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Cloud Data Lake & Warehouse Design
We architect and implement cloud-native data lakes and warehouses on Databricks, Snowflake, AWS Redshift, and Azure Synapse — purpose-built for your scale, query patterns, and budget requirements
Data Engineering
Our engineers work across the full data stack — from raw source ingestion to clean, queryable, analytics-ready layers — so your downstream teams always have what they need.
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From robust ETL/ELT orchestration and distributed storage to real-time stream processing and high-performance warehousing, ScatterPie engineers every data pipeline across your technical estate — giving you the structural integrity to ingest, transform, and deliver pristine data at any scale.
The global data landscape is shifting toward a "latency-zero" reality — defined by mesh architectures, serverless computing, and the transition from batch processing to continuous, event-driven flows. The technology leaders and data-driven organizations that will dominate this era are those moving beyond fragile legacy systems to build resilient, self-healing data foundations. ScatterPie is their infrastructure partner.
We architect and implement cloud-native data lakes and warehouses on Databricks, Snowflake, AWS Redshift, and Azure Synapse — purpose-built for your scale, query patterns, and budget requirements
We build robust, monitored data pipelines that reliably move and transform data from every source — CRMs, ERPs, APIs, flat files, and streaming platforms — into a single, governed data layer
When batch is not fast enough, we architect real-time data streams using Apache Kafka, Azure Event Hubs, and AWS Kinesis — enabling sub-second analytics for operational dashboards and AI models.
We safely migrate legacy on-premise data warehouses and complex ETL workflows to modern cloud platforms — preserving business logic while dramatically reducing operational cost and complexity.
Combining the flexibility of data lakes with the performance of warehouses, we implement Delta Lake and Databricks Lakehouse architectures that support analytics, ML, and operational workloads from a single platform.
Data pipelines that no one monitors are pipelines waiting to fail. We implement DataOps practices — automated testing, alerting, lineage tracking, and SLA monitoring — so your data stays healthy and reliable.
