Demystifying Real-Time Reporting

Real-time reporting is about making decisions based on data the moment it’s created. As businesses strive for faster insights, BI teams are often tasked with handling these requests, particularly in lean tech startups where developer resources are stretched thin. However, assigning these requests to BI teams often results in frustration and inefficiency.

To deliver effective solutions, it’s crucial to understand what real-time reporting is—and isn’t—and why not every request truly requires real-time capabilities. This post will help demystify real-time reporting and guide you in approaching these requests with clarity.

Real-Time Reporting vs. BI Reporting: An Analogy

Real-time reporting is like using an instant-read thermometer. It’s built for quick, precise measurements to help you make immediate decisions—such as knowing exactly when to flip your steak.

BI reporting, on the other hand, is more like following a cookbook. It’s designed for thoughtful planning and detailed instructions, helping you analyze past outcomes to improve future results.

The two serve very different purposes. While both are essential in their own contexts, expecting BI teams to handle real-time needs is like asking a cookbook to tell you when your steak is done—it’s not what it’s designed for.

What Real-Time Reporting Is

Real-time reporting focuses on delivering insights or triggering actions immediately as data is created. It falls into two primary categories:

1. Event-Driven Actions
   • These involve automated responses or alerts triggered by specific events in real time.
   • Example: A fraud detection system flags a suspicious transaction the moment it occurs, blocking it or alerting the fraud team instantly.
2. Real-Time Data Representation
   • This provides a live, up-to-the-second view of data with minimal delay, helping teams react quickly to changing conditions.
   • Example: A customer support dashboard shows live ticket queues, updating instantly as tickets are submitted or resolved.

Both scenarios rely on sub-second processing and focus on immediate data interaction, making them distinct from BI reporting, which emphasizes historical analysis and broader transformations.

What Real-Time Reporting Is NOT

Many requests from business units labeled as “real-time reporting” don’t actually require or align with real-time principles. Here’s what real-time reporting is not:

• Touching Historical Data
  • Real-time alerts and actions shouldn’t rely on large volumes of historical data. Complex dashboards spanning long periods of time are not great candidates for leveraging real-time. For data that is changed frequently, these dashboards have to be constantly rerun, and costs can add up. Historical data shouldn’t be needed for real-time reports, so the team ends up reprocessing lots of data that is not needed.
    • Analogy: You don’t need to know a steak’s historical temperature to determine if it’s done—only the current state matters.
• ETL Pipelines
  • ETL pipelines are designed for batch processing data and are therefore not real-time. This process of moving and transforming data is optimized for large amounts of data at infrequent intervals. Trying to increase the speed at which transformations occur can explode costs, especially if these pipelines are constantly reprocessing historical data that has not changed.
    • Analogy: It’s like trying to clean the entire house every time you take out the trash—you’re doing extra work for no real gain.
• Complex Queries
  • Queries that take minutes or hours to run are not real-time. Data is constantly changing, and by the time you are done analyzing the data, the state of things could have changed.
    • Analogy: Waiting five minutes for your instant-read thermometer means that your steak is well past well done.
• Scheduled Queries
  • Running queries every hour or even every 15 minutes might feel close to real-time, but it’s not. This method is not only reactive but inefficient because whatever system is being used to run the queries has no idea when the source system is being updated. For data that is updated infrequently, this method is even more costly.
    • Analogy: It’s like opening the grill every 2 minutes to check on the steak—if you’re looking, you ain’t cooking.

Real-time reporting requires systems designed for immediate interaction with data as it’s generated. Anything slower or dependent on periodic processing isn’t truly real-time—it’s just fast BI at best.

Responding to Real-Time Data Requests

When faced with a request for real-time reporting, it’s essential to evaluate whether it truly requires real-time capabilities. Implementing real-time solutions is often resource-intensive, especially in lean tech startups where developer bandwidth is limited. Here’s how to approach these requests effectively:

Determine the Necessity of Real-Time

• Does the decision-making process suffer from delays?
• Is there a significant penalty for acting on data a few minutes late?
• Will the team act immediately upon notification or will the system immediately correct the behavior?
• Often, requests for real-time reporting stem from convenience rather than critical need.

