Building a Data Warehouse for Multi-Tenancy: What You Need to Know

Here’s what you need to know about building a data warehouse for multi-tenant analytics, including the challenges and solutions.

Building a data warehouse is a significant undertaking for any organization, but the complexities multiply when it comes to multi-tenancy for software as a service (SaaS) products.

While data warehouses (and data lakes) offer incredible potential for embedded analytics, they aren’t inherently designed to handle the nuances of multi-tenancy. You should know the critical challenges of building a data warehouse in a multi-tenant environment and the solutions.

From design complexities and cost considerations to scaling hurdles and security concerns, here’s the blueprint for navigating the intricacies of building a data warehouse.

How to Build a Data Warehouse: Why the Traditional Structure Doesn’t Work Anymore

Choosing an appropriate data storage model to build a data warehouse carries long-term implications for your SaaS product.

When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital. Data warehousing tools such as Snowflake and Amazon Redshift are great at solving problems surrounding data optimization and data storage. However, they’re imperfect because multi-tenant analytics isn’t their primary use case despite having the purpose of backing internal analytics capabilities.

SaaS products cannot simply connect their charts to a data warehouse. The biggest reason: data security.

Data security requires custom-developed middleware in the form of metatable tables, user access controls, and a semantic layer that orchestrates everything.

How Long Does It Take to Build a Data Warehouse?

Building a data warehouse from scratch depends on the size of the data warehouse, typically taking as little as a few weeks or as long as several months. Completing the project could take over a year, however, if the data warehouse contains complex data sources and massive data volume.

Of course, other factors to consider include the resources your organization can invest in building a data warehouse.

Biggest Challenges When Building a Data Warehouse (& the Solutions to Fix Them)

With a blueprint on how to build a data warehouse and an idea of the timeframe it takes, you now need to know the challenges that will likely arise. Knowing the challenges, you’ll avoid wasting resources and losing valuable time when building your data warehouse.

Here are the challenges typically encountered when building a data warehouse:

• Design difficulties
• Higher costs
• Scaling demands
• Unstable analytics performance
• Data security and governance gaps

Let’s examine the challenges of building a data warehouse for multi-tenant analytics—and the solutions to overcome them.

Design difficulties

Data warehouses aren’t typically built to handle the multi-tenancy logic required for the application layer. To connect the two, you must build another semantic layer that translates your front-end web application, multi-tenant logic back into the data warehouse logic.

Connecting multiple components is a normal part of the development process, but this is particularly cumbersome.

One standard solution is to provision separate databases per tenant, requiring your app to communicate with each one. This requires building extra logic into the front end, which is challenging and not performant.

Amazon Web Services outlines three strategies for multi-tenant storage, which we’re explaining below:

• Pool model: All tenants share the same storage infrastructure and resources, such as a single database. Data is typically partitioned logically using identifiers to ensure tenant isolation, offering cost-efficiency and simplified management.
• Bridge model: A hybrid approach combining elements of the Pool and Silo models, this allows for shared resources like application logic while maintaining dedicated resources like databases for individual tenants, balancing cost-efficiency and data isolation.
• Silo model: Provides complete isolation by dedicating separate infrastructure and resources, like databases, to each tenant. This maximizes security and customization but increases costs and management overhead.

Snowflake also provides three design patterns: Multi-Tenant Table (MTT), Object Per Tenant (OPT), and Account Per Tenant (APT). Between Amazon Web Services and Snowflake, the Pool and Multi-Tenant Table approaches are essentially identical, and it’s the one Qrvey recommends when building a data warehouse.

As Snowflake states, “MTT is the most scalable design pattern in terms of the number of tenants an application can support. This approach supports apps with millions of tenants. It has a simpler architecture within Snowflake. Simplicity matters because object proliferation makes managing myriad objects increasingly difficult over time.”

Higher costs

Data warehouses for multi-tenant analytics require significant modeling and engineering up front, resulting in substantial costs. You need heavy development lifts initially, with the demand to build additional infrastructure and logic processes between the database and user-facing application.

In addition to the high upfront development costs, ongoing costs can be high, especially with expensive per-query fees. This is mainly a problem with the Snowflake data cloud. Snowflake was first launched as a low-cost alternative to traditional data warehouses like Oracle and Teradata, with upfront license fees of six or seven figures. Snowflake gets users in the door with zero infrastructure costs upfront.

Similar to public cloud infrastructure, your expenditure scales with your usage. It’s logical for costs to rise with increased usage, but the cost increase should be proportionate to the added value.

