Data warehouses have centralized information repositories, helping enterprises analyze them and influencing their decision-making into more informed ones.
They are especially helpful in streamlining regular data flows from transaction systems, through relational databases, and to other sources on frequent pulses. However, they aren’t designed to cope with today’s explosive data growth and pace up with user’s ever-changing needs.
Cloud data warehouses are a gamer changer. As tech evolves, cloud data warehouses revolutionize workflows by seamlessly storing data from different business applications like databases, enterprise resource planners (ERPs), and marketing applications. This, in turn, enhances enterprises’ business intelligence architecture.
Choosing the right cloud data warehouse for your business can be overwhelming cause you may not shift to another option after getting started. This post, however, compares two leading choices: Snowflake and Redshift. But before delving into the contrast, let’s evaluate the need for cloud data warehouses.
Why Use Cloud Data Warehouses?
In today’s business era, you can’t be constrained in physical data centers. You need to gain advanced business analytics to improve operations, gain a competitive advantage, and enhance customer experience.
Cloud data warehouses help you harvest vast data amounts and securely store them in a balanced platform. So, what are the benefits of using cloud data warehouses? Read on.
On-premises solutions are expensive in terms of hardware acquisition, consume significant time for upgrades, and need consistent maintenance and output management.
Cloud warehouses are cheaper as you’re only charged for computing power and the storage you seek (there’s no need for server rooms, networking, and other hardware). On evaluating speed and performance, cloud solutions are faster because they use multiple servers and load balancers. You can also integrate the cloud data warehouses seamlessly with multiple data sources, enhancing creativity in your projects.
Cloud data warehouses provide in-depth visibility into your business data without compromising computing power. This gives you a strategic approach to making value-adding, informed business decisions.
Unlike traditional solutions, where you’d have to hire a database security team, cloud solutions utilize multi-factor authentication (MFA) to handle data securely. Most cloud data solutions have virtual private networks (VPNs) to prevent breaches.
Expansive data storage is an added benefit with “pay-as-you-go” operation models. In the past, you’d have to estimate computing power and storage needs up to three years in advance, which may need to be revised multiple times and cost your enterprise a fortune.
Additionally, on-premise solutions are expensive in disaster handling. For instance, companies need backup data centers in case an issue occurs. Today, most cloud data warehouse solutions have asynchronous data duplication and version control systems – improved dependability. And if you’re worried about elasticity, cloud solutions pair infrastructure needs to appropriate resources.
This, in cascade, handles the demand for cynical changes in modern business. For example, during holidays, you can provision more analyst users. When holidays are over, you can de-provision temporary employees. These are the tip of the iceberg; there’s more to benefits.
Snowflake: An Overview
Snowflake is a leading cloud-based, analytic data warehouse built as a software-as-a-service (SaaS) platform. Running on major cloud vendors like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure Snowflake independently scales storage and computing power.
Unlike other platforms built on existing databases, Snowflake uses SQL database engines with a cloud-designed architecture. Its power is realized in cases where you’re handling instantaneous data processing. For instance, your company could run a live voting session or a new marketing campaign. It’s well known for loading, analyzing, and generating reports of large data volumes.
At its core, Snowflake is three-layered. It comprises database storage, query processing, and cloud services. The database governs files’ sizes, structure, and tied metadata. Snowflake uses virtual warehouses to process queries where each warehouse acts as a standalone and does not share computing resources. Snowflake binds many components under cloud services, including authentication/access control, infrastructure management, and query parsing.
Redshift is an Amazon-based cloud data warehouse for large data sets, analysis, and database migrations. Integrating it with business intelligence (BI) tools allows you to leverage data and gather valuable insights about your business and customers. Whatever the data set size, Redshift performs quickly for queries, which can be done on SQL-based or BI tools.
This is possible through Redshift clusters, where nodes are partitioned into slices. Slices are then allocated some portion of memory to optimize query performance. Moreover, Redshift has internal networking components and high bandwidth connections that facilitate high-speed connections between nodes.
The optimization allows you to query petabytes of data without burdening storage and managing servers. Striking a balance between easy setup, robust customization options, and maintenance, Redshift presents itself as a powerful cloud solution. You can also blend it with third-party data integration and data mining tools.
