Data & Analytics
Turn data into decisions
Build modern data platforms that centralize, organize, and deliver insights your business can actually use—from raw data to real-time analytics.
Our Data Services
Modern Data Platform
Your business runs on data—but only if you can trust it. We design modern data platforms that centralize, organize, and deliver information you can actually use.
- Data lakehouse architecture: Combine the scalability of a data lake with the structure of a warehouse using Delta Lake, Apache Iceberg or Snowflake
- Real-time analytics: Power dashboards with live metrics using Apache Kafka, Spark Streaming, or Databricks structured streaming
- Self-service BI: Connect easy-to-use dashboards (Looker, Power BI, Tableau, Metabase) for independent data exploration
Databricks
One platform for data engineering, analytics, and machine learning. We help you implement and scale Databricks so your team can move from raw data to real insights—faster.
- Workspace setup & governance: Configure environment with proper access controls, Unity Catalog, and secure cloud integration
- Data engineering with Delta Lake: Build robust, ACID-compliant data pipelines with consistent, versioned data
- ML & model lifecycle management: From MLflow experiment tracking to production deployment
- Collaborative notebooks: Enable seamless teamwork across data engineers, analysts, and data scientists
n8n workflows
Connect your tools. Automate your data. Save hours every week. We help you design and deploy low-code automation pipelines using n8n—so your data flows where it needs to, without manual work.
- Data pipeline automation: Automate ingestion, transformation, and delivery between APIs, databases, CRMs, and cloud storage
- Integration workflows: Connect Slack, Notion, Airtable, Google Sheets, Hubspot, PostgreSQL, and more
- Custom logic & triggers: Build event-driven flows that react to database changes, webhooks, or scheduled jobs
- Scalable and self-hosted: Deploy in your own infrastructure for full control and data security
Data Quality
Without clean data, your dashboards lie—and your decisions follow. We help you build robust data quality workflows that validate, monitor, and clean your data at every stage.
- Validation & schema checks: Catch inconsistencies early with rule-based validation on structure, types, nulls, and duplicates
- Quality monitoring: Real-time alerts when data freshness, completeness, or volume fall outside thresholds
- Cleansing & transformation: Standardize formats, resolve conflicts, fill missing values automatically
- Tooling integration: Work with Great Expectations, dbt tests, Airflow sensors, and custom scripts
Data Platform
Build momentum with modern data architecture
Unified Architecture
Single source of truth combining data lake scalability with warehouse structure
Real-time Processing
Live analytics and streaming data processing for instant insights
Data Governance
Built-in quality checks, lineage tracking, and access controls
Self-Service BI
Empower teams to explore data independently with intuitive dashboards
"Agilyti transformed our data chaos into a streamlined platform. Our analytics team went from spending 80% of their time on data prep to focusing on insights."
Data Pipeline Status
Live2.4TB
Daily Volume
99.8%
Quality Score
12ms
Avg Latency
| Pipeline | Status | Records |
|---|---|---|
| Customer Data | Active | 1.2M |
| Sales Analytics | Processing | 845K |
| Product Events | Active | 2.1M |
| ML Training | Completed | 650K |
How do you ensure data quality across different sources?
We implement comprehensive data quality frameworks with automated validation, schema enforcement, and real-time monitoring. Our approach includes data profiling, anomaly detection, and business rule validation at every stage of the pipeline.
Can you integrate with our existing data tools and systems?
Absolutely. We specialize in building bridges between systems—whether it's connecting legacy databases, modern cloud platforms, SaaS tools, or custom applications. Our integration approach preserves your existing investments while modernizing your data architecture.
How long does it take to implement a modern data platform?
Implementation typically takes 8-16 weeks depending on complexity and data volume. We use an iterative approach, delivering value in phases—starting with critical use cases and expanding from there. You'll see initial results within the first 4 weeks.
Do you support real-time analytics and streaming data?
Yes, we architect platforms for both batch and streaming data processing. Using technologies like Apache Kafka, Spark Streaming, and Databricks, we enable real-time dashboards, alerts, and decision-making based on live data feeds.
What's the difference between a data lake and data lakehouse?
A data lake stores raw data in its native format, while a data lakehouse combines the scalability of a data lake with the structure and ACID transactions of a data warehouse. This hybrid approach gives you the best of both worlds—flexibility and reliability.
How do you handle data governance and compliance?
We build governance into the platform from day one—data lineage tracking, access controls, audit logs, and compliance frameworks for GDPR, CCPA, and industry-specific regulations. Our approach ensures data is both accessible and secure.