Data · Data Engineering, BI & Analytics
Data & Analytics Services
Data and analytics services turn scattered data from your apps, ERP, CRM and spreadsheets into numbers people trust and AI systems can use. Aaga builds the pipelines, warehouse and dashboards end to end with a lean, senior team, starting with one business question and one working dashboard.
- Pipelines that pull data from your CRM, ERP, databases and SaaS tools on a schedule
- A warehouse or lakehouse on Snowflake, BigQuery, Databricks or Postgres, sized to your volume
- Dashboards in Power BI, Looker Studio or Metabase built on tested, documented metrics
- Clean, governed data ready for forecasting, RAG and AI agents
- One question, one pipeline, one dashboard
- Pilot first
- Data engineers on every engagement
- Senior
- Reply to your inquiry within one business day
- 1 day
What Do Our Data & Analytics Services Cover?
Everything between raw source data and a decision: ingestion, modeling, quality, reporting and the data foundation your AI work depends on.
Data Engineering & Pipelines
ETL and ELT pipelines with tools such as Airbyte, Fivetran, dbt and Airflow or Dagster, with incremental loads, retries and alerting.
Warehouses & Lakehouses
Data platforms on Snowflake, BigQuery, Databricks or Postgres, modeled into clear staging, core and reporting layers.
BI Dashboards
Power BI, Looker Studio and Metabase dashboards built on a shared metrics layer, so revenue means the same thing in every report.
AI-Ready Data
Clean, documented tables, document pipelines and vector search (for example pgvector) that feed RAG, copilots and AI agents.
Predictive Analytics
Demand forecasting, churn and lead scoring, and anomaly detection, deployed where your team already works.
Data Quality & Governance
dbt tests and data contracts, freshness checks, lineage, role-based access and PII masking, so most bad data is caught before it reaches a dashboard.
From Scattered Data to Trusted Dashboards
Data Audit
We map your sources, current reports and the decisions they support, and list the gaps and quality issues.
Target Architecture
We recommend a warehouse, ingestion and BI stack sized to your data volume, team skills and budget.
First Pipeline & Dashboard
We deliver one end-to-end slice: sources loaded, metrics modeled and a dashboard your team uses every week.
Harden
We add tests, monitoring, documentation and access controls, and agree metric definitions with owners.
Scale or Hand Over
We add sources and use cases in priority order, or train your team to run and extend the platform.
A Lean Data Team, Not a Data Program
Large firms are well suited to enterprise-wide data programs across many business units. For one company or one domain, a smaller senior team usually moves faster.
| Aaga | Typical large IT services model | |
|---|---|---|
| Starting point | One business question and a working dashboard | Enterprise data strategy and roadmap phase |
| Who builds it | Senior data engineers you talk to directly | Delivery teams behind account and project managers |
| Stack choice | Best fit for your volume, from Postgres to Databricks | Often aligned to preferred partner platforms |
| AI readiness | Designed in from the first model | Often a separate later initiative |
| Best for | SMB, mid-market and single-domain teams | Global, multi-business-unit data programs |
Comparison describes typical delivery models, not any specific company.
Which Data Warehouse Should You Use?
The right warehouse depends on data volume, workload and the skills of the team that will run it. As a rule of thumb:
- Postgres works well for modest data volumes and teams that want one familiar database. With good indexing and materialized views it carries many companies further than expected.
- BigQuery suits teams on Google Cloud and spiky, query-driven workloads, with serverless pricing and little to manage.
- Snowflake is a strong choice for SQL-first analytics across many sources, with clean separation of storage and compute.
- Databricks fits heavy data engineering, large semi-structured data and machine learning workloads on a lakehouse with Delta Lake.
Aaga recommends the simplest option that meets your needs for the next few years, and designs models with dbt so you can move later without rewriting business logic.
What Are Data & Analytics Services?
Data and analytics services help a business collect data from its systems, store it in one place, make sure it is correct and turn it into reports, forecasts and AI features. The work covers data engineering (pipelines), data platforms (warehouses and lakehouses), business intelligence (dashboards) and advanced analytics (predictive models).
Aaga delivers these as one connected service. The same senior engineers who build your pipelines also model the metrics and build the dashboards, so nothing gets lost between teams.
Why Data Projects Stall
Most companies already have the data they need. It sits in a CRM, an ERP, a billing system, a product database and a lot of spreadsheets. The usual problems are:
- No single source of truth. Sales, finance and operations each report different numbers for the same metric.
- Manual reporting. Analysts spend days exporting CSVs and rebuilding the same report every month.
