Data strategy
Identify the decisions your data must support, assess source limitations, and establish a prioritized delivery roadmap.
Big data engineering & consulting.
Bring your data into focus.
Build a dependable foundation for reporting, operations, and AI. AsonTech Solutions helps your team connect data sources, improve quality, and deliver information people can use with confidence.
Clear scopeagreed prioritiesReliable pipelinesvisible quality checksClear ownershippractical operations
Data expertise across your entire platform.
Connect engineering decisions to the people, applications, and reports that depend on your data.
Warehouses & data lakes
Design storage and analytical models around access patterns, ownership, retention needs, and expected growth.
Data pipelines
Connect source systems with repeatable ingestion and transformation workflows, including validation, retries, and traceability.
Streaming analytics
Process events where timely information matters, with explicit freshness targets and a plan for delayed or duplicate records.
Data governance
Define dataset owners, access boundaries, quality rules, and lifecycle practices that your team can maintain.
AI data preparation
Prepare discoverable, well-structured datasets for AI evaluation and development, with attention to provenance and appropriate access.
A data partner who works
with your team.
Keep analytical needs, engineering constraints, and operating responsibilities aligned throughout delivery.
Quality you can inspect
Make checks and exceptions visible so downstream teams understand the information they are using.
Shared technical ownership
Work with your engineers and analysts to document decisions and maintain a practical handover.
Architecture that fits
Choose a foundation based on workload needs and existing systems rather than adding unnecessary complexity.
Cost-aware operations
Review processing, storage, and retention choices alongside performance and reliability requirements.
Where is your data slowing your team down?
Start with a practical roadmap.
Share your sources, current platform, and the questions your reporting cannot answer. We’ll discuss the assessment and engineering work needed to move forward.
A clear path from raw data to useful information.
Shared milestones keep business stakeholders and engineering teams aligned throughout delivery.
Assess the landscape
Inventory datasets, dependencies, current reporting gaps, and the business questions that matter most.
Design the foundation
Define models, storage, access rules, and how information will move through the platform.
Build the pipelines
Implement ingestion and transformation with validation, failure handling, and repeatable deployment.
Connect the consumers
Integrate the agreed applications, reporting tools, and analytical workflows with clear data contracts.
Validate at scale
Check quality, freshness, query performance, and recovery behavior against agreed acceptance criteria.
Operate and improve
Document monitoring, ownership, and support routines, then review changes as sources and usage evolve.
A delivery model that fits your team.
Choose the balance of collaboration, responsibility, and scope your project needs.
Add data engineering capacity
Bring targeted engineering or architecture expertise into your current team and delivery process.
For a team with a defined roadmap and technical ownership.
Discuss this approachCoordinate a data platform program
Bring architecture, pipeline development, and analytical delivery into a shared plan.
For modernization work spanning several releases.
Discuss this approachDeliver a focused data outcome
Agree on sources, deliverables, dependencies, and acceptance criteria before implementation.
For a bounded assessment, integration, or migration.
Discuss this approachDependable data starts with clear foundations.
People
Named dataset owners
Planning
A roadmap tied to business needs
Quality
Visible validation and exceptions
Continuity
Documented operations and handover
OUR EXPERTISE
Every TechnologyStack Covered.
Hire specialists across AI, web, mobile, cloud, data, and enterprise software.
TensorFlow
Keras
PyTorch
Python
spaCy
OpenAI
Plotly
Pandas
OpenCV
NumPy
Scikit-learn
Hugging Face
LangChain
Jupyter
MLflow
Anthropic
Claude
GitHub Copilot
Cursor
Milvus
Explore our software portfolio.
See the application and platform work featured across AsonTech Solutions.
Big data services FAQs
Answers about platforms, pipelines, governance, and delivery.
What do big data services include?
An engagement can cover architecture, ingestion, transformation, storage, analytics integration, governance, and operational handover. The scope starts with the decisions and workflows your data needs to support.
Do we need a new data platform?
Not necessarily. We review your existing tools and limitations before recommending changes. Targeted improvements to pipelines or models may be more appropriate than replacing the whole platform.
What is the difference between a warehouse and a data lake?
A warehouse typically organizes modeled data for analysis and reporting. A lake can retain a wider range of raw and processed data. The right approach depends on how your team stores, manages, and uses information.
Can you integrate our existing systems?
We assess available APIs, database access, export mechanisms, and source ownership. Integration planning includes refresh frequency, schema changes, and handling incomplete or duplicate records.
When should we use streaming instead of batch processing?
Streaming is useful when a business decision needs timely event data. Batch processing may be simpler and more economical when scheduled updates meet the need. We define freshness requirements before choosing.
How do you address data quality and access?
We agree on quality rules, validation checks, ownership, and access boundaries. Exceptions should be visible and actionable rather than silently passed into reports or downstream applications.
How are timeline and cost estimated?
The estimate depends on source complexity, volume, integrations, migration needs, and operational requirements. Discovery produces a scope and sequence of milestones your team can review.