The next evolution in observability: How architecture is following in BI’s footsteps

Modern observability systems are hitting the same wall business intelligence did a decade ago.

As data volumes explode, the traditional model — where a single product handles ingestion, storage, compute, and visualization — has become too costly, too rigid, and too closed.

The answer isn’t just more compression or better pricing.

It’s a fundamental architectural change.

Just as BI evolved from tightly coupled monoliths to decoupled, cloud-native data warehouses, we believe observability is now following the same path.  At the center of this shift is a new foundation for the modern observability stack: the Observability Warehouse.

The problem: monolithic observability is breaking down

For years, observability and SIEM platforms promised an all-in-one experience: collect, store, search, and visualize — all under one roof.

But that integration came at a cost.

• Tightly coupled design: Ingestion, storage, compute, and visualization are bound together, forcing you to scale (and pay) for everything at once.
  
• Black-box architectures: Limited flexibility, customization, or transparency into how data is managed.
  
• Vendor lock-in: Migrating away or integrating with new tools is painful.
  
• Rising costs: As data grows, so does the bill — often exponentially.

The result? Teams are forced into impossible trade-offs: drop data, shorten retention, or accept slow queries on “cold” tiers.

Sound familiar? That’s exactly the problem BI faced ten years ago.

The precedent: how BI solved this problem

Before Snowflake, BigQuery, and Databricks, BI platforms looked a lot like today’s observability stacks — tightly coupled and closed.

In the 2000s and early 2010s, vendors such as SAP BusinessObjects, and IBM Cognos delivered end-to-end BI ecosystems where data pipelines, storage, compute, and visualization were deeply integrated within proprietary platforms.

They worked well when organizations relied on a few centralized dashboards and well-defined data models. But as data sources multiplied and analytical workflows expanded, these systems couldn’t keep up. Each vendor controlled every part of the experience — from ingestion to visualization — leaving little room to customize, integrate, or evolve.

Then the market evolved. Specialized vendors emerged for each layer:

• ETL / Ingestion: Fivetran, Informatica, Talend
  
• Storage & Compute: Snowflake, Redshift, BigQuery, Databricks
  
• Visualization: Tableau, Looker, Power BI
  

The key breakthrough was decoupling each layer of the BI stack.

This separation gave customers the freedom to mix and match best-of-breed tools, integrate across ecosystems, and escape vendor lock-in.

The decoupled BI architecture transformed analytics — lowering costs, improving flexibility, and ushering in a new era of data innovation.

Observability is now following in BI’s footsteps

The same evolution is happening in observability.

For years, observability products bundled ingestion, storage, compute, and visualization into tightly coupled, all-in-one platforms. Popular tools such as Splunk, Datadog, or Dynatrace follow this model today.

They work well when data volumes are small — but as telemetry exploded, these systems became rigid, costly, and difficult to evolve.

Now, the ecosystem is decoupling, following the same path BI took years ago.

• Collection is decoupling. OpenTelemetry has become the universal standard for collecting metrics, traces, and logs.
  
• Routing is decoupling. Tools like Cribl Stream give teams precise control over what data goes where.
  
• Visualization is decoupling. Grafana and similar tools can visualize data from hundreds of backends.

What’s still missing is the data layer — a purpose-built system for storing, indexing, and querying observability data efficiently at scale.

That’s where Imply Lumi comes in.

Why this architecture fits what leading organizations are already doing

Most large enterprises have already started down this path — even if they haven’t called it an observability warehouse yet.

As data volumes grow and licensing costs rise, teams are offloading portions of their observability data from proprietary platforms into cloud object storage such as Amazon S3, Google Cloud Storage, or Azure Data Lake.

They do it for simple, practical reasons:

• Cost control: Cloud object storage like Amazon S3 is several times cheaper than traditional “hot” observability tiers, making it far more economical to retain larger volumes of data for longer periods.
  
• Retention: Cloud storage makes it feasible to keep months or years of logs for compliance and investigation.
  
• Flexibility: Once data is in the cloud, it can be accessed by multiple tools — not locked to one vendor.

In short, the decoupling is already happening organically.

The next step is giving this cloud-stored data the performance and interactivity engineers expect from live systems — and that’s where the Observability Warehouse comes in.

The limitations of today’s approach

Today, many organizations already export logs from their observability platforms into cloud object storage to control costs.

Those logs are usually written in Gzip or Parquet format and later queried with tools such as Athena, Presto, or Trino.

This approach helps with retention and cost, but it comes at a steep price in performance.

• Gzip offers strong compression, but it’s a sequential format. The data is stored as a single compressed block, so to answer even a small query, the entire file must be decompressed. That’s fine for archiving, but it’s painfully slow for interactive queries where engineers need to scan specific time ranges or fields.
  
