Big Data Analytics That Turns Raw Data Into a Governed, Queryable Asset
We build the data pipelines, ETL processes, and batch and streaming infrastructure that take scattered, high-volume data and turn it into a governed warehouse your team can actually query — reliably, at scale, and without a spreadsheet in sight.
70+
Data Platforms Delivered
10TB+
Data Processed Daily Across Clients
12+
Years of Enterprise Experience
SOC 2 Compliant
99.98% Uptime
Big Data Analytics, Built Around Pipelines That Actually Hold Up
Most organizations don't have a shortage of data — they have data scattered across source systems, formats, and spreadsheets that nobody fully trusts. At Odidor, we treat the pipeline as the product: getting data extracted, transformed, and loaded correctly into a governed warehouse, so every report and model downstream draws from the same consistent, validated source of truth.
Our engineering covers both processing models. Apache Spark handles large-scale batch jobs for historical analysis and nightly reporting, while Apache Kafka powers streaming pipelines for data that needs to be queryable within seconds, like operational monitoring or fraud signals. Both land in a warehouse — Snowflake, BigQuery, or Redshift, depending on your existing cloud footprint — modeled specifically around the queries your business actually runs.
Data quality and governance aren't a phase we get to eventually; validation, schema checks, and access controls are built into the pipeline from day one. The result is infrastructure that scales with your data volume instead of buckling under it, and a warehouse your team can query with confidence instead of double-checking against a spreadsheet.
- Batch and streaming pipeline engineering
- Snowflake, BigQuery & Redshift warehousing
- Automated data quality validation
- Governance & access controls built in
10TB+
Data processed daily across clients
5-10x
Typical query performance improvement
7-10wk
Typical pipeline delivery window
99.9%
Pipeline uptime across deployments
A Data Analytics Partner That Builds the Full Pipeline
We don't hand you a dashboard on top of someone else's messy data. Ingestion, transformation, warehousing, and governance are built as one connected system.
Pipeline Engineering, Not Just Dashboards
We build the ETL and ingestion layer that gets raw data into a governed warehouse correctly, before a single chart is ever drawn on top of it.
Batch and Streaming, Both Covered
Nightly batch jobs and Kafka-driven streaming pipelines are engineered from the same architecture, so your analytics aren't locked to one processing model.
Scale-Tested at Real Volume
Pipelines are load-tested against your actual data volume and velocity, not a sample dataset, so performance holds up when production traffic hits.
Warehouse-First Architecture
Snowflake, BigQuery, or Redshift is the source of truth we design around, so every downstream report and model draws from one consistent dataset.
Data Governance Built In
Schema validation, lineage tracking, and access controls are part of the pipeline design, not a compliance afterthought bolted on post-launch.
Platform-Agnostic Engineering
We work across AWS, Azure, and Google Cloud data stacks and recommend the platform that fits your existing infrastructure, not the one we're used to selling.
Everything Your Data Platform Actually Needs
From the first pipeline to ongoing performance tuning, here's what a complete big data analytics engagement with Odidor covers.
Data Pipeline Architecture
End-to-end ingestion pipelines that move data from source systems into a governed warehouse reliably and on schedule.
ETL & ELT Development
Extract, transform, and load processes that clean and reshape raw data into analytics-ready tables.
Data Warehousing
Snowflake, BigQuery, or Redshift architecture designed around your query patterns and reporting needs.
Batch Processing
Apache Spark jobs that process large historical datasets efficiently on a scheduled cadence.
Real-Time Streaming
Kafka-based streaming pipelines for data that needs to be queryable seconds after it's generated, not hours.
Data Modeling
Dimensional and star-schema modeling that keeps queries fast as your dataset and user base grow.
Data Quality & Validation
Automated schema checks and validation rules that catch bad data before it reaches a report.
Analytics Dashboarding
Query-optimized tables feeding the BI layer your team already uses, built for fast, reliable reporting.
Data Lake Integration
Structured and unstructured data unified into a single queryable layer alongside your warehouse.
Legacy Data Migration
On-prem databases and spreadsheet-driven processes migrated into a scalable cloud data platform.
A Process Built for Data You Can Trust
A structured pipeline that takes your data infrastructure from scattered sources to a validated, production-grade warehouse.
Assess
We inventory your existing data sources, formats, and volume against the analytics outcomes your business actually needs.
- Data source & format audit
- Volume & velocity assessment
- Governance & compliance review
Design
Warehouse schema, pipeline architecture, and processing model — batch, streaming, or both — are designed before any pipeline code is written.
- Schema & data model design
- Pipeline architecture plan
- Governance & access model
Build
ETL pipelines, warehouse infrastructure, and processing jobs are built and validated against real data, not synthetic samples.
- ETL / ELT pipeline development
- Warehouse & storage setup
- Batch & streaming job builds
Validate & Deploy
Data quality checks and load testing at production volume happen before the pipeline goes live and reports start depending on it.
- Data quality & reconciliation testing
- Load testing at production volume
- Production cutover
Monitor & Optimize
Pipeline monitoring, query performance tuning, and cost optimization continue as data volume and reporting needs grow.
- Pipeline health monitoring
- Query & cost optimization
- Ongoing schema evolution
The Data Stack We Build On
A focused set of platforms chosen for what they're actually good at across warehousing, batch processing, and streaming.
Snowflake
Cloud data warehouse that scales storage and compute independently for teams with unpredictable query load.
Databricks
Unified platform for large-scale data engineering, Spark processing, and analytics on the same lakehouse.
Apache Spark
Distributed processing engine powering our large-scale batch and iterative data transformation jobs.
Apache Kafka
The streaming backbone for pipelines that need data queryable seconds after it's generated.
Google BigQuery
Serverless warehouse option for teams already standardized on Google Cloud infrastructure.
Amazon Redshift
AWS-native warehouse for teams that want their analytics stack close to existing AWS infrastructure.
What a Well-Built Data Platform Does for Your Business
Beyond the pipeline itself, here's the measurable impact governed, scalable data infrastructure has on the metrics that matter.
5-10x
Faster Query Performance
Proper warehouse modeling and indexing turn reports that took minutes into queries that return in seconds.
-30%
Lower Data Infrastructure Cost
Right-sized warehouse compute and storage tiers cut cloud spend compared to unmanaged, ad hoc pipelines.
7-10 wks
Faster Time-to-Insight
Typical window from kickoff to a production pipeline feeding your first governed reports.
+90%
Higher Data Trust
Automated validation and reconciliation checks dramatically cut the 'which number is right' conversations.
Data Platforms Built for Your Industry
We've built data pipelines across industries with very different volume, compliance, and reporting demands.
Retail & E-commerce
Customer, inventory, and transaction data unified for demand forecasting and personalization.
Financial Services
Governed, auditable pipelines for risk, transaction, and regulatory reporting workloads.
Healthcare
Patient and operational data pipelines built with privacy and compliance requirements in mind.
Manufacturing
Production, supply chain, and quality data consolidated for plant-level and enterprise reporting.
Logistics & Supply Chain
Shipment, fleet, and inventory data streamed into a single analytics layer for real-time visibility.
Media & Entertainment
Engagement and consumption data pipelines feeding content and audience analytics at scale.
Big Data Analytics Questions, Answered
Straight answers to the questions we hear most often from teams scoping a data pipeline or warehouse project.
Once your data pipeline is flowing, pair it with our Business Intelligence work to turn a governed warehouse into dashboards and KPI reporting your team actually uses.
Ready to Turn Your Data Into an Asset?
Tell us where your data lives and what you're trying to learn from it. We'll scope the pipeline architecture, warehouse, and timeline it takes to get it governed and queryable.