Dimodelo Data Warehouse Studio
for Azure Synapse Analytics

Quickly deliver a modern Data Warehouse on the Azure Synapse Analytics platform. Respond to your most demanding users without compromising on design. Dimodelo Data Warehouse Studio is a dedicated Data Warehouse development tool that helps you easily capture your design and generate a best practice Data Warehouse architecture, utilizing Azure Data Lake, Polybase and Azure Synapse Analytics to deliver a high-performance, modern Data Warehouse in the Cloud.

Reduce risk, effort and cost with Data Warehouse Automation.

Design

Rapidly design a Data Warehouse schema and ETL in a single integrated project specifically designed for Dimensional Modelling. Keep your team on the same page through a consistent development environment.

Features

  • Configure Data Warehouse and Data Lake connections.
  • Define source system connections.
  • Design your Staging layers. Define new staging entities by importing table and column schema from source systems.
  • Pattern Driven ETL. Choose an ETL Pattern and define a source to target mappings.
  • Design Dimensions and Facts. Customize attributes, SCD types, parent-child hierarchies, measures, role plays, etc.
  • Supports advanced storage features like Columnstores, Distribution (Azure Synapse Analytics), Indexes, Data Lake File formats, Schema etc.
Watch this 2-minute fast-forward video to see Dimodelo Data Warehouse Studio design in action.
Watch this 1-minute fast forward video to see Dimodelo Data Warehouse Studio generation and deployment in action.

Generate and Deploy

Delight your business users by accelerating development cycles and delivering compelling analytics daily. A single click Is all it takes to generate and deploy consistent, standardized, best-practice code to implement your Azure Modern Data Warehouse.

Features

  • Dimodelo uses the captured design with pre-built generation templates to generate high-quality, consistent Data Warehouse and ETL code optimized for the Azure Synapse Analytics architecture.
  • Deploy changes with a single click to multiple environments (Dev, Test, Prod)
  • Dimodelo faithfully executes a non-destructive sync/update of databases, data lakes, and ETL. It reproduces name changes, data type changes, storage changes (indexes, Columnstore indexes, distribution), etc., and will automatically update schema and redistribution/reload data as required.
  • Integrates with Azure DevOps supporting source control (e.g. Git), work management and continuous integration through Pipelines.

Run

Dimodelo Data Warehouse Studio has a companion product that simplifies the scheduling, orchestration, monitoring, logging and analysis of ETL batches. 

Features

  • Orchestrate your ETL runs with workflows. Define Phases and Tasks with wildcards. 
  • Define multiple workflows for different processing schedules. 
  • Dimodelo captures the start time, end time, duration, insert/update count and errors for all tasks executed in an ETL run in a dedicated database. 
  • Use our PowerBI Dashboard to monitor the latest ETL run and analyse run history. 
  • Run ETL from the development tool or schedule on a server. 
Watch this 1-minute fast-forward video to see Dimodelo Data Warehouse Studio batch execution in action.

DevOps/DataOps

A Dimodelo Data Warehouse Studio project integrates with Azure DevOps, supporting source control (e.g. Git), work management and Pipelines.

Automate deployment with Continuous Integration and Continuous Deployment for Data Warehouses.

Portability

Easily port your Data Warehouse between SQL Server, Azure SQL Database and Azure Synapse Analytics Data Platforms.

With Dimodelo’s metadata approach, simply select a new target platform technology and regenerate.

Documentation

Improve, communicate and collaborate with your users. Dimodelo generates an HTML data dictionary that can be deployed to your web server or file system for access by end users.


 

Easy Migration

Have an existing Data Warehouse?

Dimodelo can import the schema and generate a project to migrate your schema and data from your existing data warehouse to a new Dimodelo Data Warehouse.


Deliver a Modern Azure Synapse Analytics Data Warehouse

Dimodelo Azure Synapse ETL Architecture

Dimodelo Data Warehouse Studio generates a modern Azure Synapse Analytics solution.

We’ve listened to the Microsoft Azure Synapse Analytics product team and implemented a best practice architecture.

Hybrid high-speed ingestion to Azure Data Lake

Loads Azure Synapse Analytics using Polybase.

ELT in Azure Synapse Analytics using plain old SQL.

Dimodelo Data Warehouse Studio maximizes the value Customers derive from Azure Data Platform’s scale and elasticity.

Simplicity Multiplied

Scale Wisely

Hybrid Cloud

  • Microsoft has simplified the Data Platform.
  • Dimodelo simplifies how you use it.

Outcome: Delivery in days

  • Azure delivers massive scale.
  • Dimodelo ETL Architecture uses it wisely.

Outcome: Reduce Azure resource costs

  • Microsoft leads in Hybrid cloud enablement.
  • Dimodelo distributes the workload.

Outcome: Use under-utilized on-prem resources

Data Management Architecture

Dimodelo implements an advanced data management architecture, including the following features:

  • Data Sources. Support any database, structured file, ODBC and SaaS (through ODBC) data source.
  • High-performance incremental extracts. Three methods of incremental extract.
    • Change identifier column(s) on the source,
    • Change Tracking (SQL Server)
    • Date Range.
  • Persistent staging. Record the full history of changes to the data of the table/query that is the source of the Persistent table.
    • All history, all the time, instead of some history some of the time. Keep every version of a row.
    • Improved ETL Performance. A persistent staging layer means higher layers can use incremental ETL.
    • Provide new types of accurate time-based analysis – e.g. Churn.
    • How often have you been asked, “why has my report changed”?
    • Provide an accurate history of change in your systems for auditors.
  • Adaptive ELT. Dimodelo recognizes changes in your Dimensional design and reloads attributes and measures with full history where required.
  • Ability to truncate and reload Dimensions and Facts with full history.
  • Virtual and materialized views to implement complex ETL/ELT Logic.
  • High performance, incremental ELT at all layers above Persistent staging.

Save with in-built high-speed ingestion and ELT

Save on Azure Data Brick clusters or Azure Data Factory pipeline costs. Utilize the compute capacity you already own.

Dimodelo uses its internal “Dimodelo Shift” ETL engine to implement the Extract part of the ETL process.  Dimodelo Shift runs either in the cloud or on-premise, supporting Hybrid cloud scenarios. Utilize unused on-premise capacity.

You pay for compute capacity with your Azure SQL Data Warehouse subscription. Why not use it? Dimodelo implements ELT, which means the heavy lifting of Transformation into the Persistent Staging and Dimensional layers is done using Polybase and plain old SQL inside Azure Synapse Analytics.