Data Warehouse & Lakehouse Architecture
Snowflake, BigQuery, Redshift, and Databricks implementations designed for your query patterns, cost profile, and governance requirements — built right from day one, not patched later.
Modern Data Platform Engineering
Your Data Is Worthless Until
Someone Can Trust It.
Disconnected systems, broken pipelines, and dashboards nobody believes are costing you decisions every single day. Kalp Corporate builds data foundations that your entire organisation can rely on — from raw ingestion to boardroom insight.
150+
Data Platforms Built
10B+
Certification Pass Rate
99.9%
Pipeline Reliability
5×
Faster Insights
The Data Trust Crisis
Data You Can’t Trust Is Worse Than No Data at All
Two Reports, Two Different Numbers
Finance shows one revenue figure, sales shows another. When leadership can’t trust the data, they make decisions on instinct — and blame the data team when things go wrong.
Analysts Preparing Data Instead of Analysing It
If your analysts are writing manual SQL joins, downloading CSVs, and cleaning spreadsheets, they are data engineers by accident — and your analytics capacity is running at 20% of its potential.
Broken Pipelines Nobody Notices Until It’s Too Late
Silent data failures — where a pipeline stops without alerting anyone — result in stale dashboards, missed anomalies, and decisions based on data that is days or weeks out of date.
Data Silos That Make Cross-Team Analysis Impossible
When marketing, product, finance, and operations maintain isolated data stores, the questions that matter most — the cross-functional ones — become impossible to answer without weeks of manual effort.
73%
of enterprise data goes unused for analytics (IBM study)
44%
of analysts’ time spent finding and preparing data rather than analysing it
$15M
Average annual cost of poor data quality for large enterprises (Gartner)
Data Services
From Raw Ingestion to Trusted Insight — Full Stack
We cover the entire data value chain — architecture, engineering, quality, governance, and the analytics layer your business actually uses every day to make decisions.
Assess My Data Platform →ELT/ETL Pipeline Engineering
Robust, monitored data pipelines using dbt, Fivetran, Airbyte, and Apache Spark — handling batch, micro-batch, and real-time streaming from dozens of source systems simultaneously.
Real-Time Streaming Analytics
Apache Kafka, Apache Flink, and AWS Kinesis architectures for use cases that require sub-second latency — fraud detection, live operational dashboards, and event-driven alerting at scale.
BI Dashboards & Self-Serve Analytics
Tableau, Power BI, Looker, and Metabase implementations with semantic layer modelling — empowering business teams to answer their own data questions without waiting on analysts.
Data Quality & Observability
Automated quality testing with dbt and Great Expectations, pipeline observability with Monte Carlo, and alerting that catches data anomalies before they reach reports or downstream models.
Data Governance & Cataloguing
Data catalogues, end-to-end lineage tracking, role-based access controls, PII classification, and automated retention policies — making your data platform compliant, discoverable, and auditable.
Platform Build Methodology
Reliable Data Platforms Are Built in Layers, Not Overnight
Our phased approach delivers business value at each stage — you do not wait for the full platform to start getting insights that matter.
Data Landscape Assessment
Source system inventory, data quality audit, use case prioritisation, and architecture recommendation — delivered as a detailed Data Strategy document with a phased roadmap.
Foundation & Core Pipelines
Cloud warehouse setup, core ELT pipelines from priority sources, semantic modelling, and the first dashboard that makes leadership say “I can finally see what is happening.”
Quality, Governance & Scaling
Data quality testing, observability tooling, access controls, data cataloguing, and expanding pipeline coverage to additional source systems and analytics use cases.
Self-Serve & Advanced Analytics
Self-serve analytics enablement for business teams, feature engineering for ML models, real-time streaming capabilities, and full team training on the complete platform.
Modern Data Stack
The Right Architecture for Your Stage of Data Maturity
We design data platforms that match where you are today and can grow with where you are going — without expensive re-architecting every 18 months.
Cloud Data Warehouses
Snowflake, BigQuery, and Redshift design, implementation, and ongoing optimisation — including storage tiering, query performance tuning, and cost governance frameworks.
Data Transformation (dbt)
dbt project architecture, testing frameworks, documentation, semantic layer modelling, and CI/CD integration for version-controlled, thoroughly tested, fully documented data models.
