As organizations grow, they often struggle with inconsistent metrics across different departments. Each team may track the same key metrics using their own systems and definitions, leading to fragmented data and an incomplete view of the company's overall performance.
The root of this problem is the decentralized approach many organizations take, where individual departments hire their own data experts to manage metrics independently. Without a centralized oversight or standardization, it becomes challenging for the enterprise to gain a comprehensive, aligned understanding of its data.
These fragmented metrics pose a significant obstacle to effective, data-driven decision-making at the organizational level. To overcome this challenge, a centralized metrics store is needed to serve as a single source of truth for the company's key performance indicators.
What is a metrics store?
A metrics store is a central system for managing an organization's key performance indicators (KPIs) and metrics. It sits between data sources and analytics tools.
In simple terms, a metrics store is the place where all your metrics are managed. It serves as the single source of truth for all metrics in an organization.
The benefits of implementing a dedicated metrics store are substantial. It eliminates the problem of fragmented, inconsistent metrics across different departments, ensuring data coherency and alignment throughout the organization. Additionally, it reduces the waste of resources that often occurs when individual teams manage their own metric definitions and calculations independently.
If you realize a metrics store is exactly what you need, what factors you should consider when choosing a metrics store?
Key Considerations for choosing or building a metrics store
Real-time data ingestion
A metrics store must access all relevant data, often distributed across various systems like data warehouses, SaaS applications, and custom APIs. It should be capable of ingesting data from these diverse sources in real-time.
Efficient real-time processing
Metric calculations frequently involve filtering and joining data from multiple sources on the fly. While stream processing frameworks like Spark and Flink may be excessive, batch processing pipelines are often too slow. The ideal solution strikes a balance between these extremes.
Data consistency
Derived metrics should use consistent data versions. For example, if two metrics are based on the same source, they should use data from the same time point. This consistency requires low-latency data access, typically under one second.
Broad tool compatibility
While centralizing metrics is crucial, different teams often prefer specific BI tools. A versatile metrics store should integrate seamlessly with a wide range of analytics platforms to accommodate these preferences.
Robust stability and scalability
As a central system, a metrics store must maintain stability and adapt to fluctuating workloads for both data editing and access. It should efficiently handle concurrent access from multiple systems and scale as demand grows.
Streaming databases as metrics stores
Streaming databases, like RisingWave, are emerging technologies that enable SQL-based processing of streaming data. These systems typically employ incremental computation and maintain local data stores for rapid query resolution over the most recent results.
While not originally designed as metrics stores, streaming databases possess several attributes that make them well-suited for this purpose:
Efficient real-time processing
Optimized for ingesting and processing data as it arrives, streaming databases offer low-latency data handling. Their built-in connectors for various data sources and destinations position them as ideal intermediaries between data origins and BI systems.
SQL-based metric definition
Streaming databases utilize SQL for data processing, allowing metric definitions to be created with concise SQL queries. This aligns well with organizations where data scientists, who often prefer SQL, are responsible for defining and managing business metrics.
Support for complex calculations
These databases can perform sophisticated transformations, including windowing and top-N queries. This capability facilitates the definition of metrics involving intricate calculations and transformations within the streaming database environment.
Consistency and scalability
By processing data incrementally and maintaining up-to-date results, streaming databases ensure consistency across derived metrics. Their architecture also allows for scalable handling of concurrent queries and growing data volumes.
RisingWave as a metrics store
RisingWave offers specific features that enhance its suitability as a metrics store:
PostgreSQL-compatible SQL
RisingWave employs a PostgreSQL dialect of SQL, enabling seamless integration with numerous analytics tools through standard PostgreSQL drivers. This compatibility simplifies adoption and expands the range of tools that can interact with the metrics store.
Nested materialized views
RisingWave supports the creation of materialized views based on other materialized views (MV-on-MV). This capability allows for the definition of metrics derived from other metrics, offering several advantages:
- Reduced complexity: Metric logic can be broken down into more manageable, modular components.
- Improved consistency: All metrics, being defined as materialized views, access the same version of data.
- Enhanced maintainability: Changes to base metrics automatically propagate to derived metrics, ensuring data integrity.
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This article explored the concept of metrics stores, discussing when they're needed and the key factors to consider when choosing one. We examined the potential of streaming databases as metrics stores, with a focus on RisingWave's unique advantages. > >
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If you would like to learn more about the features that RisingWave offers, see the official documentation site. You can also sign up for RisingWave Cloud for free to test it out. If you need additional help with setting up this integration, join our active Slack community. > >
