Find answers to the most common questions about using AnalyticsCreator—features, integrations, and how it helps you accelerate data warehouse automation. Use the menu on the left to browse.

AnalyticsCreator is a metadata-driven data warehouse automation platform that enables rapid development and deployment of modern data architectures. It orchestrates the end-to-end data lifecycle—from source ingestion and transformation to semantic modeling and analytics delivery—supporting data warehouse, data lakehouse, and data mesh patterns across on-premises and cloud-native environments.

AnalyticsCreator is designed for data platform teams including data engineers, solution architects, and BI developers who need to accelerate data pipeline development, enforce governance, and maintain agility in evolving data ecosystems. It supports centralized and decentralized data ownership models, making it suitable for organizations implementing DataOps, federated data governance, or self-service analytics at scale.

AnalyticsCreator supports a broad ecosystem of data services and tools, including:

• Data Platforms: Microsoft SQL Server, Azure SQL, Azure Synapse Analytics, Databricks, Oracle, Snowflake (via export).
• Ingestion & Orchestration: SSIS, Azure Data Factory, REST APIs, CData connectors (250+ sources), Theobald for SAP, and custom ingestion layers.
• Analytics & BI Tools: Power BI (incl. Premium), Tableau, Qlik Sense, Excel.
• File & Object Storage: Azure Blob Storage, CSV, Parquet, Avro formats.
• APIs & SaaS Connectors: REST, OData, SharePoint, Salesforce, Dynamics 365, Google Ads, and others.

AnalyticsCreator supports both cloud-native and hybrid deployments. It generates infrastructure-as-code artifacts (e.g., ARM templates, DACPACs, SSIS packages) for seamless CI/CD integration. While the design-time environment runs on Windows, the generated workloads are fully compatible with Azure PaaS services and traditional on-prem SQL Server environments.
Customization is key, and our module offers extensive options to personalize the navigation bar to match your brand's aesthetic and style. From color schemes to font choices, you have full control over the look and feel of the navigation experience.

AnalyticsCreator supports modern and classical modeling methodologies, including:

• Data Vault 2.0 (with automation of Hubs, Links, Satellites, and historization logic)
• Dimensional modeling (Kimball)
• 3rd Normal Form (Inmon)
• Hybrid modeling (combining Data Vault and Kimball for agility and performance)
• Custom semantic layers
• Model-driven development (top-down, bottom-up, or reverse-engineered from source systems)

Yes. AnalyticsCreator supports delta processing and micro-batch loads with near real-time frequency. While it does not perform streaming ETL, it can integrate with near real-time ingestion platforms (e.g., Azure Data Factory, Databricks, or message queues) to automate downstream transformation and modeling logic.

No proprietary runtime engine is required. AnalyticsCreator generates standard artifacts such as T-SQL scripts, ADF2/SSIS packages, semantic models, and deployment templates. These can be executed independently using native tools (SQL Server Agent, ADF, etc.), ensuring portability and reducing platform lock-in.

AnalyticsCreator supports full DevOps enablement through multi-environment deployment (Dev/Test/Prod), environment-parameterization, and integration with source control systems. It generates deployment-ready packages, supports rollback and failure recovery in ETL processes, and aligns with CI/CD pipelines through infrastructure-as-code templates.

Yes. AnalyticsCreator includes design patterns for data anonymization, pseudonymization, and masking to support GDPR/DSGVO and other regulatory frameworks. Sensitive data handling can be integrated into data pipelines and modeling layers to enforce compliance and data governance policies.

No. AnalyticsCreator does not require a runtime engine. All generated objects—SQL scripts, models, ETL packages—are native to the platform they’re built for (e.g., SQL Server, ADF, SSIS) and can be executed independently.

Yes, the platform allows you to export your metadata repository, which contains the entire development model, as a JSON file. This file can then be used for:

• Azure DevOps
• GitHub
• Other Git-based repositories

This supports branching, versioning, and CI/CD integration

Yes. AnalyticsCreator enables the creation of modular, reusable data products by packaging source metadata, transformation logic, business rules, and semantic models into self-contained, version-controlled artifacts. Particular attention is given to data ownership and its access rights. These can be deployed across environments and consumed by BI tools or data services—supporting both traditional data warehousing and modern data product architectures such as data mesh.

AnalyticsCreator supports a wide range of structured and semi-structured data sources. In general, AnalyticsCreator can integrate all data sources that Microsoft makes available in its development environment for the respective target database (Visual Studio SQL DB). Native connectors and metadata integration are available for:

• Relational databases: SQL Server, Oracle, SAP ERP, SAP S/4HANA (via Theobald), MySQL, DB2, Netezza
• Cloud storage and file formats: Azure Blob Storage, CSV, Parquet, Avro
• SaaS and API sources: REST APIs, OData, Salesforce, Google Ads, SharePoint, Dynamics 365
• Third-party integration: 250+ sources via CData connectors (e.g., Snowflake, BigQuery, Facebook, LinkedIn)

Metadata connectors can also be manually generated to describe structure, primary keys, referential integrity, and descriptions—enabling semantic integration at ingestion.

