We value your trust in our Data Engineering. We build scalable lakehouse platforms and pipelines with data quality and governance, turning raw data into reliable business outcomes.
Years Experience
REVUTECK
What is Data Engineering—and why it matters
Data Engineering designs, builds, and runs the pipelines, storage, and governance that move data from sources to analytics, AI/ML, and apps. It covers ingestion, transformation, orchestration, quality, security, and cost, creating trusted, scalable data products that power decisions. With data contracts & SLAs.
For enterprises undergoing digital transformation, data is the foundation. Without trustworthy, timely datasets, dashboards mislead, models drift, operations slow, and customer experiences suffer—eroding revenue and trust. Now.
Data Strategy & Architecture
ETL/ELT Pipeline Development
Data Modeling & Warehousing
Data Lake & Big Data Solutions
Real-Time & Streaming Data
Data Quality & Governance
Cloud Migration & Modernization
BI & Reporting Enablement
MLOps & Operationalization
We craft scalable data strategies and modern architectures aligned with your business goals. From technology assessment to blueprint design, we ensure your data infrastructure is optimized for performance, flexibility, and long-term value.
Design and develop robust ETL/ELT pipelines to automate data extraction, transformation, and loading. Our solutions ensure high-speed data movement, integrity, and scalability across cloud, hybrid, and on-premise environments for better analytics.
We build structured, optimized data models and high-performance warehouses that support fast querying and analytics. Our solutions improve data accessibility and reporting using platforms like Snowflake, BigQuery, Redshift, and Azure Synapse.
Leverage massive volumes of structured and unstructured data with scalable data lakes and big data technologies. We implement solutions using Hadoop, Spark, and cloud-native tools to enable advanced analytics, ML, and long-term data storage.
Build real-time data pipelines that capture, process, and analyze events instantly. Using tools like Kafka, Flink, and Spark Streaming, we help you act on live data, detect anomalies, and gain instant insights to power business decisions.
Ensure data accuracy, consistency, and compliance across your organization. We implement data validation, lineage tracking, and governance frameworks to maintain clean, secure, and trustworthy data that aligns with internal and regulatory standards.
Migrate legacy systems to modern cloud platforms with minimal disruption. Our cloud modernization approach ensures data integrity, improved performance, and cost savings by leveraging AWS, Azure, or GCP-native services and architecture best practices.
Turn your data into decisions with interactive dashboards and reports. We integrate tools like Power BI, Tableau, and Looker to provide real-time visual insights, KPIs, and self-service analytics for business users across all departments.
Operationalize machine learning models using automated workflows, CI/CD pipelines, and real-time monitoring. We ensure scalable deployment, model retraining, version control, and performance tracking to keep your ML solutions production-ready.
The problem without strong Data Engineering
- Siloed, inconsistent data: Each team has a different truth; reports conflict.
- Manual, fragile pipelines: Break on schema changes; long recovery times.
- Slow analytics: Batch jobs take hours; no near real-time insights.
- Rising costs: Duplicate datasets, over-scaled clusters, and unused storage.
- Poor governance & compliance risk: No lineage, no PII controls, weak audit trails.
How our Data Engineering service works (end-to-end)
- Discover & Assess (Days 0–10)
Audit sources (apps, DBs, APIs, event streams), current pipelines, storage, quality, SLAs, and costs. Produce a 90-day roadmap with quick wins. - Design the Target Architecture (Days 11–20)
Choose the right ingestion (batch/stream), storage layers (bronze/silver/gold), and serving (warehouse, lakehouse, feature store). Define governance (catalog, lineage, RBAC), quality (DQ rules), and SLAs. - Build Reliable Pipelines (Days 21–60)
Implement ELT/ETL with orchestration (jobs/DLT/Airflow). Add schema evolution, PII masking, SCD-2 for history, checkpointing for recovery, and unit/integration tests for data. - Enable Analytics & AI (Days 45–75)
Model semantic layers (star/snowflake/medallion), expose curated data to BI (Power BI/Looker), and stand up feature stores / model registries for ML. - Operate & Optimize (Days 60–90)
Data observability (freshness, volume, distribution), alerts, cost dashboards (per pipeline/table), performance tuning, and runbooks. Knowledge transfer + documentation.
