Big Data Science
@bdscience
📊 How to avoid data chaos? Ways to ensure consistency of metrics in a warehouse
If you work with analytics, you have probably encountered a situation where the same metric is calculated differently in different departments. This leads to confusion, reduces trust in the data, and slows down decision-making. The new article discusses the key reasons for this problem and two effective solutions.
🤔 Why do metrics diverge?
The reason lies in the spontaneous growth of analytics:
🔹 One analyst writes a SQL query to calculate the metric.
🔹 Then other teams create their own versions based on this query, making minor changes.
🔹 Over time, discrepancies arise, and the analytics team spends more and more time sorting out inconsistencies.
To avoid this situation, it is worth implementing uniform metric management standards.
🛠 Two approaches to ensure consistency
✅Semantic Layer
This is an intermediate layer between data and analytics tools, where metrics are defined centrally. They are stored in static files (e.g. YAML) and used to automatically generate SQL queries.
💡 Pros:
✔️ Flexibility: adapts to different queries without pre-creating tables.
✔️ Transparency: uniform definitions are available to all teams.
✔️ Relevance: data is updated in real time.
⚠️ Cons:
❌ Requires investment in infrastructure and optimization.
❌ May increase the load on calculations (but this can be solved by caching).
📌 Example of a tool: Cube.js is one of the few mature open-source solutions.
✅Pre-Aggregated Tables
Here, tables with pre-calculated metrics and fixed dimensions are created in advance.
💡 Pros:
✔️ Simple implementation, convenient for small projects.
✔️ Saving computing resources.
✔️ Full control over calculations.
⚠️ Cons:
❌ Difficult to maintain as the number of users increases.
❌ Discrepancies are possible if metrics are defined in different tables.
🤔 Which method to choose?
The optimal approach is hybrid use:
🔹 Implement a semantic layer for scalability.
🔹 Use pre-aggregated tables for critical metrics where minimal computation cost is important.
🔎More details here
If you work with analytics, you have probably encountered a situation where the same metric is calculated differently in different departments. This leads to confusion, reduces trust in the data, and slows down decision-making. The new article discusses the key reasons for this problem and two effective solutions.
🤔 Why do metrics diverge?
The reason lies in the spontaneous growth of analytics:
🔹 One analyst writes a SQL query to calculate the metric.
🔹 Then other teams create their own versions based on this query, making minor changes.
🔹 Over time, discrepancies arise, and the analytics team spends more and more time sorting out inconsistencies.
To avoid this situation, it is worth implementing uniform metric management standards.
🛠 Two approaches to ensure consistency
✅Semantic Layer
This is an intermediate layer between data and analytics tools, where metrics are defined centrally. They are stored in static files (e.g. YAML) and used to automatically generate SQL queries.
💡 Pros:
✔️ Flexibility: adapts to different queries without pre-creating tables.
✔️ Transparency: uniform definitions are available to all teams.
✔️ Relevance: data is updated in real time.
⚠️ Cons:
❌ Requires investment in infrastructure and optimization.
❌ May increase the load on calculations (but this can be solved by caching).
📌 Example of a tool: Cube.js is one of the few mature open-source solutions.
✅Pre-Aggregated Tables
Here, tables with pre-calculated metrics and fixed dimensions are created in advance.
💡 Pros:
✔️ Simple implementation, convenient for small projects.
✔️ Saving computing resources.
✔️ Full control over calculations.
⚠️ Cons:
❌ Difficult to maintain as the number of users increases.
❌ Discrepancies are possible if metrics are defined in different tables.
🤔 Which method to choose?
The optimal approach is hybrid use:
🔹 Implement a semantic layer for scalability.
🔹 Use pre-aggregated tables for critical metrics where minimal computation cost is important.
🔎More details here
Start Data Engineering
How to ensure consistent metrics in your warehouse – Start Data Engineering
If you’ve worked on a data team, you’ve likely encountered situations where multiple teams define metrics in slightly different ways, leaving you to untangle why discrepancies exist. The root cause of these metric deviations often stems from rapid data utilization without prioritizing long-term maintainability. Imagine this common scenario: a company hires its first data professional, who writes an ad-hoc SQL query to compute a metric. Over time, multiple teams build their own datasets using this query—each tweaking the metric definition slightly. As the number of downstream consumers grows, so does the volume of ad-hoc requests to the data team to investigate inconsistencies. Before long, the team spends most of its time firefighting data bugs and reconciling metric definitions instead of delivering new insights. This cycle erodes trust, stifles career growth, and lowers team morale. This post explores two options to reduce ad-hoc data issues and empower consumers to derive insights independently.
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