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Big data analytics

Big data analytics for datasets that outgrow spreadsheets and single databases

When event logs, transactions or sensor readings become too large for your current tools, we design processing that scales without making every query expensive.

  • Distributed processing with Spark and cloud-native engines
  • Streaming pipelines for near-real-time data
  • Cost-aware storage and compute choices
  • Analytics and machine learning on large datasets

Big data services

Platform design

Choosing between managed warehouses, lakehouses and Spark clusters for your workload.

Pipelines

Reliable batch and streaming jobs with retries, monitoring and data quality checks.

Performance tuning

Partitioning, clustering and caching so queries stay fast as volume grows.

Advanced analytics

Feature engineering and model training on full datasets rather than samples.

How an analytics engagement runs

  1. Step 1

    Questions first

    We agree the decisions the data should support and the measures that matter.

  2. Step 2

    Data review

    We profile your sources, note quality issues and confirm what is feasible.

  3. Step 3

    Build in slices

    Pipelines, models and reports are delivered one subject area at a time.

  4. Step 4

    Hand over

    Documentation, access controls and training so your team can run and extend it.

Frequently asked questions

Do we actually have big data?

Not always. Sometimes a well-tuned warehouse is enough. We size your data and query patterns before recommending a distributed platform.

Can you work with the tools we already pay for?

Yes. We usually build on your existing cloud, warehouse and BI licences, and recommend new tools only when there is a clear gap.

Tell us what you are working on

Share your goals and current setup. We will come back with questions, a suggested approach and the next steps.

Contact Raushang4 Technology