Databricks Brickbuilder DP2BRICKS Migration Accelerator

Dataproc to Databricks

A migration accelerator that streamlines the shift from Dataproc's cluster-heavy architecture to Databricks' unified compute model for a predictable, governed modernization workflow.

Why migrate from
Dataproc to Databricks?

Dataproc handles small pipelines well, but growing workloads lead to fragmented clusters, duplicated logic, and inconsistent runtimes. Manual dependency management slows delivery, rising volumes increase costs, and governance stays scattered.

Databricks solves this with unified compute and Delta-native governance, but reaching it requires a structured, automated migration pathway.

DP2BRICKS provides exactly that: a governed, automated modernization engine for Dataproc workloads.

Customer Challenges Addressed by DP2BRICKS

Fragmented Google Dataproc clusters with limited cross-team visibility

Fragmented clusters lead to limited cross-team visibility.

High operational overhead and pipeline maintenance.

Manual Spark logic in Dataproc causing metric drift and slow reporting

Manual logic causes metric drift, slow reporting, and unreliable analytics.

Lack of unified governance and lineage across GCP Dataproc workloads

Lack of unified governance and lineage makes scaling difficult.

Unable to estimate migration effort, cost, or timelines for Dataproc to Databricks migration

Unable to estimate migration effort, cost, or timelines, blocking modernization planning.

Core Capabilities

Spark Conversion & Optimization Engine

Transforms Dataproc Spark/Hive workloads into Databricks-ready formats, including Spark SQL rewrites, config normalization, Delta Lake adoption, and optimized cluster/job definitions.

Workload Classification & Dependency Mapping

Maps dependencies across jobs, libraries, JARs, UDFs, Airflow/Dataproc Workflow templates, and cluster configs, highlighting incompatibilities and grouping workloads for staged migration.

AI-Powered Intelligence

AI-driven insights to detect Spark anti-patterns, recommend efficient Databricks alternatives (Photon, Delta Lake, Auto-Optimize), and automatically fix common migration blockers.

Performance Monitoring & Alerts

Near real-time visibility into migration readiness with automated detection of performance bottlenecks, deprecated APIs, non-portable configurations, and required optimizations.

Data Governance & Security

Unity Catalog ensures secure and compliant handling of notebooks, SQL logic, libraries, job configurations, and lineage during migration.

Data Ingestion & Standardization

Ingestion of Dataproc Spark jobs, PySpark notebooks, Hive SQL scripts, and workflow metadata into a normalized structure for automated analysis and migration planning.

Technical Capabilities

Dataproc Workload Ingestion & Normalization

Automated ingestion of Spark jobs, PySpark notebooks, Hive scripts, workflows, init actions, and cluster configs, normalized into a unified model for scalable analysis and conversion.

Spark & Hadoop Code Translation Engine

AST and rule-driven translation of Dataproc Spark, PySpark, and Hive workloads into Databricks-compatible Spark, resolving API differences, deprecated constructs, and runtime semantics.

Cluster-to-Job Refactoring Framework

Refactors Dataproc’s cluster-centric model into Databricks Jobs and Workflows, mapping cluster properties, init scripts, and resources to job clusters or serverless compute.

Intelligent Error Classification & Self-Healing Execution

Automated execution of migrated workloads with structured error classification and LLM-assisted remediation, automatically fixing issues and retrying until successful execution.