Data Pipelines You Can Check, Rerun and Trust
We design and build data pipelines that ingest, clean, test and model your data, then deliver it to Salesforce Data Cloud, warehouses and reporting tools in a usable shape.
Segments, dashboards and AI features all depend on the same thing: data that arrives on time, in a known shape, with problems caught before anyone acts on them. When that foundation is weak, the symptoms show up elsewhere. Marketing audiences that change size for no clear reason. Revenue figures that differ between two reports. Identity resolution merging people who should never have been matched.
GeekMidst builds the pipelines underneath. We ingest data from your source systems, transform and test it, model it in a warehouse or lake, and deliver it to the places it is used, including Salesforce Data Cloud (now branded Data 360) and your reporting tools.
Problems we are usually asked to solve
- Source data scattered across CRM, ecommerce, billing, support and spreadsheet exports, with no single, documented flow between them.
- Manual extracts that one person runs every Monday, which stop when that person is away.
- Duplicate or malformed records reaching Data Cloud and distorting unified profiles.
- Reporting tables that nobody can trace back to a source, so no one trusts the numbers.
- A planned Data Cloud implementation where the source data is not yet ready to be mapped.
What we build
Ingestion
Batch and incremental loads from databases, APIs, files and SaaS applications, with the pattern chosen per source based on volume and freshness needs.
→Quality checks
Automated tests for keys, nulls, formats, volumes and freshness, run on every load so bad data is stopped or flagged before it spreads.
→Modelling
Warehouse or lake models at a clear grain, documented field by field, that reporting and activation can rely on.
→Feeding Data Cloud
Clean, well-keyed sources prepared for mapping to the Customer 360 Data Model and for identity resolution.
→Reporting feeds
Tables and views shaped for your BI and marketing analytics tools, with definitions agreed with the people who use them.
→How we structure a pipeline
We separate each pipeline into layers so problems can be found and fixed at the right point. Raw data is landed as it arrived, so loads can be replayed. Staged data is typed, renamed and deduplicated. Modelled data is shaped around business entities such as customers, orders and subscriptions. Delivery outputs are the specific tables or feeds that Data Cloud, a dashboard or a downstream system consumes.
Quality checks run between layers. A check either blocks the load, for example when a primary key is duplicated, or raises a warning that someone reviews, for example when daily volume drops sharply. The example below shows the kind of test we write. Exact syntax varies by warehouse engine.
-- Assumes a staging table stg_orders with order_id and loaded_at columns.
-- 1. Primary key must be unique: any rows returned should fail the load.
SELECT order_id, COUNT(*) AS row_count
FROM stg_orders
GROUP BY order_id
HAVING COUNT(*) > 1;
-- 2. Required fields must be populated.
SELECT COUNT(*) AS missing_customer
FROM stg_orders
WHERE customer_id IS NULL;
-- 3. Freshness: when did the latest load arrive?
SELECT MAX(loaded_at) AS latest_load
FROM stg_orders;How an engagement runs
- 01
Source inventory
We list every source, its owner, grain, identifiers, volume and how often it changes, and record which business questions each one must answer.
- 02
Target design
We agree the target model, the delivery outputs and the quality rules, and confirm which warehouse, lake or integration tools fit your existing stack.
- 03
Build the first pipeline end to end
One source is taken from raw to delivery, with checks and monitoring, to prove the pattern before scaling it.
- 04
Extend source by source
Remaining sources are added in priority order, each with its own tests, documentation and reconciliation against the source system.
- 05
Operate and hand over
Schedules, alerts and runbooks are put in place, and your team is walked through how to diagnose and rerun a failed load.
Deliverables and who uses them
| Deliverable | What it contains | Main audience |
|---|---|---|
| Source inventory | Owners, grain, keys, volumes and refresh frequency per source | Data owners, architects |
| Data dictionary | Field definitions, types, allowed values and lineage | Analysts, admins, marketers |
| Pipeline code | Ingestion, transformation and test code in your repository | Data engineers |
| Quality rules | Blocking and warning checks with agreed thresholds | Data owners, support |
| Runbook | Schedules, alerts, rerun steps and common failure causes | Operations and support |
Questions we hear
Which warehouse or pipeline tools do you use?
Do we need a warehouse if we have Data Cloud?
Can you fix data quality at the source?
How do you handle personal data?
Can you work alongside our internal data team?
Not sure your data is ready?
Share your sources and what the data needs to power. We will help you find the weak points and plan a first pipeline worth building.
