Most SMBs are sitting on valuable data but lack the infrastructure, tools, or talent to unlock it. Datallies bridges that gap — delivering enterprise-grade data engineering, analytics, and AI platforms sized and priced for businesses like yours. No bloat. No hand-holding. Just results.
About Us
Datallies is a boutique data consultancy built for one purpose: making modern data infrastructure accessible to small and medium businesses. We believe great data engineering shouldn't require a 50-person internal team or a Big Four budget. Our senior engineers and analysts work hands-on with your team — building pipelines, platforms, and products that turn raw data into your most valuable asset.
From greenfield data platform builds to cloud migrations and production-grade ML pipelines, we bring deep expertise across the full modern data stack. We work across cloud and on-premise environments, adapting to your constraints — not the other way around. Every engagement is led by experienced practitioners, not junior consultants. You get direct access to the people who actually build and own your solution.
What We Do
End-to-end data services to help you build, scale, and optimize your data infrastructure.
We design and build production-ready data pipelines, ETL/ELT workflows, and streaming architectures that move your data reliably from source to insight. Using battle-tested tools like Apache Airflow, Kafka, Spark, and dbt, we build infrastructure that scales with your business — without the complexity tax.
Not sure where to start — or what's broken? We audit your current data landscape, define a clear roadmap, and help you make the right technology choices before you invest. We translate business goals into actionable data architecture decisions your team can actually execute.
Turn your raw data into dashboards, reports, and self-serve analytics your team actually uses. We build semantic layers, define metrics frameworks, and deploy BI tools (Power BI, Metabase, Superset) tailored to your workflows — so every decision in your company is backed by real numbers.
From feature engineering and model training to deployment and monitoring, we build end-to-end ML platforms on your infrastructure. Whether it's churn prediction, demand forecasting, or recommendation systems, we use modern tools like Feast, MLflow, and Airflow to bring AI to production — not just notebooks.
Moving your data workloads to AWS, Azure, or GCP? We handle the full migration lifecycle — schema mapping, pipeline re-engineering, cost optimization, and go-live — with minimal disruption to your operations. We design for performance and cost efficiency from day one.
Cloud isn't always the right answer. We architect and deploy robust data platforms on your own hardware — from local Spark clusters and PostgreSQL warehouses to containerized pipelines with Kubernetes. Full control, no vendor lock-in, and the same modern engineering standards we apply in the cloud.
Our Work
A fast-growing online retailer was drowning in siloed data across 5 tools. We built a centralized platform on AWS with Airflow-orchestrated pipelines and dbt — delivering a 60% reduction in time-to-report.
Built a streaming pipeline with Kafka and Spark landing events into Snowflake, with a Metabase dashboard for the customer success team. Churn identification time dropped from weeks to hours.
Deployed an on-premise feature store (Feast + Redis) feeding an ML model for route optimization, reducing last-mile delivery cost by 18% — fully compliant, zero cloud dependency.
Migrated legacy SQL Server warehouse to Snowflake with 200+ dbt models — introducing testing, docs, and version control. Data freshness improved from daily to hourly.
Insights
Lakehouse architectures combining the flexibility of data lakes with the reliability of warehouses are no longer just for Netflix and Uber — here's how to implement one at SMB scale using Apache Iceberg and Delta Lake.
Getting dbt right in production requires more than just writing SQL. We share our playbook for structuring dbt projects, writing meaningful tests, and avoiding the most common pitfalls across a dozen client deployments.
Power BI, Metabase, Superset, Looker, Tableau — we break down the real trade-offs for SMBs: total cost of ownership, SQL literacy requirements, embedding options, and which tools we actually reach for.
Get In Touch
Whether you're starting from scratch or untangling years of technical debt, we'd love to understand your data challenges. Drop us a message and a senior Datallies engineer will get back to you within 24 hours — no sales team, no runaround.