Snowflake vs. Redshift
While Snowflake and Redshift offer similar advantages like scalability, flexibility, and handling vast data, each has unique identifiers. Both solutions are easy to set up and get started. When contrasting the two solutions, your key areas to focus on are:
Integration and Performance
Let’s delve into each of these parameters in detail.
#1. Key Features
Here’s a breakdown of key features.
Snowflake comprises several features which make it an ideal data warehouse. It has secure storage and data protection measures. It supports SQL to run transactions, user-defined functions, scripting, and geospatial data support.
Snowflake’s interface and tooling are extensive, with Visual Studio Code extensions, Snowsight, and SnowSQL for efficient data query and management. For working with other applications and extensibility, Snowflake supports APIs for Java, Python, and Scala.
It also works well with RESTful APIs and various client connectors/drivers. With its data import and export features, you can work with bulk data in diverse formats and continuous loading for compatible storage. Also, it has replication and failover tools to ensure business continuity in cases of disaster recovery.
Redshift’s key features include federated queries, AWS Data Exchange integration, machine learning for SageMaker model creation, spark integration, Aurora integration with real-time analytics, streaming data integration, and easy data lake integration.
Redshift seamlessly integrates with multiple AWS tools. Redshift has flexible pricing options. In the security context, multiple security components make it robust.
It’s user-friendly and allows for automated table designs and multiple data querying tools. The platform supports granular access control and complies with established security standards.
#2. Integration and Performance
Based on the cloud services you’re using, there’s a need for a natural choice, as integration and performance affect your overall operations.
If your enterprise already uses AWS services, other solutions like AWS CloudWatch, AWS DynamoDB, and AWS Athena seamlessly integrate with Redshift. If your data is on Amazon products, you can use the massive parallel processing and load data faster.
However, it would be best to be cautious when working with sort and distribution keys; they define how your system stores and works with data. If not well planned, they pose performance issues.
There’s a steep learning curve here. Beware that once keys are set, you can’t modify them, so you need a solid understanding before implementing them. Maintenance can be complex. You’ll also need to add caching layers for optimal performance when working with live apps.
Such integrations aren’t available with Snowflake and may pose a challenge to iterate. However, Snowflake has other options like Apache Spark, Tableau, Qlik, and IBM Cognos. You can leverage Snowflake to build a comprehensive and efficient data system.
Some key aspects of integrating Snowflake with other features include Extract Transform Load Tools (ETL) – which mold data into desired formats; visualization tools – which help in the analysis for decision-making; data integration platforms – which allow the sharing of data, and data catalogs – which let you understand and collaborate on data assets.
As of this writing, data is among the valuable assets of any project, which is why it needs to be secured. When working with data warehouses, challenges arise as you work with new data sources, opening up new vulnerabilities. Both solutions offer various components to secure data.
Snowflake’s documentation on security elaborates on multiple features, including federated authentication, key-pair authentication/rotation, multi-factor authentication, network policies, private endpoints for internal stages, private services, access control, end-to-end encryption, and encryption key management.
User authentication and access are divided through external entities in the federation environment. In this case, Snowflake is the service provider, and the authentication is handled using an identity provider like Microsoft Identity provider.
Regarding key pair authentication, you generate public and private keys that form the print verification base. The keys can then be replaced on a schedule you customize (rotational-based). Moving to multi-factor authentication, Snowflake has a Duo Security service available on iOS, Windows, and Android operating systems.
However, you must enroll in the MFA program to use it. If you’re working with Snowflake and third-party services, you can use OAuth to generate access and optional refresh tokens for security integrations. Supported external authorization servers, custom clients, and partners, including Okta, Microsoft Azure AD, Microsoft Power BI, Sigma, and Ping Identity PingFederate, among other External OAuth Custom Clients.
Fast forward to the network policies; Snowflake restricts access to your account as per user IP addresses, where you can declare an IP allowed and block lists. When dealing with AWS, Google, and Azure virtual private cloud, the setups are designed to offer secure connectivity across Snowflake internal stages to ensure data loading and unloading doesn’t occur on a public network. You can use SCIM, an open specification that manages user identities in cloud products using RESTful APIs when managing users and groups.
