We engineer robust pipelines, analyze large/complex datasets, and ship reproducible results-ready for thesis chapters, manuscripts, and data-driven decision making.
*Lightweight text/image analytics; deep models upon scope agreement.
Batch/stream ingestion, CDC, file/object storage, scheduling (Airflow).
Modeling (star/snowflake), partitioning, clustering, Z-ordering.
Validation rules, profiling, PII handling, reproducible lineage.
Window functions, joins, aggregates, distributed summary/inference.
Encodings, aggregations, leakage checks, train/test separation.
Spark MLlib/sklearn on clusters; tuning & evaluation basics.
Wide-table extracts, Power BI-ready models, journal-style figs.
Raw → cleaned → curated layers; notebooks + SQL + semantic models.
Kafka ingestion, mini-batch processing, late-arrivals handling.
Columnar files, predicate pushdown, partition pruning, caching.
| Area | Examples | Notes |
|---|---|---|
| ETL/ELT | Airflow, dbt, Spark jobs | Idempotent, parameterized, logged |
| Storage | Parquet/Delta, HDFS, S3/GCS/ADLS | Partition by date/entity; compression |
| Compute | Spark SQL, PySpark, UDFs | Broadcast joins, caching, AQE |
| Analytics | Distributed stats/ML | Stratified splits, eval metrics with CI |
| Governance | PII policies, access control | De-identification, audits, lineage |
We document assumptions, tuning choices, and trade-offs for academic transparency.
Ingest + clean + profiling + ready-to-analyze table.
Pipelines + distributed SQL/stats + figures/tables + notes.
Layered storage + dbt models + BI extracts + docs.
Pricing depends on data size/complexity, security needs, and turnaround. You’ll get a clear plan after discovery.
Goals, data map, constraints; success metrics agreed.
Choose storage/compute, file formats, partitions, access.
Ingestion + transforms + tests; scheduled jobs.
Distributed SQL/stats/ML; diagnostics & validation.
Publication-style figures/tables; optional dashboard.
Code, configs, runbooks, and change log.
Yes. We support AWS/Azure/GCP and on-prem clusters; we adapt to your security policies.
We design for large tables (billions of rows) using columnar formats, partitioning, and scalable compute.
Yes-Airflow (or cloud schedulers), with logging, retries, and alerting per environment.
We apply de-identification, access controls, and minimal-privilege principles. NDAs on request.
Parquet/Delta for analytics; CSV only for interchange. We choose compression/partitioning to fit your queries.
Absolutely. You’ll receive parameterized notebooks/scripts with clear instructions and a change log.
Yes-based on the question and data structure. We explain trade-offs and document assumptions.
Yes. We prepare BI-friendly tables and can publish to Power BI (or export for your tool).
Depends on scope and environments. Small pipelines: ~days; lakehouse setups: weeks with milestones.
By scope, data condition, complexity, and turnaround. We share a transparent plan and quote after discovery.