BIMachine - Analytics and IA Platform
BIMachine

Integrated Data Lakehouse

Native analytical lakehouse: scalable storage, governed model, and BI on one platform

Data Lakehouse BIMachine

BIMachine Data Lakehouse combines lake flexibility (unlimited history, Parquet, any source) with warehouse discipline (governed metrics, trusted joins, BI performance). Dashboards in seconds — without parallel engineering projects.

Totvs
Salesforce
Vtex
SAP
Oracle
Excel

Unified Data Lakehouse

100B+ rows | semantic layer

Why you can't
analyze complete business history

Databases die with volume

Relational databases weren't meant for analyzing billions of rows.

YEAR 1

2 seconds

500 thousand orders

YEAR 2

15 seconds

2.5 million orders

YEAR 3

90 seconds

8 million orders - Crashes!

Traditional solution: "Let's archive old data. Delete orders > 3 years."

You sacrifice history for performance. Decisions are short-sighted.

Each system has its database

Data in different places, different formats, they don't talk.

Totvs

SQL Server

Salesforce

Cloud

Vtex

Cloud

Budget

Excel

Supplier

CSV

Real cost: Complex analyses take days/weeks. Most give up. Decisions are made without deep analysis.

History is expensive to maintain

Companies cut history to save storage.

SQL SERVER ON-PREMISE (10 TB)

Hardware$ 20k/year
Licenses$ 30k/year
Backup$ 10k/year
Total$ 60k/year

BIMACHINE DATA LAKEHOUSE (10 TB)

Cloud Storage$ 8k/year
Processing$ 4k/year
BackupIncluded
Total$ 12k/year
SAVINGS: $ 48K/YEAR (80% LESS)

Unlimited Storage + Analytical Performance
+ Optimized Cost

Columnar Storage

Data in Apache Parquet format. To sum revenue, it reads only the "value" column, ignores the rest.

90s → 3s (30x faster)

Governed Semantic Layer

Standardized metrics and dimensions on the lakehouse. Same definition of revenue, margin, and cohort across every dashboard — no parallel spreadsheets.

Lake + integrated dimensional model

Unlimited History

Data Lakehouse scales horizontally. Smart partitioning by date.

10 years with no performance impact

Optimized Cost

Cloud storage with up to 80% lower cost than traditional infrastructure.

Linear scale, predictable cost

Smart Compression

Data compressed automatically. Text 10:1, Numbers 5:1, Dates 8:1.

50 GB → 8 GB (6x smaller)

Smart Cache

Frequent results cached. Monthly revenue: 1h cache. Pipeline: 15min cache.

<500ms even with billions of rows

How it works under the hood

1

Ingestion

Connectors extract data from source systems

  • ΓÇóStructured (SQL, APIs)
  • ΓÇóSemi-structured (JSON, XML)
  • ΓÇóUnstructured (CSV, logs)
2

Storage

S3-compatible Object Storage, Parquet format

/company_id/
/orders/
/year=2024/
data.parquet
3

Lakehouse Engine

Analytical engine on Parquet + semantic layer for BI

  • ΓÇóPredicate pushdown
  • ΓÇóColumn pruning
  • ΓÇóParallel processing
4

Cache

Frequent results in memory

  • ΓÇóRevenue: 1h cache
  • ΓÇóPipeline: 15min cache
  • ΓÇóHistory: 1 day cache

Real world use cases

Deep History Analysis

CFO wants to compare 2024 vs 2019 performance (5 years ago)

WITHOUT DATA LAKEHOUSE:

2019 data was archived/deleted. Analysis impossible.

WITH DATA LAKEHOUSE:

2019 data is there. Query returns in 3 seconds.

INSIGHT:

2024 margin (18%) equal to 2019. But in 2020-2023 reached 22%. Why did it drop?

Cohort Analysis (Clients)

Manager wants to analyze retention by first purchase cohort

WITHOUT DATA LAKEHOUSE:

Analysis limited to last 2-3 years. Old cohorts deleted.

WITH DATA LAKEHOUSE:

Complete history since 2018. Full cohort analysis.

INSIGHT:

2018-2019 clients have 40% higher retention than 2023-2024. Why?

Multi-Year Seasonality

Director wants to plan inventory for December/2025

WITHOUT DATA LAKEHOUSE:

Only 2022-2024 data (3 years). Small sample.

WITH DATA LAKEHOUSE:

5 years of history. Identifies +47% pattern in December.

INSIGHT:

Data-driven decision: Increase inventory 50% for Dec/2025.

Behavior Change

CEO wants to know: Why did margin drop from 22% to 18%?

WITHOUT DATA LAKEHOUSE:

Superficial analysis: Costs rose, prices didn't keep up.

WITH DATA LAKEHOUSE:

Deep drill-down: Margin by product, region, client 2021 vs 2024.

INSIGHT:

Product A: margin dropped from 28% to 19%. Supplier X increased 45%.

Data organized, auditable, secure

Data Catalog

Automatic inventory. Smart search by any field or metric.

Data Lineage

Source to destination tracking. Know exactly where each number came from.

Granular Control

Automatic filters per user. Region, period, access profile.

Auditing

Complete log for GDPR, SOX, ISO 27001. Who accessed, when, which query.

From traditional database to
Operational Data Lakehouse in 1 week

1

ASSESSMENT

1 DAY

System survey, data volume, priority tables, requirements

2

HISTORICAL LOAD

2-4 DAYS

Full migration, transformation, integrity validation

3

VALIDATION

1 DAY

Row counting, aggregated totals, performance testing

4

GO-LIVE

1 DAY

Dashboard transition, production connectors, rollback available

Keep full historywithout sacrificing performance or cost

Corporate lakehouse with unlimited history and governed metrics — ready for billions of rows

Strategic AI Diagnosis

  • checkMapping of current analytical maturity
  • checkIdentification of concrete opportunities for AI use in your area
  • checkPrioritization of initiatives based on impact and feasibility
  • checkGuidance on structure, tools and necessary integrations

Free Trial 14 Days

  • checkFull platform + active Ada.IA
  • checkIntegration with your data sources
  • checkBIMStore Templates
  • checkDedicated technical support in English
  • checkNo card, no commitment

Speak with one of our specialists

  • checkQuick 30-minute schedule
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  • checkClear your doubts about the platform