Automated deployment of Databricks pipelines
Move from development to production environments faster
& easier with automated deployment of data pipelines to
Databricks clusters.

Data Migration to Databricks

Trying to keep up with increasing data from multiple sources? Facing high reporting and analytics costs? A Data Lakehouse may be the solution—but migrating your current processes into Databricks can come with a myriad of unforeseen technical requirements, learning curves, and risks of project failure.

See how WhereScape can speed up the delivery of a migration.

WhereScape Data Automation with Databricks

95 %

Time Savings

on hand-coding development, refactoring and management tasks.


Developer Productivity

in implementing and managing infrastructure through automation.


Return on Investment

by avoiding failures, filling skill gaps and adding built-in best practices.

Stress-Free Deployments with

Data Lakehouse Automation

WhereScape simplifies data workflow orchestration, scalability, and monitoring in Databricks by automating the deployment and management of data lakehouses.

Near-Limitless Scalability with

Databricks Pipelines Automation

WhereScape automates end-to-end data pipeline development in Databricks for easier data ingestion, transformation, and loading into Databricks clusters.

Benefits of Databricks

Databricks Lakehouse Architecture:

Databricks Lakehouse Architecture blends the best of data lakes and data warehouses to create a unified, high-performance system, providing enterprise-level security, access control, data governance, auditing, retention, lineage, and data discovery tools.

  • Transactional Support: Ensures data consistency with ACID transactions, enabling concurrent reads and writes.
  • Schema Enforcement and Governance: Supports complex schema architectures like star/snowflake-schemas, with robust governance and auditing mechanisms to maintain data integrity.
  • BI Tool Integration: Enables direct use of BI tools on source data, reducing latency and operational costs by eliminating the need for multiple data copies.
  • Decoupled Storage and Compute: Facilitates scalability by using separate clusters for storage and compute, accommodating more users and larger datasets.
  • Openness: Utilizes open, standardized storage formats (like Parquet) and APIs, allowing diverse tools and engines to access data efficiently.
  • Support for Diverse Data Types: Capable of handling structured, semi-structured, and unstructured data, including images, videos, audio, and text.
  • Diverse Workload Support: Accommodates various applications, from SQL analytics and real-time monitoring to data science and machine learning.
  • End-to-End Streaming: Supports real-time data applications, eliminating the need for separate systems for streaming data.

WhereScape with Databricks:

Databricks’ unique Medallion Architecture provides a streamlined, scalable approach to data organization within a lakehouse. This architecture progressively enhances the structure and quality of data as it flows through three distinct layers:
  • Bronze layer: Captures raw data from external sources while maintaining source system structures and vital metadata for historical archiving and auditability.
  • Silver layer: Data is cleansed, matched, and merged. This layer supports self-service analytics, ad-hoc reporting, and advanced analytics, prioritizing speed and agility.
  • Gold layer: Offers consumption-ready, curated business-level tables optimized for reporting and complex analytics projects.

Additional Databricks Features:

  • Unity Catalog: The industry’s only unified and open governance solution for data and AI.
  • Built on Apache Spark: Offers high performance for batch and streaming data, analytics capabilities, seamless integration.
  • Delta Lake and Apache Iceberg: Open-source table formats, offering reliability to data lakes with ACID transactions and metadata handling.
  • Delta Live Tables: Simplifies the construction of reliable data processing pipelines.
  • Collaborative Notebooks: Support for multiple programming languages and real-time collaboration.
  • Machine Learning Capabilities: MLflow and AutoML for the entire ML lifecycle, from experiment tracking to deployment.
  • Generative AI: Optimized for specific tasks, offers deployment solutions, balancing accuracy and efficiency.
  • Databricks Assistant: Query data through a conversational, context-aware AI assistant.

Modernize Your Approach to
Data Projects with WhereScape

WhereScape makes owning your data easy. See what you can achieve with WhereScape today.