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Big data solutions let you store and analyse data that has outgrown a regular database: millions of orders, app events, GPS points or log lines every day. Hyper Software builds data pipelines, lakes and warehouses on modern cloud tools, sized to your real needs and budget.
Big data means data that is very large in volume, arrives very fast, or comes in many formats. A big data solution usually includes:
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You probably do if:
You probably don't yet if your data fits comfortably in a well-tuned PostgreSQL or MySQL database. In that case, optimization or a simple reporting database is cheaper.
| Option | What it is | Good for |
|---|---|---|
| Data lake | Raw files in cloud storage (S3, GCS) in formats like Parquet | Cheap storage of everything |
| Data warehouse | Structured tables for SQL analytics (BigQuery, Snowflake, Redshift) | Fast business reporting |
| Lakehouse | Lake storage with warehouse-style tables (Databricks, Iceberg) | Mixed analytics and ML |
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Professional web development service ke liye Hyper Software ko zarur choose karein. Team experienced hai aur har problem ka quick solution deti hai. Bahut achha support.
Hamari old website ko completely redesign kiya gaya. Naya design modern, fast aur user-friendly hai. Business ki online image aur strong ho gayi.
Project time par deliver hua aur quality expected se bhi better mili. Website speed aur SEO optimization dono excellent hain. Thank you Hyper Software.
Website development ke saath domain aur hosting ka complete solution bhi mila. Team ne har step par proper guidance di. Bahut trusted company hai.
Website development ke saath domain aur hosting ka complete solution bhi mila. Team ne har step par proper guidance di. Bahut trusted company hai.
| Need | Tools |
|---|---|
| Cloud warehouse | BigQuery, Snowflake, Amazon Redshift |
| Batch processing | Apache Spark, dbt, SQL |
| Streaming | Apache Kafka, Google Pub/Sub, AWS Kinesis |
| Orchestration | Apache Airflow, Cloud Composer |
| Analytics | Power BI, Looker Studio, Metabase |
Not sure where to start? Call or WhatsApp Hyper Software on +91 9079282750 or email info@hypersoftware.in for a free first consultation about Big Data Solutions.
| Project | Typical time | Indicative cost |
|---|---|---|
| Assessment and architecture plan | 1 to 2 weeks | ₹40,000 to ₹1,00,000 (about US$500 to US$1,200) |
| Cloud warehouse + pipelines for several sources | 1 to 3 months | ₹2,00,000 to ₹8,00,000 |
| Streaming / real-time platform | 2 to 4 months+ | Quoted after design |
Cloud usage (storage, queries, compute) is billed by the provider. We estimate it upfront and design to keep it low.
| In-house from scratch | Data engineering partner | |
|---|---|---|
| Hiring time | Months | Start in weeks |
| Architecture mistakes | Common first time | Avoided from experience |
| Cost control | Learned the hard way | Designed in |
They are systems that collect, store and process very large or fast data so it can be analysed efficiently.
If reports time out on huge tables, or you need real-time event analysis, you likely do. If not, a tuned database may be enough.
An assessment usually costs ₹40,000 to ₹1,00,000. Full platforms cost more depending on sources and real-time needs.
We work with Google Cloud, AWS and Azure, plus Snowflake and Databricks.
Yes. We use Kafka, Pub/Sub or Kinesis for streaming data.
Through partitioning, clustering, efficient queries, storage tiers and cost alerts.
Yes. We set up scheduled or real-time sync from MySQL and other databases.
Yes. We use encryption, access controls and masking for personal data.
Yes. We connect Power BI, Looker Studio, Metabase or custom dashboards.
Most projects take 1 to 4 months depending on scope.
A logistics platform collected tens of millions of GPS and status events each month in its main MySQL database, slowing both the app and reports. We streamed events to cloud storage, loaded them into BigQuery with date partitioning, and modelled delivery performance tables with dbt. The main database got lighter and faster, and operations reports that used to time out now run in seconds, with a predictable monthly query bill.
Volume, velocity and variety.
A lake stores raw data cheaply; a warehouse stores structured data for fast queries.
Yes. It charges by usage, so small datasets cost little.
An automated process that moves and transforms data from sources to storage and reports.
Processing large datasets in parallel for batch and streaming jobs.