# Data technologies

> 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.

## 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 dashboards:** Following scattered data in one place, up to date and easy to read.
- **Combining sources:** Bringing data from different systems together in a common structure.
- **Operational monitoring:** Watching system and process data live and raising alerts on deviations.
- **Data preparation for AI:** Cleaning data and bringing it into a suitable form for model training.
- **Real-time streams:** Processing 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.

- **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 source:** Errors travel to later layers and get more expensive to fix.
- **Not documenting schema and meaning:** The same field is read differently by different people and analyses become inconsistent.
- **Focusing only on volume:** More data is not the same as right data; quality and relevance come first.
- **Not setting up monitoring:** When a pipeline breaks silently, nobody notices and wrong data keeps being used.
- **Leaving access management for later:** Permission 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?

1. **Research:** We clarify which question will be answered, where the data lives and how reliable it is.
2. **Prototype:** We build an end-to-end flow on a small slice of data so the result becomes visible early.
3. **Build:** We develop the data pipelines, validations and analysis layer in a testable way.
4. **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 destek@veriset.org with a short description of your current data sources and the output you want.

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https://veriset.org/en/data-technologies/ · destek@veriset.org