With Snowflake, once you require more power, the cost jump is exponential, doubling at each tier. Snowflake could be an excellent fit for enterprise business intelligence, where you load data from a single source into your warehouse once every night and then shut it off. In that traditional use case, you could expect significant quantities of idle time, but that goes out the window when your app must be available almost instantly to everyone.

SaaS companies aim to provide efficiency to end users within a customer’s organization, making products stickier and increasing retention. However, this also makes utilization less predictable. Real-time, interactive embedded analytics that delivers responsive queries requires larger environments, which raises Snowflake costs excessively.

Warehouse customer data platform Rudderstack describes the cost challenges when accessing your data warehouse: “Unoptimized queries cause the execution to take longer, affecting the performance of the database and increasing cost. The cost consideration is especially important in a warehouse solution like Google’s BigQuery which charges by query execution time.”

To solve this challenge, significantly reduce costs using an embedded analytics platform and a data synchronization model.

Qrvey, for example, introduced ‘Live Connect,’ a feature enabling real-time data support to optimize data sources like Snowflake, Amazon Redshift, and PostgreSQL in terms of performance while lowering costs. It unlocks flexibility to optimize usage by deciding what will sync live versus a predefined schedule.

Scaling demands

In a multi-tenant data warehouse, one shared data warehouse holds data from multiple sources and makes it available for various analysis and reporting purposes. This single instance of a data warehouse services multiple tenants, each with its own isolated data set.

While this may sound simple and effective, this solution has several challenges. Data warehouses don’t easily scale for multi-tenancy without significant development effort.

Anything can scale with enough effort, but SaaS companies must consider the effort necessary to get there. To achieve multi-tenancy when building a data warehouse, you need to build additional infrastructure and processes between the database and the user-facing application.

To achieve smooth scaling, SaaS companies need a solution that employs auto-scaling, optimizes data partitioning, and improves query performance. The goal is to minimize operational overhead while ensuring consistent performance across all tenants.

Unstable analytics performance

Without building larger clusters, data warehouses can be harder to optimize for concurrency.

If you proceed to build out those large clusters to meet peak demand, they’re likely to spend time sitting idle outside those peaks. And despite sitting idle, you’ll likely still pay for parts of the process that may use on-demand pricing, negating a key advantage of cloud infrastructure.

Snowflake pricing, for example, is based on compute, storage, and cloud services usage. Data warehouses are available in X-Small to 6X-Large, with each tier doubling the cost and compute power.

Provisioning for peak usage may give your customers the necessary query speed, but you’ll often use extensive computing power for non-peak usage as well.

Again, if you establish the proper architecture at the outset, it can work effectively. Amazon Web Services, for example, emphasizes the need to balance managing costs while providing consistent performance and better customer experience.

Data security and governance gaps

Data warehouses function well as a single source of truth for internal reference at one company. Yet they’re not natively built for row-level security in multi-tenant environments. Every data warehouse solution requires extra effort to secure tenant-level separation of data, and this challenge is compounded with user-level access control.

As Microsoft writes, “A particular concern for multi-tenant data solutions is the level of customization your support. For example, can a tenant extend your data model or apply custom data rules? Ensure that you design for this requirement upfront. Avoid forking or providing custom infrastructure for individual tenants. Customized infrastructure inhibits your ability to scale, to test your solution, and to deploy updates. Instead, consider using feature flags and other forms of tenant configuration.”

Fortunately, you can support role-based access control when building a data warehouse. Be intentional—think about it upfront and understand the effort required to build this middle layer, which is time-consuming and potentially expensive.

Building a Data Warehouse to Deliver Multi-Tenant Analytics

Building a data warehouse for multi-tenant analytics is complex—yet not insurmountable. Understanding the most significant challenges prepares you to lay the groundwork for a successful implementation.

You need to choose the right architecture, like the Pool model or Multi-Tenant Table approach, for scalability and efficiency, and remember to factor in the ongoing costs of your data warehouse and explore solutions like an embedded analytics platform to optimize performance (as well as spending).

In multi-tenant analytics, a data warehouse is a secure, centralized hub for all your tenant data. It allows for efficient querying and analysis across tenants while maintaining data isolation. It empowers your customers with self-service analytics across users, enabling them to generate customer reports, dashboards, and visualizations tailored to their specific needs—all within your SaaS product.

Instead of building the expensive and resource-intensive middle layer, why not integrate an analytics layer that is already built for multi-tenancy? Learn how the right architectural approach for multi-tenant analytics sets you up for success.

Book a product tour to discover how Qrvey’s embedded analytics platform helps SaaS leaders save time and get to market faster, while lowering development costs.