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12 minute read · January 20, 2025

Building Scalable Data Applications with Dremio

Alex Merced

Alex Merced · Senior Tech Evangelist, Dremio

Building data applications is an exciting but complex journey. As a developer, you're often tasked with transforming raw data into actionable insights while balancing performance, scalability, and cost. Along the way, you face a series of challenges: databases that struggle to handle both transactional and analytical workloads, ETL pipelines that become costly to maintain, and architectures that grow increasingly complex as data sources multiply.

These hurdles are not just technical—they can slow down your development process, make your applications harder to scale, and ultimately affect the user experience. However, by understanding the common pain points in the evolution of data application architectures, you can identify tools and strategies to overcome them.

In this blog, we'll explore the typical challenges developers face as their data needs grow, the evolution of data application architectures to address these challenges, and how Dremio can play a pivotal role in simplifying and scaling your solutions.

Working Straight Out of Transactional Databases: A Costly Starting Point

When building your first data application, it's common to rely on the same transactional databases that power your business operations. These databases excel at handling day-to-day processes like recording sales, managing inventory, or processing customer interactions. However, as you start to layer in analytical capabilities—generating reports, dashboards, or running predictive queries—you quickly encounter a critical issue: your transactional systems aren't designed for analytics.

Handling both transactional and analytical workloads on the same database introduces significant challenges. Transactional queries require low-latency performance, while analytical queries demand high computational resources to process large volumes of data. Running these workloads simultaneously leads to resource contention, where analytical queries slow down transactional performance and vice versa.

To make matters worse, scaling these systems to meet growing demands becomes prohibitively expensive. Vertical scaling—adding more power to your database server—has limits and costs that quickly spiral as your business and data grow. At this stage, many developers realize they need a dedicated solution to separate their analytical workloads from their transactional systems.

Moving to a Data Warehouse: Performance Gains at a Cost

As your application grows and the limitations of transactional databases become evident, the next logical step is to offload analytical workloads to a dedicated data warehouse. This separation solves many initial problems: data warehouses are purpose-built for analytics, providing the computational power needed to handle large-scale queries without disrupting transactional operations.

However, while data warehouses deliver significant performance improvements, they also introduce new challenges. To populate the data warehouse, you need to build ETL (Extract, Transform, Load) pipelines to extract data from transactional systems, transform it into the required structure, and load it into the warehouse. These pipelines can be complex to design and maintain, especially as your data sources grow in volume and variety.

Additionally, scaling the entire ecosystem becomes a balancing act. You need to provision not just your transactional databases and data warehouse but also the compute resources required for running your ETL jobs. Costs can skyrocket as you account for increased storage, compute, and the overhead of managing these interconnected systems.

While data warehouses provide a better foundation for analytics, developers often find themselves looking for a more flexible and cost-efficient solution as their data landscape expands.

Managing Multiple Systems: The Complexity of Distributed Data

As your application scales further, you find yourself working with multiple data systems—transactional databases, data warehouses, and maybe even specialized data stores like NoSQL databases or object storage systems. This diversification is often necessary to meet the varied needs of your application, but it introduces a new challenge: managing connections to all these systems.

Initially, you might handle this by embedding multiple database connections directly into your codebase. Each system has its own APIs, query languages, and authentication mechanisms, forcing you to write custom logic to interact with each source. While this works in the short term, it quickly leads to a tangle of complex, hard-to-maintain code. Small changes, like switching a data source or adding a new system, can result in significant development effort and downtime.

This complexity also spills over to your frontend developers. They rely on backend APIs to fetch the processed data they need, and as the number of data sources grows, so does the complexity of the interfaces you provide. Maintaining consistency and usability in these APIs becomes increasingly difficult, adding friction to the development process.

At this stage, you start to realize that simply managing multiple connections isn't enough. You need a way to unify your data and simplify how both backend and frontend developers interact with it.

Unified APIs: A Step Forward, but Not Far Enough

To address the growing complexity of managing multiple systems, you decide to create a unified API layer. Using technologies like REST, GraphQL, or RPC, you consolidate access to your various data sources, providing a single interface for your frontend developers and application consumers. This unified API layer simplifies data consumption by abstracting the underlying systems, making it easier to retrieve the data needed for dashboards, reports, or user-facing applications.

While this approach helps streamline frontend development, it doesn’t solve the root problems on the backend. Your developers are still responsible for managing the individual connections to each data source. They must handle authentication, query optimization, and data transformations for every system. Combining and processing data from multiple sources often happens within your backend application, which can be resource-intensive and introduces scalability concerns.