• Parquet was designed for structured, columnar analytics — perfect for data warehouses where schemas change rarely and queries aggregate across well-defined columns. Logs, however, are semi-structured, high-cardinality, and constantly evolving (new fields appear daily). Parquet can’t efficiently handle this variability or time-based slicing, so every query still ends up scanning large chunks of data.
  
• Query engines like Athena or Trino can read these formats directly from S3, but without indexes or partition awareness, every query triggers a full scan of massive files. The result: queries that take minutes instead of milliseconds, driving up both latency and compute cost.

In short, these open formats are great for cheap storage, but they were never meant for real-time investigation or event exploration — exactly what observability demands.

Enter Lumi: the industry’s first observability warehouse

Imply Lumi is the missing data layer for modern observability and security stacks — just as Snowflake became the data layer for business intelligence.

It’s built on a simple belief: teams shouldn’t have to choose between cost, performance, and flexibility.

Lumi is the industry’s first Observability Warehouse — delivering the speed of hot data at the cost of cloud storage.

It combines the openness of cloud architecture with the power of indexed, event-native data, so organizations can keep all their logs searchable without compromise.

In traditional observability systems, data is divided into hot, warm, and cold tiers — fast but expensive, slower but cheaper, or archived and effectively unusable.

Lumi eliminates that divide. All your data lives in one unified layer that stays instantly searchable, whether it’s minutes old or years old.

Built for the cloud era, Lumi applies the same architectural principles that redefined BI:

• Open storage: Data lives in low-cost cloud object stores like S3 or GCS, not proprietary disks.
  
• Elastic compute: Query power scales dynamically with demand — no clusters to manage or overprovision.
  
• Ecosystem-friendly: Works seamlessly with existing ingestion pipelines (Cribl, Fluentd, etc.) and visualization tools (Splunk, Grafana, Power BI).

The result is a decoupled data layer that extends, rather than replaces, your current observability stack.

With Lumi, teams can:

• Keep using their existing dashboards, alerts, and forwarders.
  
• Retain far more data for far less cost.
  
• Run queries that once took minutes in milliseconds.

How Lumi works under the hood

Lumi introduces a new storage format purpose-built for observability and security logs — one that merges Gzip-level compression with built-in indexing to make every query fast and efficient.

Where formats like Gzip and Parquet were designed for storage efficiency, Lumi’s format is optimized for query efficiency without sacrificing size. It compresses data as tightly as Gzip while embedding lightweight indexes directly alongside it. That means queries can jump straight to the exact time range or field needed — no decompression of entire files, no full-table scans.

Better compression: Lumi’s event-native format achieves smaller storage footprints than Gzip or Parquet, even while embedding indexes for fast search.

Built-in indexing: Lumi embeds native time and field indexes within every data block, enabling targeted reads and avoiding full-file scans.

Event-native design: Built specifically for semi-structured, time-based logs, Lumi’s format adapts to evolving schemas and delivers fast, efficient event exploration.

In internal benchmarks, 1.2 GB of raw logs compressed to about 50 MB with Gzip, but only ~30 MB with Lumi — including indexes.

The result is not just smaller storage footprint, but orders-of-magnitude faster search performance.

Lumi’s architecture delivers 3–12× faster query performance than federated engines and legacy observability stores, across a range of query types — from simple searches to complex aggregations

By storing data directly in cloud object storage (S3, GCS, ADLS), Lumi preserves all the benefits of the cloud — durability, elastic scaling, and low cost — while delivering the interactive speed engineers expect from live systems.

This is what modern observability warehousing is all about: open storage, elastic compute, and indexed speed combined in one unified data layer.

The future: the era of the observability warehouse

Just as data warehouses redefined analytics, observability warehouses are now emerging to transform how organizations store and explore telemetry data.

The shift is only beginning — and Imply Lumi is leading it.

By decoupling storage and compute — and combining cloud economics with indexed speed — this architecture delivers what observability teams have always wanted:

• Open architecture: Data lives in standard cloud object stores, avoiding lock-in.
  
• Elastically scaling compute: Compute scales dynamically with demand.
  
• Full-fidelity retention: Keep all logs searchable — not just what fits within a license.

Imply Lumi is the first realization of this model. It completes the modern observability stack — working seamlessly with:

• Ingestion pipelines like Cribl Stream
  
• Collection frameworks like OpenTelemetry
  
• Visualization tools like Splunk and Grafana

The result is a fully decoupled, cloud-native architecture purpose-built for the next decade of observability scale — one where data is open, elastic, and instantly accessible.Book a demo and experience Imply Lumi in action.