- Fragile pipelines. Scripts break silently, and nobody notices until a dashboard is wrong in a board meeting.
- AI on bad data. A chatbot or forecasting model is only as good as the data underneath it.
The fix is rarely a bigger tool. It is a small, well-designed data platform with clear ownership and automated checks.
What Aaga Builds
Data pipelines (ETL and ELT)
We connect your sources, such as Salesforce, HubSpot, Zoho, Odoo, Shopify, Stripe, Google Ads, application databases and files, using managed connectors like Airbyte or Fivetran where they fit, and custom Python pipelines where they don't. Pipelines run on a schedule or on change (change data capture), with retries, logging and alerts. If your source systems need cleaner integrations first, our enterprise solutions and integration team handles that side.
Warehouse and data models
Raw data lands in the warehouse and is modeled with dbt into staging, core and reporting layers. Every important metric, such as revenue, active customers or on-time delivery, is defined once, tested and documented. That is what makes a dashboard trustworthy.
Dashboards and self-serve BI
We build dashboards in Power BI, Looker Studio or Metabase around the decisions people make each week, not around every available chart. We also set up row-level security so each team sees only what it should. On our marketing automation and CDP project, customer data from several sources was imported, enriched and segmented, then surfaced in reporting dashboards for campaign decisions.
Predictive analytics
Once the foundation is solid, forecasting and scoring models become practical: demand forecasting, churn prediction, lead scoring and anomaly alerts. Our machine learning services take these from notebook to production, with monitoring for drift.
AI-ready data
Generative AI raises the bar for data. Retrieval-augmented generation needs documents that are clean, chunked, permissioned and kept up to date. AI agents need reliable, well-described tables to query. We design both, so your AI engineering work rests on a dependable base. Our guide to RAG for business explains the retrieval side in more detail.
Data Quality and Governance, Built In
Governance does not have to mean a heavy program. For most companies it means a few practical controls:
- Tests on every model: freshness, uniqueness, not-null and accepted values, run on every pipeline run.
- Alerts: failed loads and broken tests notify an owner in Slack, Teams or email.
- Lineage and documentation: you can see where each number comes from and what it means.
- Access control: role-based access, masking of personal data and audit logs, aligned with privacy rules such as GDPR and India's DPDP Act.
What You Get at the End of an Engagement
- Pipelines in your own cloud account and code repository, with scheduling and alerting configured
- A documented data model with tested metric definitions
- Dashboards with access rules set per team
- A runbook covering how to add a source, fix a failed load and change a metric
- A handover session for your analysts or engineers
Who This Is For
Aaga's data and analytics services fit:
- Growing companies replacing spreadsheet reporting with a real warehouse and dashboards
- Mid-market teams whose existing BI has become slow, inconsistent or expensive
- Product companies adding customer-facing analytics to their platform
- Teams starting AI projects that need clean, governed data first
If you need a global, multi-year data program across dozens of business units, a large IT services firm may be the better fit. If you want one domain working well within weeks, start with a free consultation and we'll scope a first pipeline and dashboard.
Frequently Asked Questions
Data and analytics services cover collecting data from your systems, storing and modeling it in a warehouse, checking its quality, and presenting it in dashboards and predictive models. Aaga delivers all of these with one senior team, from the first pipeline to production BI.
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse, usually with dbt. Most modern cloud warehouses favor ELT because it keeps raw history and makes transformations easier to test and change.
Power BI suits Microsoft-centric companies, Looker Studio suits Google Workspace and marketing reporting, and Metabase is a strong open-source option for self-serve internal analytics. We recommend based on your users, licensing and existing tools, and can work with the one you already have.
Yes. Many engagements start by fixing or extending an existing Snowflake, BigQuery, Databricks, Redshift or Postgres setup. We audit what is there, keep what works and fix pipelines, models or dashboards that are unreliable.
AI-ready data is clean, documented, access-controlled and available in a form AI systems can use, such as well-modeled tables for forecasting and chunked, indexed documents for retrieval-augmented generation. Without it, AI agents and copilots give unreliable answers.
We add automated tests for freshness, uniqueness and accepted values, set up alerts when pipelines fail, document lineage and definitions, and apply role-based access and masking for personal data. Ownership of each key metric is agreed with your team.
It depends on how many sources are involved and how clean they are. A focused first slice with a few sources and one dashboard is typically a matter of weeks. We give you a timeline after the data audit.
Ready for Numbers Your Team Trusts?
Tell us your sources and the decisions you need to make. We'll propose a first pipeline and dashboard you can use within weeks.
Plan My Data Stack