Multi-Tenant Analytics and Its Importance 

Multi-Tenant analytics allows groups of users, known as “tenants,” to securely access their data within a SaaS platform. Multi-tenant analytics enable SaaS companies to embed analytic capabilities directly within their applications, creating a seamless user experience for their customers.

SaaS providers often serve numerous clients. Each of these customers has their own unique data that supports their particular business needs. With multi-tenant embedded analytics, each customer – or tenant – can analyze and extract valuable insights from their own data.

Multi-tenant analytics software enables all customers to use the same application to analyze their data. Meanwhile, their data at rest remains isolated and secure from other tenants, ensuring privacy and security.

Common Challenges with Scalable Multi-Tenant Analytics

Creating performant, secure, and scalable multi-tenant analytics is difficult and requires overcoming steep data engineering and infrastructure challenges. Specifically, multi-tenant analytics platforms must isolate data between different customer tenants to ensure data security.

Additionally, multi-tenant analytics has unpredictable traffic patterns, making it difficult for SaaS providers to manage performance.

What is a Data Engineer?

Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data. The role is particularly important when data volumes reach a large scale. A data engineer manages the processes required for organizations to gather meaningful insights from data.

The activities of a data engineer include collecting and managing data. In addition, converting it into usable formats and managing systems for analytics. Data engineers need skill sets that include:

  • Building data pipelines 
  • Managing data warehouses
  • Ensuring compliance with data governance
  • Using technologies such as relational databases, NoSQL Data Stores, and big data tools

While we might associate “engineering” with specific fields like aerospace and automotive. However, nearly every industry uses data, and it creates more data every second. In 2020, users generated 64.2 ZB of data, which exceeded the number of detectable stars in the cosmos.

Joe Reis, in his book Fundamentals of Data Engineering, discusses how data engineers work together with other professionals. “Data engineering is the intersection of security, data management, DataOps, data architecture, orchestration, and software engineering. A data engineer manages data from collection to analysis and machine learning. They gather data from source systems and create data models for different purposes. 

What is a Software Engineer?

Michigan Technological University defines software engineering as, “The branch of computer science that deals with the design, development, testing, and maintenance of software applications.” Software engineers make various systems.

These systems include video games for PC and console. They also create the mobile apps we use every day. They also create business applications like cloud security platforms that generate raw data for analytics.

Software engineers design new systems and maintain software applications. They update systems, add new features, fix security issues, and make software faster and more scalable. Releasing new apps and maintaining existing ones both require evaluating and testing.

Software engineers must also maintain documentation, which is important, but often ignored. Software engineers collaborate with numerous other professionals. Examples include developers, systems architects, data scientists, security specialists, and product managers who consult with current and prospective users. 

Developers, on the other hand, spend more time working independently, focused on code. Software development is a subset of software engineering. In this scenario, engineers supervise the overall design and architecture of an application, including connecting different components.

Both engineers and developers require time management, creativity, and presentation skills. Coursera lists multiple skills software developers require, including:

  • Proficiency in popular programming languages 
  • Ability to write and test code
  • Proficiency in developing responsive web designs
  • Ability to work on front-end application development 
  • Knowledge of algorithms and data structures

According to the Bureau of Labor Statistics, the overall employment of software developers is projected to grow 25% from 2022 to 2032. This stat includes quality assurance analysts and software testers. This growth rate is much higher than the average for all occupations.

Common Tasks of Data Engineers, Data Scientists, & Software Engineers

Data engineers make data accessible to those analysts who can use it to improve business outcomes. This can involve ensuring the data is easy to understand for numerous users.

The Pragmatic Engineer describes one example. “Many [data engineering] teams will set standards on naming conventions in order to allow anyone who views the data set to quickly understand what data type a field is…”

The article continues: “The purpose of these changes is to treat data as a product; one that data analysts and data scientists can then build their models and research from.” 

In describing this relationship, Marijn Markus, AI Lead and Managing Data Scientist, says data engineers help data scientists stay afloat.

data engineer

Software engineers design and develop software systems and applications, of which analytics may be but one component. Among these professions, data scientist has the unique accolade of being named “The Sexiest Job of the 21st Century” by Harvard Business Review.

Why Typical Job Skills of a Data Engineer Are Essential for Multi-Tenant Analytics

Specialized skills are necessary for managing multi-tenant analytics and large data volume queries, including:

  • Managing concurrent analytical workloads
  • Implementing sophisticated security models
  • Designing high-performing query engines

These skills aren’t common among software engineers, making the involvement of data engineers essential. Some describe data engineers as plumbers who pipe data from one place to the other. Additionally, it is their job to make sure it’s clean for the business to use.

