Data technologies
Systems that collect, process and turn data into useful knowledge.
In data technologies, VeriSet A.Ş. develops big data processing, data infrastructure and data analysis systems. The goal is to turn scattered, raw data into knowledge that can be used for decisions.
What are data technologies?
Data technologies are all the tools, architectures and methods used to collect, store, process and analyse data and deliver it to the people who need it.
Big data describes datasets that are hard to process with traditional tools because of their volume, velocity and variety. AI and analytics work is built on top of these infrastructures.
- Area
- Data technologiesData infrastructure and analytics systems development
- Provider
- VeriSet A.Ş.Technology company based in Türkiye
- Contact
- [email protected]You can also write from the contact page
What does VeriSet do in this area?
Data infrastructure
Data pipelines and storage architectures that reliably collect, store and serve data from different sources.
Big data processing
Scalable solutions that process high-volume, high-velocity data with distributed systems.
Data analysis
Analyses that extract relationships, trends and deviations, and visualisations that make them easy to understand.
Data quality
Removing missing, inconsistent and duplicate data, and validation steps that keep data trustworthy.
Where are data technologies used?
The following are typical uses of this technology; every project is evaluated separately against its own data sources and goals.
- Reporting and dashboardsFollowing scattered data in one place, up to date and easy to read.
- Combining sourcesBringing data from different systems together in a common structure.
- Operational monitoringWatching system and process data live and raising alerts on deviations.
- Data preparation for AICleaning data and bringing it into a suitable form for model training.
- Real-time streamsProcessing continuously produced data instantly so it can be used.
Key concepts in data technologies
- Data pipeline
- An automated chain of steps that moves data from source to target and cleans and transforms it.
- ETL and ELT
- ETL transforms data before loading it into the target. ELT loads first and transforms inside the target system.
- Schema
- The structure that defines which fields data consists of and of what types.
- Data warehouse and data lake
- A warehouse stores organised, analysis-ready data; a lake stores raw data.
- Data lineage
- Information about where a piece of data came from and which transformations it passed through.
- Batch and stream processing
- Batch processing handles large groups of data at intervals; stream processing handles data as it arrives.
How do we choose the right approach?
The choice of data architecture depends on the speed you need and the structure of the data.
| Approach | When it fits | What to watch for |
|---|---|---|
| Batch processing | Daily or hourly reports are enough and data volume is large. | Results arrive with a delay. |
| Stream processing | You need instant alerts, live monitoring or fast decisions. | Needs more complex infrastructure and error handling. |
| Data warehouse | Reporting and analysis run on data with an organised schema. | The schema has to be designed in advance. |
| Data lake | Raw data in different formats is collected and what it will be used for is not yet certain. | Without governance it can turn into a messy pile of data. |
Common mistakes in data projects
- Collecting data without fixing it at the sourceErrors travel to later layers and get more expensive to fix.
- Not documenting schema and meaningThe same field is read differently by different people and analyses become inconsistent.
- Focusing only on volumeMore data is not the same as right data; quality and relevance come first.
- Not setting up monitoringWhen a pipeline breaks silently, nobody notices and wrong data keeps being used.
- Leaving access management for laterPermission and privacy rules for personal and sensitive data should be designed from the start.
Checklist before you start
- Which question am I trying to answer?
- Where is the data and who is responsible for it?
- How often does the data need to be updated?
- How will I measure data quality?
- Who should be able to access which data?
- If the pipeline breaks, how will I find out?
How do we run data projects?
Research
We clarify which question will be answered, where the data lives and how reliable it is.
Prototype
We build an end-to-end flow on a small slice of data so the result becomes visible early.
Build
We develop the data pipelines, validations and analysis layer in a testable way.
Scale
As data volume grows, we scale the infrastructure in a distributed and observable way.
Frequently asked questions
What is big data?
Big data describes datasets that are hard to process with traditional tools. It is usually defined by volume (large amounts), velocity (fast, continuous production) and variety (different formats and sources).
What is the difference between a data warehouse and a data lake?
A data warehouse holds data organised by a predefined schema and ready for analysis. A data lake stores raw data without imposing a format up front. Depending on the need, both can be used together.
What is a data pipeline?
A data pipeline is a chain of steps that takes data from a source, cleans and transforms it and loads it into a target system, automatically and repeatably.
Why does data quality matter?
Analyses and AI models are only as reliable as the data that feeds them. Missing or inconsistent data leads to wrong results, so quality checks should be built in as part of the data infrastructure.
How can I contact VeriSet about data projects?
You can email [email protected] with a short description of your current data sources and the output you want.
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Modern web applications, services and high-performance digital platforms.
Open pageInfrastructure
Cloud systems, APIs, distributed architectures and reliable software infrastructure.
Open pageR&D
We research and experiment with new technologies and build solutions that can be used in the future.
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