The more complex your backend becomes, the harder it is to maintain. Scaling these unified APIs requires careful balancing of resources to ensure they can handle spikes in query load. Moreover, as your application evolves, adding new data sources or modifying existing ones often leads to significant refactoring, further increasing technical debt.

At this point, the limitations of this architecture become clear. While unified APIs are a step forward, you need a more robust solution—one that reduces backend complexity, simplifies data transformations, and can scale efficiently without breaking the bank.

Enter Dremio: Simplifying and Supercharging Your Data Architecture

Dremio is a tool that addresses the challenges of managing, unifying, and scaling your data delivery to your application. By leveraging Dremio, you can eliminate many of the pain points associated with traditional data architectures and unlock a new level of efficiency and performance.

Harness the Power of the Data Lakehouse

One of the first key shifts Dremio enables is the ability to adopt data lakehouse architecture, where data is stored in open formats like Apache Parquet or Apache Iceberg. Unlike traditional data warehouses, which are expensive to scale and lock you into proprietary storage, data lakehouses allow you to store massive amounts of data cost-effectively. Dremio connects directly to your data lake (object storage like s3) and query your data from there, eliminating the need to load the data into the more expensive storage of warehouse for analytics (although Dremio can connect to your data warehouse too).

Accelerate Queries Without Creating Manually Written ETL

With Dremio’s Reflections feature, you can accelerate queries across your data sources without the need for time-consuming ETL processes. Reflections act as a relational cache, allowing Dremio to optimize query execution by precomputing joins, aggregations, and other transformations. This not only speeds up queries but also reduces the load on your source systems, freeing up resources for other operations.

Boost Performance with Live and Incremental Reflections

If you choose to land data in your data lake storage as Parquet or Iceberg tables, Dremio’s reflections become even more powerful. Features like live reflections and incremental reflections ensure that your data transformations and optimizations stay up to date with minimal compute. Live reflections keep pace with real-time changes in your data, while incremental reflections efficiently process only the data that has changed, saving time and resources.

Unify Data Across All Sources

Dremio simplifies data unification by managing connections to your data sources such as databases, data lakes, data lakehouse catalogs and data warehouses. Instead of writing custom code for every system, Dremio handles all the connection management. You can then query across all your sources in your application code using a single JDBC/ODBC connection, Apache Arrow Flight, or Dremio’s REST API.

Simplify Data Transformations with the Semantic Layer

Dremio’s semantic layer takes your architecture to the next level by allowing you to define reusable business logic. Predefine joins, aggregations, and transformations at the semantic layer, so your backend code doesn’t have to handle these operations. This not only reduces complexity but also ensures that all users and applications work with consistent, reliable data definitions.

Scalable and Cost-Effective

Dremio’s ability to auto-scale ensures that your infrastructure dynamically adjusts to query loads, so you never pay for more resources than you need. Whether your workload spikes or remains steady, Dremio’s intelligent scaling capabilities ensure cost-effective performance without compromising speed.

The Complete Solution

With Dremio, you gain a unified, scalable platform for your data analytics needs. From accelerating queries with reflections to simplifying data unification with its semantic layer, Dremio empowers you to build high-performing data applications without the complexities and costs of traditional architectures.

Conclusion: Transforming Data Applications with Dremio

The journey of building scalable and efficient data applications is full of challenges, from the limitations of transactional databases to the complexity of managing multiple systems and scaling APIs. Each step introduces new hurdles that can slow down development, increase costs, and add unnecessary complexity to your architecture.

Dremio offers a solution that not only simplifies but supercharges this process. By enabling a data lake architecture with open formats like Apache Parquet and Iceberg, Dremio reduces storage costs while maintaining flexibility. Its Reflections feature accelerates queries without requiring ETL, and advanced capabilities like live reflections and incremental reflections ensure that your data is always up to date with minimal compute overhead.

Dremio also shines in unifying data across multiple sources, allowing you to query seamlessly without managing dozens of backend connections. With its semantic layer, you can define business logic once and reuse it consistently across your applications, making your backend code leaner and your APIs more reliable. Combined with its ability to auto-scale, Dremio delivers cost-efficient performance for even the most demanding analytics workloads.

Whether you’re starting with transactional systems or already managing a complex web of data sources, Dremio provides the tools to simplify your architecture, accelerate performance, and reduce costs. By transforming how you build and manage data applications, Dremio empowers you to focus on delivering value to your users—without getting bogged down by infrastructure challenges.

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