Pipeline Orchestration
Apache Airflow, Dagster, and Prefect implementations with DAG design patterns, retry logic, SLA monitoring, and data-aware scheduling that keeps pipelines reliable and observable.
Data Ingestion & CDC
Fivetran, Airbyte, and Debezium for source-to-warehouse replication with change data capture — reliable ingestion from 200+ connectors with minimal operational overhead.
Stream Processing
Kafka topic design, Flink job development, and Kinesis stream processing — for real-time aggregation, event-driven pipelines, and operational analytics at millisecond latency.
Data Catalogue & Lineage
DataHub, Apache Atlas, and Alation implementations providing end-to-end lineage visibility, business glossary management, and data discovery for all stakeholders across the organisation.
Technology Expertise
Deep Expertise Across the Modern Data Stack
Every tool chosen for production reliability, community maturity, and long-term maintainability by your own team.
Snowflake
BigQuery
Redshift
Databricks
dbt
Apache Airflow
Dagster
Apache Kafka
Apache Spark
Apache Flink
Fivetran
Airbyte
Tableau
Power BI
Looker
Metabase
Python
SQL
Monte Carlo
Great Expectations
DataHub
Industries We Serve
Data Platforms That Speak Your Industry’s Language
Every industry has unique data volumes, regulatory requirements, and analytical questions. We build platforms shaped by that context — not generic templates reused across verticals.
Delivery Track Record
Data Platforms That Perform at Scale
10B+
Records processed daily across client data platforms in production
5×
Faster time-to-insight after modern data stack implementation
60%
Reduction in analyst time spent on data preparation vs analysis
99.9%
Pipeline reliability with automated monitoring, alerting, and self-healing
Common Questions
Data Engineering Questions, Answered Clearly
What is the difference between a data warehouse and a data lake?
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A data warehouse stores structured, processed data optimised for SQL analytics. A data lake stores raw data in any format at lower cost. Modern lakehouses like Databricks combine both approaches. We help you choose the right architecture for your use case and budget — there is no universal right answer, and we give you an honest recommendation based on your specific workloads.
Which BI tools do you work with?
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We work with Tableau, Power BI, Looker, Metabase, and Apache Superset. We also build custom embedded analytics. Tool selection is based on your team’s existing skills, governance requirements, budget, and the complexity of visualisations needed. We help you choose, then build the semantic layer and training to make adoption stick.
How do you handle data quality and pipeline reliability?
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We implement dbt data tests, Great Expectations validation suites, and data observability tooling with automated alerting. Data quality checks run on every pipeline execution — anomalies are caught and flagged before they propagate to downstream reports, dashboards, or ML feature stores.
Can you work with our existing data infrastructure?
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Yes. We integrate with and extend existing stacks — whether that is a legacy on-premise data warehouse, a partially migrated cloud environment, or a partially built modern data platform. We meet you where you are and improve incrementally, delivering value at each step without requiring a full rebuild upfront.
What is the modern data stack?
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The modern data stack typically combines a cloud data warehouse (Snowflake, BigQuery, or Redshift), a transformation layer (dbt), an orchestrator (Airflow or Dagster), and a BI tool — connected by ELT pipelines from Fivetran or Airbyte. It replaces brittle, undocumented custom ETL with maintained, observable, version-controlled data infrastructure your team can actually understand and debug.
How do you ensure data governance and regulatory compliance?
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We implement data catalogues, end-to-end lineage tracking, role-based access controls, PII classification and masking, and automated retention policies. Everything is documented and auditable — meeting GDPR, HIPAA, and other regulatory requirements specific to your industry and jurisdiction.
Start With a Data Audit
Stop Flying Blind. Build a Data Foundation You Can Trust.
Book a free data platform assessment. We will review your current stack, identify gaps, and show you the fastest path to data your whole organisation can rely on — no sales pitch, no obligation.
Explore More
Services That Work Best With a Strong Data Foundation
Custom Software Development
When automation requires a custom interface, self-service portal, or integration layer, our development team builds the surrounding system that makes it all work seamlessly.
Cybersecurity & Compliance
Cloud infrastructure without security architecture is a liability. We embed security into every layer — from IAM policies and network segmentation to runtime threat detection.
Cloud & DevOps
The infrastructure that runs your custom software — scalable cloud architecture, CI/CD pipelines, auto-scaling, and 24/7 monitoring to keep your systems fast, available, and cost-efficient.