Analytics

/lʊk-ɛm-ɛl/

n. “The language that teaches Looker how to see your data.”

LookML is a modeling language used in Looker to define relationships, metrics, and data transformations within a data warehouse. It allows analysts and developers to create reusable, structured definitions of datasets so that business users can explore data safely and consistently without writing raw SQL queries.

Unlike traditional SQL, LookML is declarative rather than procedural. You describe the structure and relationships of your data — tables, joins, dimensions, measures, and derived fields — and Looker generates the necessary queries behind the scenes. This separation ensures consistency, reduces duplication, and enforces business logic centrally.

Key concepts in LookML include:

• Views: Define a single table or dataset and its fields (dimensions and measures).
• Explores: Configure how users navigate and join data from multiple views.
• Dimensions: Attributes or columns users can query, such as “customer_name” or “order_date.”
• Measures: Aggregations like COUNT, SUM, or AVG, defined once and reused throughout analyses.

Here’s a simple LookML snippet defining a view with a measure and a dimension:

view: users {
  sql_table_name: public.users ;;

dimension: username {
sql: ${TABLE}.username ;;
}

measure: total_users {
type: count
sql: ${TABLE}.id ;;
}
}

In this example, the view users represents the database table public.users. It defines a dimension called username and a measure called total_users, which counts the number of user records. Analysts can now explore and visualize these fields without writing SQL manually.

LookML promotes centralized governance, reducing errors and inconsistencies in reporting. By abstracting SQL into reusable models, organizations can ensure that all users are working with the same definitions of metrics and dimensions, which is critical for reliable business intelligence.

In essence, LookML is a bridge between raw data and meaningful insights — it teaches Looker how to understand, organize, and present data so teams can focus on analysis rather than query mechanics.

/ˈiː-tiː-ɛl/

n. “Move it. Clean it. Make it useful.”

ETL, short for Extract, Transform, Load, is a data integration pattern used to move information from one or more source systems into a destination system where it can be analyzed, reported on, or stored long-term. It is the quiet machinery behind dashboards, analytics platforms, and decision-making pipelines that pretend data simply “shows up.”

The first step, extract, is about collection. Data is pulled from its original sources, which might include databases, APIs, flat files, logs, or third-party services. These sources are rarely uniform. Formats differ. Schemas drift. Timestamps disagree. Extraction is less about elegance and more about endurance.

The second step, transform, is where reality is negotiated. Raw data is cleaned, normalized, filtered, enriched, and reshaped into something coherent. Duplicates are removed. Types are corrected. Units are converted. Business rules are applied. This is the step where assumptions become code — and where most bugs hide.

The final step, load, places the transformed data into its destination. This is often a data warehouse, analytics engine, or reporting system, such as BigQuery. The destination is optimized for reading and querying, not for the messy business of data collection.

Traditional ETL emerged in an era when storage was expensive and compute was scarce. Data was transformed before loading to minimize cost and maximize query performance. This design made sense when every byte mattered and batch jobs ran overnight like clockwork.

Modern systems sometimes invert the pattern into ELT, loading raw data first and transforming it later using scalable compute. Despite this shift, ETL remains a useful mental model — a way to reason about how data flows, where it changes shape, and where responsibility lies.

ETL pipelines often operate on schedules or triggers. Some run hourly, some daily, others in near real time. Failures are inevitable: a source goes offline, a schema changes, or malformed data sneaks through. Robust ETL systems are designed not just to process data, but to fail visibly and recover gracefully.

Consider a practical example. An organization collects user events from a website, sales data from a CRM, and billing records from a payment provider. Each system speaks a different dialect. An ETL pipeline extracts this data, transforms it into a shared structure, and loads it into a central warehouse where analysts can finally ask questions that span all three.

Without ETL, data remains siloed. Reports disagree. Metrics cannot be trusted. Decisions are made based on partial truths. With ETL, data becomes comparable, queryable, and accountable — not perfect, but usable.

ETL does not guarantee insight. It does not choose the right questions or prevent bad interpretations. What it does is establish a repeatable path from chaos to structure, turning raw exhaust into something worth examining.

In data systems, ETL is not glamorous. It is plumbing. And like all good plumbing, it is only noticed when it fails — or when it was never built at all.

/ˈlʊk-ər/

n. “See the numbers, tell the story.”

Looker is a business intelligence (BI) and data analytics platform designed to turn raw data into actionable insights. It connects to databases, warehouses, and data lakes — for example, BigQuery, Cloud Storage, or SQL Server — allowing users to explore, visualize, and share data across organizations.

At its core, Looker abstracts SQL into a modeling language called LookML, which defines relationships, metrics, and dimensions in a reusable way. This lets analysts and business users query complex datasets without writing raw SQL, reducing errors and improving consistency across reports.

Looker is more than dashboards. It enables embedded analytics, scheduled reports, and data-driven workflows. For instance, a marketing team might pull campaign performance metrics and automatically trigger follow-up actions, while finance teams can produce audit-ready reports sourced directly from their database. The key advantage is centralizing the "single source of truth," so everyone in the organization makes decisions based on the same definitions.

Security and governance are built-in. User roles, access controls, and row-level security ensure that sensitive data is protected, while still providing broad analytics access for teams who need it. This balance is critical in enterprises managing compliance requirements like GDPR or CCPA.

Looker integrates with modern analytics stacks, including tools for ETL, machine learning pipelines, and visualization. It solves the common problem of fragmented data: instead of multiple spreadsheets or ad-hoc queries floating around, Looker provides a structured, governed, and interactive environment.

Consider a scenario where a sales team wants to analyze revenue by region. With Looker, they can slice and dice the data, drill into customer segments, or visualize trends over time without waiting on engineering. The same data model can serve marketing, finance, and product teams simultaneously — avoiding inconsistencies and manual reconciliation.

In short, Looker is a platform for anyone who wants to turn complex data into insight, whether through visualizations, dashboards, or integrated workflows. It combines analytical power, governance, and usability into a single tool, making it a cornerstone of modern data-driven organizations.