With access control, you can use a framework to enact discretionary access control (DAC), where objects have owners who control access to the object, or role-based access control (RBAC), where access privileges are tied to roles that, in turn, are granted to users.
Finally, Snowflake has end-to-end encryption that restricts third-party applications from reading through data (whether at rest or in transition) to minimize the surface of attacks.
Amazon Redshift’s data security is distinctive from Amazon Redshift’s security and has multiple security aspects. They include sign-in credentials, access management, cluster security groups, virtual private clouds (VPCs), cluster encryption, secure socket layer (SSL) connections, column and row-level access controls, and security for data in transit.
AWS security credentials verify your identity to determine whether you can access resources; they authenticate and authorize your requests. In this case, a root user has long-term credentials without expiry dates, while federated and successor product users have temporary accessory credentials.
Security in the AWS cloud is highly prioritized and implemented in a shared responsibility model. To control your Redshift resources, there are several levels. The first is cluster connectivity, specifying AWS instances connecting with Redshift.
Next, database access controls how user accounts interact with tables and views. You’re also granted temporary database credentials and single sign-in for SQL clients to authenticate users on your database and those outside AWS. You can also use MFA to add extra security levels by leveraging codes, touch or tap methods, and biometric scans.
Amazon Redshift cluster groups allow you to define who can access your clusters. You can also protect the cluster using a virtual networking environment. Data in your tables is protected using cluster encryption, which you can turn on at will.
Secure socket layer connections enhance security between your SQL clients and clusters. This also applies to data transit. With columnar and row-based access controls, view-based access and role-attached policies exist, respectively.
Highlighting the Differences
Here’s a table to summarize the key differences.
Federated queries AWS Data Exchange integration Machine learning for SageMaker model creation Spark integration Aurora integration with real-time analytics Streaming data integration Easy data lake integration Seamless integration with multiple AWS tools Flexible pricing options Granular access control Compliance with security standards
Integration with AWS services like CloudWatch, DynamoDB, and Athena Parallel processing for faster data loading (with caveats) Cautions regarding the sort and distribution keys Complex maintenance Need for caching layers for optimal performance
Integration and Performance
Sign-in credentials Access management Cluster security groups Virtual private clouds (VPCs) Cluster encryption (optional) SSL connections Column and row-level access controls Security for data in transit Shared responsibility model Temporary database credentials Single sign-on for SQL clients MFA support for extra security Cluster groups and virtual networking for access control End-to-end encryption (data protection)
Limited integration with AWS services Integration with Apache Spark, Tableau, Qlik, and IBM Cognos ETL tools support Visualization tools for data analysis Data integration platforms Data catalogs for data asset collaboration
Integration with AWS services like CloudWatch, DynamoDB, and Athena Parallel processing for faster data loading (with caveats) Cautions regarding the sort and distribution of keys Complex maintenance Need for caching layers for optimal performance
Secure sign-in credentials Access management Network policies for IP address-based access control Security for data in transit within AWS, Google, and Azure VPCs SCIM support for user identity management Access control via DAC and RBAC End-to-end encryption to restrict third-party access Multi-factor authentication with Duo Security (enrollment required) OAuth for third-party services Integration with various identity providers
Use Cases of Snowflake and Redshift
Snowflake is a good choice for your organization when:
Handling light query loads.
Your workload needs frequent scaling.
Your enterprise needs a managed solution with zero operational overhead.
Amazon Redshift is a good choice when:
Handling high query loads.
Your organization is using AWS services.
Your workloads operate on structured data.
Snowflake vs. Redshift – Choosing Between the Two
As you’ve seen, Snowflake and Redshift are good cloud data warehouse solutions options. Redshift will work well if you already use other AWS services but has a steeper learning curve when configuring database sorting and distribution keys.
Snowflake may not easily integrate with AWS services but will provide extensive features for working with data to gather meaningful insights. Redshift has flexible pricing, but Snowflake has automated data recovery features.
The security domain is not an exact deal breaker, as the levels are based on how well you’re conversant with your solution operation model. When well configured, either service you choose to work with provides robust security for your data operations.
How you choose a solution for your needs will depend on the type of integrations you’d like to have and the security patterns you’d like to work with. If you keep the right concerns in mind, you’ll make the best choice to suit your needs.