Data Engineering Challenges Holding Enterprises Back. Mid-market organizations and large enterprises confront a shared strategic challenge: fragmented, unverified, and AI-unready data operating at different scales. While mid-market firms navigate ungoverned SaaS sprawl and inconsistent standards, global enterprises contend with monolithic legacy silos and decades of technical debt. Talk to us
Lakehouse Architecture and Design Platform selection and architecture across Databricks, Snowflake, BigQuery, and Microsoft Fabric, grounded in your workloads, your team, and your cloud estate, not in a vendor default. Talk to us
Open Table Formats: Delta Lake and Iceberg Delta Lake and Apache Iceberg implemented as the storage foundation, with ACID transactions, schema evolution, time travel, and cross-engine portability engineered in from day one. Talk to us
Medallion Architecture Bronze, silver, and gold layers that progressively refine raw data into business-ready datasets, with data quality enforced at every promotion and lineage traceable to source. Talk to us
Governance and Catalog Unity Catalog, Snowflake Horizon, or Microsoft Purview implemented for access control, lineage, discovery, and audit, so the lakehouse is governed from the first table, not retrofitted later. Talk to us
Warehouse Migration and Replatforming Legacy warehouse estates migrated to Databricks, Snowflake, BigQuery, or Fabric with schema conversion, SQL refactoring, and dual-running validation so results reconcile before cutover. Talk to us
SQL Engineering at Depth Complex stored procedures, views, and semantic layers refactored by engineers who have spent twenty years inside SQL engines, preserving business logic while modernizing the platform. Talk to us
Dimensional and Semantic Modeling Star schemas, semantic models, and metrics layers engineered so the KPI the CFO reads is defined once, governed centrally, and consistent across every dashboard and tool. Talk to us
Performance and Cost Engineering Partitioning, clustering, materialization, and warehouse right-sizing engineered so queries return in seconds and the monthly platform bill stays defensible. Talk to us
Streaming Ingestion Pipelines Apache Kafka, Azure Event Hubs, AWS Kinesis, and Google Pub/Sub ingestion engineered into Delta and Iceberg tables with exactly-once semantics and schema enforcement. Talk to us
PySpark Structured Streaming Real-time transformation and enrichment on Spark Structured Streaming, engineered in PySpark by teams who run it in production, with watermarking, stateful processing, and late-data handling. Talk to us
Real-Time Analytics Serving Streaming aggregates served to dashboards and applications with sub-minute freshness, replacing the nightly batch that made every morning report a day late. Talk to us
Operational Monitoring and Recovery Pipeline observability, dead-letter handling, replay, and automated recovery engineered in, so a stream that breaks at 2 a.m. heals without paging the business. Talk to us
Change Data Capture Engineering Log-based CDC from operational databases, SQL Server, Oracle, PostgreSQL, MySQL, into the lakehouse via Debezium, native connectors, and platform-native CDC, with ordering and consistency guaranteed. Talk to us
Incremental Merge Patterns MERGE-based upserts on Delta Lake and Iceberg, slowly changing dimensions, and watermark-driven incremental loads engineered in SQL and PySpark for correctness under concurrency. Talk to us
Pipeline Cost Optimization Full-reload pipelines converted to incremental, cutting compute consumption and bringing processing windows back inside SLA as data volumes grow. Talk to us
Backfill and Reconciliation Safe historical backfills and automated reconciliation checks, row counts, checksums, and distribution comparisons, so incremental correctness is proven, not assumed. Talk to us
Document and Content Pipelines Automated ingestion, parsing, and extraction across PDFs, Office documents, images, and scanned content, engineered in PySpark for volume and in production for reliability. Talk to us
Vector and Embedding Pipelines Chunking strategies, embedding generation, and vector indexing on Databricks Vector Search, pgvector, and platform-native stores, refreshed continuously as source content changes. Talk to us
Text, Audio and Log Engineering Call transcripts, support conversations, clickstreams, and machine logs structured into analyzable datasets that feed both BI and AI workloads. Talk to us
Governance for Unstructured Data PII detection and redaction, access control, and lineage applied to unstructured pipelines, so the content feeding your AI carries the same governance as your structured estate. Talk to us
Engineered on every major data platform. Sundew delivers data engineering across the four platforms that define the enterprise data landscape, with the platform choice grounded in your workloads, your team, and your cloud estate. 20yrs Inside SQL engines and data platforms 04 Platforms: Databricks, Snowflake, BigQuery, Microsoft Fabric 02 Open table formats by default: Delta Lake and Apache Iceberg
Turn fragmented data into a scalable, AI-ready engine. Stop spending 80% of your engineering bandwidth fixing broken pipelines and cleaning datasets. Speak with Sundew’s principal data architects to design a unified, low-latency data lakehouse engineered for real-time analytics and agentic AI. Talk to us