Article · Data
Big Data for Small and Mid-Size Companies: When You Need It, and When You Don’t
Most small and mid-size companies do not have a big data problem; they have a scattered data problem. Their sales, operations and finance data usually fits comfortably in one well-designed relational database such as PostgreSQL, whose documentation lists a limit of 32 TB for a single table. Big data tools such as data warehouses, streaming pipelines and data lakes start to pay off when data arrives faster or in larger volumes than one database can query quickly, or when many systems must be combined for analysis.
By Web Your Vyavsay team · Published
Drafted with AI assistance. Claims are checked against the sources listed below and reviewed by our team.
The real problem: scattered data
The symptoms usually look like this: the same customer exists in three spreadsheets, sales and accounts never agree on a number, and every report takes someone a day to assemble. None of that needs big data tools. It needs the data in one place, with one definition of each number.
When one database is enough
- Your data is measured in gigabytes, not terabytes.
- You report on orders, customers, stock, payments and staff.
- Dashboards refreshed every few minutes or every hour are fast enough.
- One application, or a handful, creates most of the data.
When big data tools pay off
- You collect high-volume events, such as clicks, sensor readings or transactions, every second.
- Analysts need to combine data from many systems, such as the website, CRM, ERP and marketing tools.
- Heavy reports slow down the application your staff and customers use.
- You train models on years of detailed history.
A sensible path
- 1
Put operational data in one database
Replace scattered spreadsheets with one system and one definition of each record.
- 2
Add reporting views and dashboards
Build the reports people actually use on top of that database.
- 3
Separate reporting from the live system
When reports start to slow the application, move them to a read replica or a data warehouse.
- 4
Add pipelines only when volume demands it
Introduce streaming and data lakes when event volume or the number of sources really requires them.
Which tool does what
| Good for | |
|---|---|
| Relational database (for example PostgreSQL) | Running the business: orders, customers, stock, invoices |
| Data warehouse | Analysis across many sources without slowing the live system |
| Data lake | Storing large volumes of raw data, such as logs, files and events, cheaply |
| Search engine (for example Elasticsearch) | Fast full-text search and log analysis |
Frequently asked questions
How much data counts as big data?
There is no fixed line. A practical test is whether one well-designed database can store your data and answer your questions quickly enough. If it can, you do not need big data tools yet.
Do we need a data warehouse?
You need one when you analyse data from several systems together, or when analytical queries slow down the system people work in. Until then, reports built on your main database are simpler and cheaper.
Can AI use our data without a big data platform?
Yes. Most business AI uses, such as searching documents, summarising records or classifying requests, work on data in an ordinary database, as long as the data is clean and in one place.
What should we do first if our data is spread across spreadsheets?
Move the process that creates the data into one system, so new records are clean from the start, and migrate the old spreadsheets into it.