it easier for executives and decision-makers to derive insights that will guide their decision-making.
In addition, data warehouse platforms enable business leaders to rapidly access historical activities carried out by an organization and assess those that were successful or unsuccessful. This allows them to tweak their strategies to help reduce costs, improve sales, maximize efficiency and more.
AWS Redshift and Snowflake are among the powerful data warehouses which offer key options when it comes to managing data. The two have revolutionized quality, speed, and volume of business insights. Both are big data analytics databases capable of reading and analyzing large volumes of data. They also boast of similar performance characteristics and structured query language (SQL) operations, albeit with a few caveats.
Here we compare the two and outline the key considerations for businesses while choosing a data warehouse. (Remember, it is not so much about which one is superior, but about identifying the right solution, based on a data strategy.)
It offers lightning-quick performance along with scalable data processing without having to invest big in the infrastructure. In addition, it offers access to a wide range of data analytics tools, features pertaining to compliance and artificial intelligence (AI) and machine learning (ML) applications. It enables users to query and merge structured and semi-structured data across a data warehouse, data lake using traditional SQL and an operational database.
Redshift, though, varies from traditional data warehouses in several key areas. Its architecture has made it one of the powerful cloud data warehousing solutions. Agility and efficiency offered by Redshift is also not possible with any other type of data warehouse or infrastructure.
Several of Redshift’s architectural features help it stand out.
Data can be organized into rows or columns and is dictated by the nature of the workload.
Redshift is a column-oriented database, enabling it to accomplish large data processing tasks quickly.
It is a distributed design approach with several processors employing a divide-and-conquer strategy to massive data tasks. Those are organized into smaller tasks which are distributed amongst a cluster of compute nodes. They complete the computations simultaneously rather than in a sequential manner. The result is a massive reduction in the duration of time Redshift requires to accomplish a single, mammoth task.
No organization or business is exempt from security and data privacy regulations. One of the pillars of data protection is encryption, which is particularly true in terms of compliance with laws such as GDPR, California Privacy Act, HIPAA and others.
Redshift boasts of robust and customizable encryption options, giving users the flexibility to configure the encryption standards that best suits their requirements.
It determines the maximum number of clusters or nodes that can be provisioned at a given time.
Redshift preserves concurrency limits similar to other data warehousing solutions, albeit with flexibility. It also configures region-based limits instead of applying one limit to all users.
It is one of prominent tools for companies that are looking to upgrade to a modern data architecture. It offers a more nuanced approach in comparison to Redshift, which comprehensively addresses security and compliance.
Snowflake is cloud-agnostic and a managed data warehousing solution available on all three cloud providers: Amazon Web Services (AWS), Azure and GCP. Organizations can seamlessly fit Snowflake into the existing cloud architecture and be able to deploy in regions that best suit their business.
Snowflake has a multi-cluster shared data architecture, which allows it to separate out compute and storage resources. This feature helps users with the ability to scale up their resources when they require large data volumes to load faster and scale down once the process is complete.
To help with minimal administration, auto-scaling and auto-suspend features have been implemented by Snowflake.
Delivered as a Data Warehouse-as-a-Service, Snowflake enables companies to set up and manage the solution without needing significant involvement from the IT teams.
The Snowflake architecture enables the storage of structured and semi-structured data in the same destination with the help of a schema on a read data type known as Variant, which can store structure and semi-structured data.
Features: Redshift bundles storage and compute to offer instant potential to scale to enterprise-level data warehouse. Snowflake, on the other hand, splits computation and storage and provides tiered editions. It thus offers businesses flexibility to buy only the required features while maintaining scaling potential.
JSON: In terms of JSON storage, Snowflake’s support is clearly the more robust. Snowflake enables to store and query JSON with built-in and native functions. On the flip side, when JSON’s loaded into Redshift, it splits into strings – making it challenging to query and work with.
Security: While Redshift consists of a set of customizable encryption options, Snowflake offers compliance and security features geared to specific editions. It thus provides a level of protection most suitable for an enterprise’s data strategy.
Data tasks: A more hands-on maintenance is necessary with Redshift, particularly for those tasks that cannot be automated, like compression and data vacuuming. Snowflake has a benefit here: it automates many of such issues, helping save substantial time in diagnosis and resolving of those issues.
Whether it is Redshift or Snowflake, when it comes to business intelligence (BI), both are very good options as cloud data warehouses. Irrespective of the choice of data warehouse, getting all the data to the destination as quickly as possible is essential to provide the background required for sound BI.
By Uma Raj
By Uma Raj
By Abishek Balakumar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.