The Pragmatic Engineer Blog states, “In short, data engineers play an important role in creating core data infrastructure that allows for analysts and end-users to interact with data which is often locked up in operations systems.” Data engineers must process data for machine learning to use it.

Data warehousing, in particular, requires significant up-front modeling. Engineers must build several logical layers in a SaaS application to get data ready for multi-tenant analytics. (Check out our Snowflake vs Redshift article on these challenges.)

Data engineers must build additional infrastructure and data processes between the database and the user-facing application. Data warehouses do not handle the multi-tenancy logic. This leads software engineers to build everything in between the data and charts.

To connect the two, data engineers must build another semantic layer. This translates user permissions from the SaaS application to data permission. This also requires the creation of an entitlements database, another component to build and manage.

Additionally, data warehouses don’t easily scale out for multi-tenancy without significant development effort. Depending on the tool, data warehouses can be harder to optimize for concurrency without building out larger (and expensive) clusters. We need data engineers’ expertise to manage all these challenges in scalability, performance, and cost.

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Why Software Engineers Struggle with Scalable Multi-Tenant Analytics

With multi-tenant embedded analytics, each customer – or tenant – can analyze and extract helpful insights from their own data. The ultimate goal is to obtain more value from the SaaS product. However, software engineers can struggle to build it, particularly with the following challenges.

Ensure Tenant Data Security

Multi-tenant analytics platforms must isolate data between different customer tenants. This requires tackling permissions, access controls, and strict separation mechanisms to prevent unauthorized exposure. This challenge becomes even more difficult as data volumes, queries, and user concurrency expand over time.

Manage Performance

Customers can access reports and dashboards independently. Each customer has their own unique usage patterns and needs, leading to unpredictable traffic patterns. SaaS providers have a big challenge. They need to make sure their data systems can handle the needs of many tenants and lots of data efficiently. 

Integrate Disparate Data Sources

While some apps use only one database, many SaaS providers must combine different data sources. Complex analytics usually require data from multiple, diverse sources for multi-tenant scenarios.

Companies typically must build separate pipelines with dedicated ETL for every data source. With the extensive variety of sources, the task of data integration can become quite daunting. Flexible and repeatable pipelines are essential for the platforms that manage these multi-tenant analytics.

Map User Roles to Data Access

Customers will access a shared data source for reporting. This means that the SaaS application requires a mapping exercise for user permissions to the analytics data. This is another task that is particularly difficult to maintain over time. This is a big reason that building vs buying is inefficient.

Scale Smoothly

As your user base grows, more development tasks become increasingly demanding. This one involves seamlessly migrating tenant data and ensuring smooth onboarding. Handling growing data while minimizing disruption to the end users requires careful planning and execution.

One common approach to building multi-tenant analytics is to connect the front-end dashboards directly to the database. This works for static, one-size-fits-all reporting. Typically, you create a database per tenant or add a tenant variable to a shared database. Unfortunately, building and maintaining over time requires substantial efforts, such as:

  • A data pipeline for any necessary data transformations
  • Another database or data warehouse specifically for analytics
  • A semantic layer that adds multi-tenant security logic
  • An API layer so the front end can make calls to its back end

How Qrvey Removes the Need for Data Engineers

Many people often view data engineering as a highly desirable job in the 21st century. SaaS companies that use Qrvey can create and release analytics on their own. They don’t rely on assistance from data engineers nearly as much as a build-it-yourself situation.

Qrvey offers a turnkey multi-tenant analytics platform that includes:

  • A single API to ingest any type of data
  • Pre-built data connectors to common databases and data warehouses
  • A transformation rules engine
  • A data lake optimized for scale and multi-tenant security
  • A unified data pipeline

Qrvey offers flexible data integration options to cater to various needs. It can connect to databases and store data in its own data lake using ElasticSearch. This data lake approach optimizes performance and cost-efficiency for complex analytics queries. Additionally, the system automatically normalizes data during ingestion to make it ready for multi-tenant processing and reporting. 

Qrvey supports connections to common databases and data warehouses. This includes an ingest API for real-time data pushing, and handling JSON and semi-structured data formats like FHIR.

You can also import data from cloud storage, such as S3 buckets. This allows you to work with unstructured data, such as documents, text, and images. Qrvey includes data transformations as a built-in feature, removing the need for separate ETL services.

With Qrvey, bridging the gap between data and software becomes much less daunting. Our goal is for you to build less software and deliver more value to customers. See for yourself.

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