Data Engineering
Turn Data Into Decisions.
- ETL / ELT pipelines
- Warehouses & lakehouses
- Real-time streaming
- BI & analytics
Overview
Data Engineering at Assistora
Every analytics dashboard, forecast and AI feature depends on data that is timely, accurate and well understood. Many organisations have the data they need but cannot use it because it is scattered, inconsistent or locked inside operational systems.
Assistora builds data platforms that are modular, tested and documented. We favour managed cloud services and open formats, apply software engineering discipline to data pipelines, and make data quality visible.
Business challenges
The problems we help you solve.
- 01
Data silos
Information is spread across applications, spreadsheets and SaaS tools with no single view.
- 02
Untrusted numbers
Different reports show different answers, eroding confidence in data.
- 03
Slow insight
Batch processes deliver data too late to act on.
- 04
Not AI-ready
Unstructured, undocumented data cannot safely feed machine learning or AI assistants.
Capabilities
What we deliver.
ETL / ELT
Robust ingestion and transformation pipelines with orchestration, retries, lineage and alerting.
Data Warehouses
Dimensional and analytics-ready models on cloud warehouses designed for performance and cost.
Data Lakes & Lakehouses
Open-format storage with Delta Lake or Apache Iceberg for flexible analytics and ML.
BigQuery
Serverless analytics on Google Cloud with partitioning, clustering and cost governance.
Snowflake
Scalable warehousing with secure data sharing, role design and workload isolation.
Apache Spark
Large-scale distributed processing for complex transformations and feature engineering.
Apache Kafka
Event streaming backbones for decoupled systems and real-time data movement.
Real-Time Pipelines
Streaming ingestion and processing for live dashboards, alerts and operational analytics.
Analytics Engineering
Tested, documented transformation layers with dbt and a shared semantic model.
Business Intelligence
Dashboards and self-service analytics in Looker, Power BI or similar tools, built on governed metrics.
Platforms & technologies
The stack we work with.
Warehouses & lakes
- BigQuery
- Snowflake
- Amazon Redshift
- Databricks
- Azure Synapse
Processing
- Apache Spark
- dbt
- Apache Beam
- Dataflow
Streaming & orchestration
- Apache Kafka
- Pub/Sub
- Kinesis
- Apache Airflow
- Dagster
BI
- Looker
- Power BI
- Tableau
- Metabase
Typical solutions
Where clients usually start.
- 01
Modern data platform
A cloud warehouse or lakehouse with automated ingestion from core business systems.
- 02
Single customer view
Unified customer data across CRM, product and support systems.
- 03
Real-time operations dashboard
Streaming metrics that show what is happening now, not yesterday.
- 04
AI-ready data foundation
Clean, documented, access-controlled data prepared for ML and retrieval use cases.
Delivery process
How we deliver.
- 01
Discover
Workshops and technical discovery to understand goals, constraints, current systems and success measures.
- 02
Map the data
Catalogue sources, consumers, quality issues and the decisions the data must support.
- 03
Design the platform
Select storage, processing and orchestration; define models, governance and access.
- 04
Build pipelines
Implement ingestion and transformations as tested, version-controlled code.
- 05
Enable analytics
Deliver dashboards and datasets, train users and monitor data quality continuously.
Business benefits
What it means for your business.
One version of the truth
Governed metrics give every team the same reliable answers.
Timely insight
Fresh data supports faster, better-informed decisions.
Lower platform cost
Efficient models and managed services reduce compute and maintenance.
Foundation for AI
Quality data unlocks machine learning and AI assistants.
Relevant industries
Applied across sectors.
- Financial ServicesSecure, compliant and resilient platforms for banking, payments, lending, insurance and fintech.
- E-commerceFast, scalable commerce experiences and the data and infrastructure behind them.
- LogisticsReal-time visibility, optimisation and mobile tools for supply chain and transportation operations.
- ManufacturingConnected operations, data and automation for modern manufacturers.
FAQs
Data questions, answered.
Each is excellent. The right choice depends on your existing cloud, workload types, team skills and pricing model. We provide an objective comparison for your situation.
Yes. We plan migrations with parallel runs and reconciliation so reports stay accurate throughout the transition.
Automated tests on every pipeline, freshness and volume monitoring, and clear ownership of each dataset.
Yes — streaming architectures with Kafka, Pub/Sub or Kinesis where real-time data provides genuine business value.
Related services
Often delivered together.
AI & Data
Artificial Intelligence
Generative AI, intelligent agents and machine learning engineered into real business workflows — securely and measurably.
- OpenAI API
- Anthropic Claude
- Google Gemini
Cloud & Infrastructure
Cloud Computing
Design, migrate and operate secure, cost-efficient cloud environments across AWS, Google Cloud and Microsoft Azure.
- AWS
- Google Cloud
- Microsoft Azure
Software Engineering
Custom Software Engineering
Custom applications, microservices and integrations engineered around how your business actually works.
- TypeScript
- Python
- Go
Ready to talk data engineering?
Share your goals and constraints. An Assistora engineer will respond with practical next steps.