AnalyticsCreator is metadata-centric by design. It maintains a centralized metadata repository in SQL Server, capturing technical, business, and operational metadata. It supports full data lineage, impact analysis, and automated documentation generation in formats such as Word, Visio, and exports to Azure DevOps or GitHub for traceability.

Yes. AnalyticsCreator supports automatic detection of structural changes in source systems and provides options for automatic adaptation or manual impact review. For example, if a source table is altered (e.g., a column is added or renamed), the system flags the change and can propagate it across the data pipeline—ensuring data models and ETL logic remain consistent.

AnalyticsCreator supports both batch and micro-batch integration patterns:

• Delta loads using change detection or CDC (Change Data Capture)
• Full loads for initial ingestion or small datasets
• Near real-time ingestion through external orchestration (e.g., ADF or SSIS with frequent triggers)
• Layered architecture: Raw/staging → harmonization → business logic → semantic model

Additionally, both push and pull integration models are supported for analytical frontends like Power BI, Qlik and Tableau.

AnalyticsCreator provides both automated and customizable transformation options:

• Predefined, datatype-aware transformations
• Transformation Wizard for rapid development
• Manual transformations using SQL views, stored procedures, and scripts
• ETL logic generation through SSIS packages or Azure Data Factory pipelines
• Support for calculated columns within staging or DWH layers

This enables flexible implementation of business logic, data cleansing, enrichment, and standardization.

Yes. While AnalyticsCreator provides its own internal ETL orchestration capabilities (via SSIS workflows), it fully supports integration with external orchestrators like:

• Azure Data Factory
• SQL Server Agent
• Custom schedulers or workflow engines

Workflows can then be triggered on schedule or conditionally based on dependencies, enabling integration into enterprise-wide data operations.

All integration logic, including source metadata, transformations, and workflow definitions, is version-controlled within AnalyticsCreator’s repository. Users can:

• Use embedded versioning tools or integrate with Azure DevOps or GitHub
• Automatically generate documentation in Word or Visio
• Export ETL metadata for audit and governance purposes
• Leverage macro language and scripting to standardize reusable integration patterns

AnalyticsCreator primarily operates on a schema-on-write basis, aligning with traditional data warehouse architectures. However, it supports flexible source metadata ingestion and allows custom logic or ingestion layers for semi-structured data, enabling integration with schema-on-read data lakes where applicable (e.g., via Azure Blob Storage with CSV, Parquet, or Avro files).

In AnalyticsCreator, a data product is a packaged, version-controlled deliverable composed of source-aligned data, business logic, metadata, and semantic models—ready for consumption by analytics tools or data services. It adheres to the principles of discoverability, trustworthiness, reusability, and ownership, supporting both centralized data warehouse and decentralized data mesh architectures.

AnalyticsCreator supports data product creation by automating:

• Source metadata ingestion
• Standardized transformation logic
• Multi-layer modeling (staging, harmonization, business vault, and presentation)
• Semantic layer generation for Power BI, SSAS, Tableau, and Qlik
• Version-controlled packaging and deployment artifacts

Each data product can be independently developed, tested, and deployed across environments using predefined templates, ensuring scalability and consistency. Particular attention is given to data ownership and its access rights.

Yes. AnalyticsCreator supports domain-oriented development through:

• Multi-user support 
• Modular repository structure, enabling team-based ownership of data domains
• Object-level locking and embedded version control
• Integration with Git and Azure DevOps for decentralized development workflows

This aligns with data mesh principles, allowing domain teams to own, evolve, and deploy their data products independently while conforming to enterprise standards.

AnalyticsCreator generates deployable artifacts including:

• SQL DB/ SQL Server objects (views, tables, stored procedures)
• SSIS packages / ADF pipelines
• Power BI datasets and semantic models
• Tabular and multidimensional SSAS cubes

These can be deployed using CI/CD pipelines and consumed by analytics platforms or downstream services, turning each product into a reusable and reliable data asset.

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Customization is key, and our module offers extensive options to personalize the navigation bar to match your brand's aesthetic and style. From color schemes to font choices, you have full control over the look and feel of the navigation experience.

