Top 3 Challenges with Amazon Redshift for Multi-Tenant Analytics

Creating a multi-tenant database with Amazon Redshift can be complex. Redshift has great tools for big data, but setting up a multi-tenant reporting function is a different beast. Your product managers think they gave you ample time on your roadmap….but did they really?

Multi-tenant analytics is harder than most engineering teams realize.

We see engineering teams struggle with this all the time. Companies invest far too much only to offer mediocre analytics experiences to end users. Customers of SaaS applications want to be able to customize their own reports. Every business has unique needs, so they want it their way.

Easy task right?

If it was only as easy as pushing the easy button.

Multi-tenant reporting creates an extremely large burden for engineering teams. Internal analytics is much easier to connect a data warehouse to a BI tool. External analytics, also known as embedded analytics, often require building middleware components in-house to prepare data for multi-tenant analytics.

Amazon Redshift is a popular data warehouse solution for SaaS companies to store data. We reviewed an article on designing multi-tenant patterns from AWS. Here are the top three challenges software engineers face when working with multi-tenant databases in Amazon Redshift:

1. Data Isolation and Security

Let’s start with data security. Is Amazon Redshift going to expose your data on purpose? Of course not, Amazon goes to great lengths to secure their products.

However, it is your job to implement data isolation and security in a multi-tenant environment. Tenants must not have access to each other’s data. Any breach in this isolation can have severe consequences for your business.

Row-Level Security (RLS):

Implementing RLS is crucial to ensure that each tenant can only access their own data. This involves creating views and managing access control lists.

Engineers need to make strong security rules for databases. Getting this wrong can have large security implications which is risky for the business.

Schema and Database Design

Redshift supports different multi-tenant models, such as:

• the pool model (where all tenant data resides in a single schema with a tenant identifier)
• the bridge model (each tenant has a separate schema)
• the silo model (each tenant has a separate database and data structure)

Each approach has its own complexity in terms of security management. The choice of model affects the level of isolation. Which, in turn, affects the complexity of the access control model of the SaaS application uses.

See why custom data models are a hidden benefit to users.

2. Performance Management and Scalability

Maintaining consistent performance across tenants while scaling the system is a significant challenge.

Resource Allocation

With the pooling model, you share resources. Sounds great, right?

Not so fast. This can lead to noisy neighbor issues where one tenant’s heavy usage impacts the performance of multiple users. Engineers must implement resource governance policies to mitigate this. Methods often include setting query limits or using workload management queues to prioritize critical workloads.

Concurrency Scaling

As the number of tenants grows, so does the demand for concurrent query execution. Redshift’s concurrency scaling can help, but it comes at an additional cost. And you’ll need a knowledgable database administrator to manage it.

Engineers need to design the system to scale efficiently without degrading performance.

This is one of the hardest tasks with multi-tenant analytics. It often involves a combination of scaling out (adding more clusters) and scaling up (enhancing existing cluster capabilities).

Getting this wrong is really expensive. Not to mention you risk damaging customer satisfaction.

Data Sharing

Amazon Redshift’s data-sharing feature makes it simple to share data between clusters, improving efficiency by separating ETL and BI tasks. While this setup helps to manage load distribution and performance optimization, it still requires thoughtful planning and management.

3. Operational Complexity and Maintenance

Managing a multi-tenant database system increases overhead and complexity quite a bit.

ETL Pipeline Complexity

In a multi-tenant setup, the ETL pipelines need to handle data for multiple tenants. You need to ensure that you correctly ingest, transform, and load the data. All within the appropriate tenant-specific structures.

This requires sophisticated job orchestration and monitoring to handle failures and ensure data integrity.

The complexity increases when you want to analyze multiple data types as well. This gets into the data warehouse vs data lake debate, but data lakes are going to help here is the bottom line. Otherwise, it’s separate data pipelines for every data source.

Schema Management

Managing schema changes in a multi-tenant environment can be cumbersome. You will need to replicate each schema change across all tenant-specific schemas. This typically requires additional automation tools and version control mechanisms to ensure consistency and avoid downtime.

Cost Management

Multi-tenant architectures often involve additional clusters and resources to meet the performance and isolation requirements. Engineers need to learn cost-tracking and optimization strategies.

Challenge	Description	Key Components
Data Isolation and Security	Ensuring each tenant’s data is securely isolated to prevent unauthorized access.	– Implementing Row-Level Security (RLS) – Creating views and managing access control lists – Designing schema and database based on multi-tenant models (pool, bridge, silo)
Performance Management and Scalability	Maintaining performance consistency and scalability as the number of tenants grows.	– Resource Allocation (mitigating noisy neighbor issues) – Concurrency Scaling (managing query execution) – Data Sharing (efficiently distributing load)
Operational Complexity and Maintenance	Handling the increased complexity and overhead in managing multi-tenant databases.	– ETL Pipeline Complexity (ensuring correct data processing) – Schema Management (replicating schema changes) – Cost Management (optimizing resource usage and tracking costs)

While Amazon Redshift provides powerful features for implementing a multi-tenant database, the challenges of data isolation, performance management, and operational complexity are significant.

Solving these problems requires a good grasp of Redshift’s technical abilities and the needs of multi-tenant architecture.

Or…..you could try Qrvey.

Our primary purpose to remove the burden of building the blocks in between data warehouses and web applications. So all those components that require building in-house can be eliminated with Qrvey.

Here’s a quick overview of life with and without Qrvey:

When you integrate Qrvey, you get a complete data management layer:

• a semantic layer to translate user permissions to data security using an inheritance model
• a multi-tenant data lake for analytics data storage
• a unified data pipeline that can handle a large number of types and sources
• a self-hosted solution so your data never leaves your cloud environment

Together, this means your embedded analytics solution function scales now and into the future.

You can offer a better experience for your end-users. This eliminates the development effort of building and maintaining a custom analytics layer. Who doesn’t want that?

Watch our video below that goes a bit deeper.

Then let’s chat about how we can work together to make your engineering team more efficient.