Addressing Application Bugs: A Common Use Case

Background:
Customer support teams often request real-time alerts, especially when dealing with bugs that impact user experience. While engineering teams may leave certain bugs in the backlog (due to workarounds, rare reproduction, or competing priorities), customer support needs proactive alerts to prevent users from encountering these bugs.

Challenge:
Support teams aim to identify and prevent bugs to ensure customer satisfaction and renewals. The need for real-time alerts depends on the bug’s behavior. If the bug only appears after significant delays, real-time reporting may not be needed. However, for bugs that occur immediately, swift action from engineering is essential.

Example:
While working on a virtual events platform, we faced a scenario where the system had no registrant limit, but it couldn’t scale indefinitely. The issue wasn’t the total number of registrants but the simultaneous attendance, which could strain system performance. To address this, we implemented a simple scheduled query to track registrant numbers, which provided a time-saving solution and helped prevent performance issues.

Delivering Real-Time Reports

For users who need real-time insights, the most efficient approach is to integrate visualizations directly into the application connected to the primary database. This method minimizes complexity while ensuring that users access the most current data instantly.

By embedding real-time dashboards or charts in the application, you eliminate the need for costly intermediary systems and reduce latency. However, this approach requires developer resources, which are often in high demand and may not be prioritized over other critical application features.

As a practical alternative, real-time data replication to a data warehouse is becoming increasingly efficient and cost-effective. While there is typically a slight delay as data moves from the source to the warehouse, this approach can meet the needs of many use cases without requiring instant data updates. It’s a viable solution when developer time is constrained.

For example, Microsoft offers mirrored databases within its ecosystem, which provide real-time data replication capabilities with minimal setup. Learn more about this technology here: Mirrored Databases Overview.

Avoid Overengineering with Real-Time Architectures

For small startups, adopting event-driven architectures like Kafka or Azure Event Hubs can seem appealing, but these tools often introduce unnecessary complexity and cost. While they excel in high-velocity environments, their benefits are often irrelevant for smaller teams.

Before implementing real-time systems, evaluate your actual needs. Event-driven architectures are designed for large enterprises managing real-time data across multiple systems—not for simple tasks like notifying a customer success rep when a new user signs up.

Consider starting with a simple scheduled query or batch processing. These approaches are cost-effective and can easily handle many use cases without the overhead of a real-time system.

Begin with a minimal setup and scale as your needs evolve. Overengineering early wastes resources and shifts focus from delivering value. For more insights, check out Estuary’s guide on Kafka alternatives.

Webhooks: A Simpler Approach to Real-Time Reporting

Webhooks are a way to receive real-time data from applications as events happen, without the need for a complex event-driven system. When a specific event occurs, the application can send an HTTP request (the webhook) to a designated URL. This setup can be used to trigger actions or send alerts, making it a useful tool for teams that need real-time reporting without investing in more elaborate solutions like Kafka or Azure Event Hubs. Webhooks are especially beneficial for startups or smaller teams looking to implement real-time capabilities quickly and without the overhead of managing large-scale infrastructure.

Making Real-Time Reporting Work for Your Team

Real-time reporting offers immense value but also comes with significant challenges. Whether through custom code or a full-fledged event-driven architecture, the right solution must balance immediate needs with long-term sustainability. Teams should carefully evaluate the complexity of their use cases, the availability of resources, and the potential costs involved before committing to a strategy.

Real-time solutions can enable timely decision-making, enhance customer experiences, and support business agility. However, they require careful planning, collaboration between business and technical teams, and ongoing maintenance to remain effective. By understanding the trade-offs and addressing the risks upfront, organizations can confidently implement real-time reporting systems that deliver actionable insights and drive meaningful outcomes.

Thanks for reading!