Key Features
- Streaming & Batch Ingestion
Kafka/Event Hubs/Kinesis, CDC (Debezium/Fivetran), REST/SaaS connectors; scheduling & backfills. - Lakehouse + Warehouse
Delta/Parquet/Iceberg for cost-efficient storage; Snowflake/BigQuery/Synapse for interactive SQL. - Data Quality & Reliability
Expectations & tests (e.g., Great Expectations), anomaly alerts, SLA tracking, replay & idempotency. - Modeling & Versioned History
Medallion architecture, SCD-2 dimensions, slowly changing schemas, semantic layer for BI. - Governance & Security
Data catalog/lineage, RBAC/ABAC, tokenization/masking for PII, audit logs, retention policies. - Observability & FinOps
Pipeline health dashboards, freshness KPIs, unit economics (₹/TB, ₹/query), right-sizing & auto-pause. - DevOps for Data (DataOps)
CI/CD for pipelines & SQL, promotion Dev→Stage→Prod, infra as code (Terraform), blue/green data releases.
What makes Revuteck unique
- Outcome-first blueprints: Proven reference architectures that cut time-to-value—customized to your stack.
- True hybrid expertise: Lakehouse + warehouse + streaming, across Azure/AWS/GCP and on-prem.
- Quality by default: Tests, expectations, and lineage shipped with every pipeline—not an afterthought.
- Cost transparency: We design for efficient storage/compute and give you live spend visibility per table/pipeline.
- Enablement built-in: Clear docs, runbooks, and handover sessions; optional ThinqNXT training for your team.
Industry-specific use cases
Utilities & Energy
- Use case: Smart-meter streams + outage events into a unified lakehouse; operational dashboards.
- Impact: Near real-time outage visibility; forecast accuracy improves; compliance reporting simplified.
BFSI / Fintech
- Use case: CDC from core banking + card systems to fraud models, governed access to PII.
- Impact: Fraud detection latency reduced; consistent compliance evidence packs for audits.
Retail / eCommerce
- Use case: Clickstream + POS + inventory unification; attribution & demand forecasting.
- Impact: +30–40% faster insights; stockouts reduced; marketing spend efficiency up.
Healthcare / Life Sciences
- Use case: HL7/FHIR ingestion, PHI masking, model features for clinical predictions.
- Impact: Secure data sharing; improved model performance; quicker regulatory reporting.
SaaS / Product
- Use case: Usage telemetry pipelines + feature store; self-serve analytics.
- Impact: Activation insights reduce churn; faster roadmap decisions.
Tech stack we excel at (we adapt to you)
- Cloud: Azure, AWS, GCP (and hybrid/on-prem)
- Compute: Databricks (DLT, Delta, Spark), Synapse, EMR/Glue, Dataflow/Dataproc
- Warehouses: Snowflake, BigQuery, Redshift, Azure SQL
- Streaming/CDC: Event Hubs, Kafka, Kinesis, Debezium, Fivetran, StreamSets
- Orchestration: Databricks Jobs, Airflow, ADF, Cloud Composer
- Quality/Observability: Great Expectations, Monte Carlo/Databand (or equivalent), custom dashboards
- Catalog/Governance: Unity Catalog, Purview, Collibra, Alation; RBAC/ABAC with Key Vault/Secrets Manager
Sample 90-day plan (typical)
- Weeks 1–2: Assessment, roadmap, security/governance baseline
- Weeks 3–6: Ingestion (2–4 top sources), bronze/silver layers, quality checks & tests
- Weeks 7–10: Curated gold models for 3–5 KPIs, BI semantic layer, lineage & catalog
- Weeks 11–13: Observability/alerts, cost dashboards, runbooks, enablement & handover
FAQs
How long does it take to see value?
Early wins in 2–4 weeks (first curated tables & dashboards). A full foundation typically within 90 days, then we scale.
What does this cost?
Starts with a fixed-fee assessment/QuickStart. Build-out and managed operations are monthly and depend on sources, SLAs, and compliance scope.
Do we need Databricks/Snowflake to start?
No. We design to your context. That said, lakehouse + warehouse combinations (e.g., Databricks + Snowflake/BigQuery) often deliver the best mix of cost and performance.
Can you integrate on-prem systems?
Yes—via CDC, secure gateways, or batch exports. We support hybrid and phased migrations.
How do you ensure data quality?
We embed expectations/tests at each stage, monitor freshness/volume anomalies, and enable rollbacks & replays with lineage.
Will our team be enabled post-project?
Yes—architecture docs, data contracts, runbooks, and recorded walkthroughs are included; optional training available.
Data Strategy & Architecture . ETL/ELT Pipeline Development . Data Modeling & Warehousing . Data Quality & Governance . Cloud Migration & Modernization . MLOps & Operationalization
Data Strategy & Architecture . ETL/ELT Pipeline Development . Data Modeling & Warehousing . Data Quality & Governance . Cloud Migration & Modernization . MLOps & Operationalization