What we deliver
Twara Technologies designs and builds the data foundations that analytics and AI depend on. Most organisations already hold valuable data, but it sits in separate systems with different definitions, gaps and duplicates. We bring it together through tested pipelines into a well-modelled warehouse or lakehouse, add the quality checks and governance that make it trustworthy, and deliver dashboards and datasets that people actually use. The same foundation then feeds Predictive Analytics & Forecasting and Generative AI & LLM Applications.
This service suits organisations whose reports disagree depending on who produced them, whose analysts spend more time collecting data than analysing it, or who want to adopt AI and find their data is not ready. We can start a greenfield platform or strengthen an existing one.
Typical scope
- Assessment of source systems, current reports, data quality and ownership.
- Target architecture: warehouse, lake or lakehouse; batch and streaming paths; environments.
- Ingestion from databases, SaaS applications, files, APIs, event streams and IoT platforms.
- Change data capture for near-real-time replication from operational databases.
- Transformation into layered models: raw, cleaned and business-ready.
- Data quality testing, freshness monitoring and incident alerts.
- Catalogue, lineage, access control, masking and retention policies.
- Dashboards, KPI definitions and governed self-service datasets.
- Migration from legacy reporting databases or spreadsheets.
Technologies we work with
- Platforms: Snowflake, Databricks, Google BigQuery, Amazon Redshift, Microsoft Fabric or Azure Synapse, and PostgreSQL for smaller estates.
- Ingestion: managed connectors such as Fivetran, open-source Airbyte, Debezium for change data capture, and Apache Kafka for event streams.
- Transformation: dbt for SQL-based modelling with tests and documentation; Apache Spark for large-scale or complex processing.
- Orchestration: Apache Airflow, Dagster or cloud-native workflow services.
- Open table formats: Apache Iceberg and Delta Lake for lakehouse storage that several engines can read.
- BI and visualisation: Power BI, Tableau, Looker, Apache Superset or Metabase.
- Governance: platform-native catalogues and access controls, plus open-source catalogues where several platforms coexist.
How we choose: existing cloud commitments and skills come first, then data volume and variety, then cost model. A smaller organisation is often best served by a managed warehouse, a handful of connectors and dbt; larger or more varied estates may justify a lakehouse and streaming.
How we approach it
- Start from questions. Identify the decisions and reports that matter most and the data they need.
- Assess sources. Profile data quality, volumes, refresh needs and ownership in each system.
- Design the platform. Agree architecture, naming, modelling standards, environments and access patterns.
- Deliver in slices. Build one subject area end to end, from source to dashboard, then repeat for the next.
- Test and monitor. Add data tests, pipeline alerts and cost monitoring from the first slice.
- Enable your team. Document models and runbooks, and train analysts and engineers to extend the platform.
Security, privacy and quality
- Personal data. Analytics platforms concentrate personal data, which raises the stakes. India’s DPDP Rules, 2025 were notified on 14 November 2025 with an eighteen-month phased compliance period, and the framework rests on principles including purpose limitation, data minimisation, storage limitation and security safeguards. We translate these into tagging of personal fields, masking, role-based access and automated retention.
- Logging and incidents. India’s CERT-In directions require covered entities to keep ICT system logs securely for a rolling 180 days within Indian jurisdiction and to report listed incidents, including data breaches and data leaks, within 6 hours of noticing them. We configure platform audit logs and alerting with these duties in mind.
- AI readiness. Data used to train or ground AI systems is documented for provenance, quality and permitted use, supporting risk management under the NIST AI Risk Management Framework.
- Engineering quality. Pipelines in version control, peer review, automated tests in CI/CD, separate development and production environments, and infrastructure defined as code.
Engagement options
- Data assessment: a review of sources, quality and reporting needs with a target architecture and roadmap.
- Platform build: Twara Technologies delivers the warehouse or lakehouse, pipelines and first dashboards.
- Modernisation: migrate legacy reporting databases and spreadsheets to a governed platform.
- Data operations: ongoing pipeline monitoring, new sources, model changes and cost reviews.
Contact us to talk about the data you have and the questions you want it to answer.