Databricks

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

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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.

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WhereScape Data Automation with Databricks

95 %

Time Savings

on hand-coding development, refactoring and management tasks.

8x

Developer Productivity

in implementing and managing infrastructure through automation.

6x

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.

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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.

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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.

Master Databricks with WhereScape RED: Automation at Every Layer

Supercharge your Databricks workflows with WhereScape RED, automating the entire pipeline process from ingestion to production. By utilizing the Medallion Architecture for raw, cleaned, and curated data, it ensures top-quality results. Seamless integration with Delta Lake’s ACID transactions and time travel boosts efficiency while minimizing manual coding.
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Modernize Your Approach to
Data Projects with WhereScape

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

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