Migration Assurance
Marzal Labs supports every migration phase, from planning and architecture through full migration onto the Lakehouse, including optimization and scaling guidance.

Marzal Labs provides delivery expertise, implementation support, and proven best practices to help partners execute seamless customer outcomes and long-term growth on the Databricks Data Intelligence Platform.
Engaging Marzal Labs can be a strong differentiator in complex or competitive customer scenarios. We help partners secure early wins, deliver immediate impact, and demonstrate Data Intelligence Platform value while scaling their Databricks practice with confidence.
Marzal Labs provides these Databricks-aligned offerings to accelerate customer success through partner collaboration.
Marzal Labs supports every migration phase, from planning and architecture through full migration onto the Lakehouse, including optimization and scaling guidance.
Marzal Labs configures Unity Catalog as the unified governance layer for data and AI assets, including design and migration assurance for up to three workspaces using best-practice architecture patterns.
Marzal Labs provides tailored options for each stage of the AI journey, from starter advisory services to LLM POCs and advanced machine learning support.
Marzal Labs delivers architecture and best-practice assessments, architectural design reviews, and enterprise target architecture design and implementation plans.
Marzal Labs offers a strategic assurance model that aligns expertise to priority industries or delivery portfolios, with pre-paid support drawdown across multiple engagements.
Marzal Labs offers a flexible custom engagement model for specific activities or day allocations tailored to customer accounts and project requirements.
Retire IBM Netezza (PureData) onto the Lakehouse. We inventory databases, NZPLSQL and stored procedures, convert schemas and ELT logic to Delta Lake and Spark SQL, migrate history, and validate row and aggregate parity before cutover.
Modernise Teradata EDW to Delta Lake. We translate BTEQ, stored procedures and SQL-MR, re-engineer primary-index models for the Lakehouse, migrate data with full reconciliation, and rebuild downstream BI.
Move Oracle and Exadata workloads to Databricks. We convert PL/SQL packages, materialised views and scheduler jobs to Spark SQL and Workflows, migrate historical data, and reconcile results against source.
Exit on-prem Hadoop (HDFS, Hive, Impala, Spark, MapReduce). We re-platform the Hive metastore to Unity Catalog, convert Hive and Impala SQL and legacy Spark jobs, land data as Delta in cloud storage, and decommission the cluster.
Convert SAS to Databricks. We translate DATA steps, PROC SQL and macros into PySpark and Spark SQL, reproduce statistical and reporting logic, and re-point outputs to the Lakehouse and BI tools.
Migrate SQL Server and other relational databases. We convert T-SQL, stored procedures and SSIS packages to Spark SQL and Databricks Workflows, migrate schema and data, and validate parity end to end.
Migrate Snowflake to Databricks. We map schemas, convert SQL, tasks, streams and stored procedures, move data and history, and rebuild governance in Unity Catalog with cost and performance tuning.
Move Azure Synapse, ADLS and legacy Azure platforms to the Databricks Lakehouse on Azure. We re-engineer Data Factory pipelines, convert to Delta, and establish Unity Catalog governance.
Migrate Amazon EMR to Databricks. We port Spark, Hive and Presto workloads, convert job configurations, re-platform data to Delta on S3, and modernise orchestration.
Migrate Amazon Redshift to Databricks. We convert Redshift SQL, UDFs and COPY and UNLOAD pipelines, migrate data, and rebuild BI on the Lakehouse with performance validation.
Migrate Google BigQuery to Databricks. We translate BigQuery SQL and scheduled queries, move datasets, and re-establish governance and reporting on Delta Lake.
Deliver production generative AI on Databricks. RAG pipelines, Mosaic AI Vector Search, model serving and evaluation, with guardrails, monitoring and MLflow lifecycle management on your own governed data.
Stand up Databricks AI/BI dashboards and Genie natural-language analytics. Curated semantic models, certified metrics and governed self-service so business users can ask questions in plain language.
Bring cost and platform observability to Databricks. System-table dashboards for usage, job health and spend, budget alerts, cluster and SQL-warehouse right-sizing, and FinOps governance to keep consumption predictable.
From a first production ML MVP to a full MLOps foundation. Feature engineering, MLflow tracking, model registry, CI/CD and automated retraining and monitoring on Databricks.
Stand up a greenfield Lakehouse. Medallion (bronze, silver, gold) architecture, ingestion frameworks, Delta standards, Unity Catalog and Workflows, delivered as a reusable, governed platform.
Build operational data apps on Databricks. Lakebase (Postgres-compatible OLTP) and Databricks Apps to serve low-latency, transactional experiences directly on Lakehouse data.
Migrate legacy Hive metastore and workspace-level access into Unity Catalog. Unified catalog design, lineage, fine-grained access control and audit across data and AI assets, for up to three workspaces.
Establish a governance operating model on Unity Catalog. Catalog and schema standards, data classification, access policies, data quality and lineage, and stewardship processes tuned for regulated environments.
Talk to Marzal Labs to scope Databricks-aligned assurance, migration, governance, and GenAI delivery services for your partner and customer scenarios.