Giggabytes

Giggabytes OyData engineeringSemantic modelsPower BI

Data engineering for reporting that holds up.

Giggabytes designs lakehouse platforms, semantic models, and the calendar, customer and promotion logic that keep weekly and monthly KPIs consistent.

Data engineering, BI development and solution architecture, with 18 years of enterprise data delivery experience.
Built in your own platform, documented, yours to keep.

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Databricks PartnerConsulting & SI partner, member of the Databricks Partner ProgramOur Databricks work

System records from source to decision

Illustrative

Select a stage to see what happens there.

Net sales · period P03

ViewShortcutShared calendar
Weekly roll-up4.82M5.11M
Monthly view5.11M5.11M
Gap5.7%0.0%

Both views now read one calendar.

Illustrative pipeline. Records from ERP, CRM, POS, event, file and API sources are ingested through one ingestion point into a raw bronze layer, validated in silver where failed records are quarantined, modelled in gold, and served through a semantic model whose date dimension carries the calendar. In the example report, a weekly roll-up of 4.82 million and a monthly view of 5.11 million disagree by 5.7 percent; once both read the same date dimension and calendar, both report 5.11 million.
  • ERP · CRM · POS · events · files · APIs. Each system has its own keys, grain and clock. None of them agree on what a week is.
  • Fabric pipelines · Data Factory · Auto Loader. Incremental, idempotent loads. Every record keeps its source, load time and lineage.
  • Delta Lake · OneLake. Raw history kept exactly as delivered: append-only, replayable, auditable.
  • dbt · PySpark · SQL. Deduplication, type and key enforcement, quality rules. Failed records are quarantined, not dropped.
  • Kimball · Data Vault 2.0 · Unity Catalog. Conformed dimensions and facts at a declared grain, governed in one catalogue.
  • Power BI semantic models. One date dimension, one calendar, one definition per measure, shared by every report.
  • Power BI. Weekly and monthly views agree because they are built on the same model.

03 The problem

The dashboard is rarely where inconsistent reporting begins.

Weekly and monthly reports read the same transactions. They disagree because each decides differently which days belong to which period. That is a modelling decision made upstream, long before anyone opens a report.

  1. Same factsone sales table
  2. Different calendar logicweek rule vs month rule
  3. Different KPItwo “correct” numbers

One trading week across a month boundary

Illustrative
Weekly logic
Whole week → Month A
Calendar month
Month AMonth B
Friday to Sunday, the three heaviest trading days, land in different months depending on the rule. Neither report is wrong. They are answering different questions with the same name.

04 What Giggabytes builds

Platforms, models, and the logic between them.

Reporting breaks at the joins: between ingestion and modelling, between the model and the measure, between the calendar and the KPI. Giggabytes builds across all three.

  1. 01API
  2. 02Raw
  3. 03Quality
  4. 04Model
  5. 05Semantic
  6. 06KPI
  7. 07Report

Select a capability to trace it through the pipeline.

01

Data platforms

  1. Source
  2. Ingest
  3. Lakehouse
  4. Governed data

Lakehouse platforms with incremental ingestion, medallion layers and governance designed in from the first table, not retrofitted after the first audit.

Azure Databricks · Microsoft Fabric · Snowflake · Delta Lake · Unity Catalog · Terraform

02

Semantic analytics

  1. Gold
  2. Model
  3. Measures
  4. Power BI

Star schemas with a declared grain, conformed dimensions and one definition per measure. Power BI semantic models analysts can extend without breaking.

Kimball · Power BI semantic models

03

KPI integrity

  1. Fact
  2. Date
  3. Calendar
  4. Business logic
  5. Consistent KPI

The calendar, customer and promotion logic that decides which number is right. Written once, in the model, and tested like code.

dbt tests · SQL · date dimension design

05 How it works

From the first call to a handover.

Everything is built in your own platform, documented as it goes and handed over at the end. Nothing depends on us to keep running.

  1. A 30-minute call

    Free

    Your platform, your reports and the numbers that do not agree.

  2. Assessment

    1–2 weeks

    On your data, we measure where reports disagree and why, and propose the fix at a fixed price.

  3. Build

    Agreed up front

    In your own workspace, in small steps you can review, with checks that run every time.

  4. Handover

    Final week

    Code, documentation and a walkthrough for the people who will run it. Yours to keep, with no lock-in.

07 Project experience

Experience across leading organisations.

Giggabytes has delivered data engineering and BI consulting projects for these organisations.

  • Allianz
  • Euroclear
  • Nokia
  • Sodexo
  • Sympa
  • THG
  • Vattenfall

Logos are trademarks of their respective owners and are shown solely to identify organisations for which Giggabytes has delivered consulting work. Their use does not imply endorsement by these organisations.

Project experience

08 Who builds it

Senior delivery, from Lahti, Finland.

Senior work from the first call to the handover, with no hand-offs along the way.

Experience
18 years of enterprise data delivery, since 2008
Disciplines
Data engineering, BI development and solution architecture
Industries
Retail and grocery, financial services, energy, enterprise services
Languages
English and Finnish
Partner
Databricks Partner, Consulting & SI
Company
Giggabytes Oy, Business ID 3147928-8

→ Decision

Start with a free 30-minute call.

Email us about the numbers that do not match, and we will set up a call. Lakehouse builds, semantic model reviews, KPI reconciliation and discount price checks.