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DevPals — Header Component
DevPals — Data Intelligence
Data Intelligence

Scale on data
you trust, not
spreadsheets

Your supplier data is inconsistent. Pricing lags. Your team wastes time on manual fixes. DevPals builds automated data pipelines that validate and clean your data in real-time — so you scale inventory, pricing decisions, and revenue with confidence.

Book a 30-min call
40k+
Data points per customer
Real-time
Pipeline validation
100%
Senior delivery
Pipeline validated
Just now · 0 errors
Live Pipeline
Supplier Data Quality
Dashboard
Data completeness 97%
Pricing accuracy 94%
Duplicate records 2%
Validated
1.2M records synced
📊
Manual fixes saved
−74%
DevPals — Data Intelligence
First- & Third-Party Data
We use Big Data analysis based on
first- and third-party data

More than 40,000 data points on your customers — ranging from recent purchases to loans — to unleash insights on segments and achieve:

01
Targeted Acquisition
Customized look-alike profiles for top-performing traffic that converts.
02
Better Conversion
Profiling-based custom funnels that move buyers through faster.
03
Personalised Retention
Individualized messaging and product experience that keeps customers coming back.
04
Sales Automation
When it comes to messaging, sales cannot connect the dots — data does it for them.
40,000+
DevPals stands at a data fork in the road and acknowledges the business need to enhance client interactions and business processes. We understand how big data profiling and analysis can aid in data quality control — simplifying the process of exploring and rebuilding complex data lakes.
Data Quality
DevPals Big Data Profiling Practices

Rigorous profiling techniques applied at every stage — so your data is trustworthy before it ever reaches a decision.

01.
Distinct count and percent
Identifies natural keys — distinct values in each column that aid in the processing of inserts and updates.
02.
Percent of zero / blank values
Identifies data that is missing or unknown. Assists ETL architects in establishing appropriate default values.
03.
Minimum / maximum string length
To improve performance, you can set column widths to be just wide enough for the data — no waste, no truncation.
01.
Key integrity
Ensures keys are always present using zero/blank/null analysis. Identifies orphan keys — problematic for ETL and future analysis.
02.
Cardinality
Examines one-to-one, one-to-many, and many-to-many relationships between related data sets. Assists BI tools in correctly performing inner or outer joins.
03.
Distributions
Checks that data fields are properly formatted. Data fields used for outbound communications — such as emails and phone numbers — are verified and well-formed.
Architecture Decisions
What is our process for choosing a database?

When it comes to selecting a database, we take into account both Relational Database Management Systems (RDBMS) and NoSQL databases, in order to gain a comprehensive understanding of each ecosystem. We evaluate different systems based on factors such as data type, storage, structure, and intended use — with the goal of meeting the specific needs of our clients.

RDBMS
Required consistency, latency conditions, and transaction speed — including real-time querying mechanisms.
NoSQL
Data type, storage, structure, and intended use — evaluated for scale and flexibility requirements.
Decision factors
Data shape, query patterns, scale requirements, and team expertise all feed into our architecture recommendations.
DevPals — Data Intelligence CTA
Let's talk

Gain a competitive edge. Say goodbye to costly errors.

Gain a competitive edge with DevPals' Data Intelligence and say goodbye to costly errors in databases.

Book a free 30-min call

No commitment. No sales team.
Reviewed by a senior engineer.