Absolutely. AnalyticsCreator supports the full data product lifecycle, including:

• Metadata-driven modeling and transformation
• Environment-specific configuration for Dev/Test/Prod
• Manual detection of source schema changes during metadata refresh
• Custom pre- and post-deployment scripts for validation and testing

The headline and subheader introduce the essence of your message, while the following text elaborates on the value proposition. Here, you have the opportunity to convey why your offering stands out and why it's compelling for your audience. At amwhiz, we're dedicated to delivering exceptional solutions tailored to meet your needs. Our commitment to excellence drives everything we do, from the innovative products we develop to the outstanding service we provide. With our vertically fixed navigation bar, you can enhance user experience and streamline navigation on your website. The navigation bar remains fixed vertically, ensuring easy access to important links and content as users explore your site.

Customization is key, and our module offers extensive options to personalize the navigation bar to match your brand's aesthetic and style. From color schemes to font choices, you have full control over the look and feel of the navigation experience.

AnalyticsCreator automates data product creation by generating all components required for a governed, reusable, and deployable data asset. This includes:

• Ingesting and modeling source metadata
• Automatically generating staging, business, and semantic layers
• Creating output models for tools like Power BI, SSAS Tabular, Tableau, and Qlik
• Generating all required deployment artifacts (SQL scripts, SSIS packages, ADF pipelines)

Developers can use wizards, templates, and parameterized configurations to build reusable product definitions that are consistent across environments and domains.

Metadata is foundational to how AnalyticsCreator defines and manages data products. When a developer connects to a source system, AC automatically extracts metadata—such as table structures, relationships, keys, and data types—into its repository. This metadata:

• Drives the generation of ETL logic and data models
• Enables automated lineage visualization
• Supports semantic model generation
• Facilitates consistent governance across domains

This metadata-driven approach ensures that every data product is traceable, consistent, and versionable.

Yes. AnalyticsCreator is well-suited for organizations adopting Data Mesh principles. It enables domain-oriented teams to build and manage their own data products by providing:

• Modular, metadata-driven repositories
• Multi-user support with object-level access and locking
• Reusable modeling and transformation templates
• Environment-specific deployment pipelines

Access for domain teams can be granted both horizontally and vertically. This means that access can be restricted at specific medallion layers, within domain layers, or vertically across the entire data flow — from the source to the presentation layer

In AnalyticsCreator, data models are created at both the conceptual and logical levels using a combination of top-down and bottom-up approaches. Metadata is extracted from data sources and serves as the basis for data warehouse modeling. Smart wizards and templates then use this metadata to suggest modern data warehouse and analytics models and architectures.

AnalyticsCreator supports a range of Data Modelling strategies:

• Dimensional modeling (Kimball)
• Data Vault 2.0, with automated hub, link, and satellite generation
• 3NF Normal Form (Inmon)
• Hybrid models combining Data Vault and Star Schemas
• Medallion Architecture (Bronze, Silver and Gold)
• Custom models designed manually

AnalyticsCreator automates data model creation through:

• Collective Intelligence approach by AnalyticsCreator
• Data Sources metadata extraction and the recognition of relations.
• Collective Intelligence. (Build-in knowledge from over ten- thousand projects
• Cognitive suggestion

Yes. AnalyticsCreator supports:

• Top-down modeling – starting from business requirements and designing the model first.
• Bottom-up modeling – driven by existing meta-data structures in source systems.
• Mixed/hybrid approaches – combining automated discovery with additional manual design input

Yes. AnalyticsCreator offers native support for Data Vault 2.0 modeling standards, enabling fully automated generation of Data Vault artifacts directly from source metadata. This includes:

• Hubs –  Unique business keys stored with surrogate keys and load metadata (e.g., LoadDateTime, Record Source).
• Links –. Relationship entities that connect two or more Hubs, capturing business process associations.
• Satellites –  Descriptive attribute storage tied to Hubs or Links, supporting historization and change tracking (with fields like LoadDateTime, EndDate, RecordSource).
• Hash Key Generation – Supports customizable hash algorithms for primary keys and hash differences (HashDiff) for change detection.
• Point-in-Time (PIT) Tables – AC automates PIT table creation to enable efficient historical joins and time-based analysis. (See my comment on SCD/PIT)
• Bridge Tables – AC supports generation of Bridge Tables (link or hub-centric) for complex many-to-many relationships or hierarchy flattening. (I have seen Dimitri do this, he just didn’t call it Bridge tables)
• Metadata-Driven Business Rules – Optionally extendable into Data Vault Business Vault layers (e.g., derived PIT, calculated measures).

Surrogate Keys - AnalyticsCreator provides flexible and configurable options for surrogate key generation to support various data warehouse modelling approaches, including Data Vault 2.0 and Dimensional (Kimball) models.

Supported surrogate key types include:

• Auto-increment (Identity Columns): Standard SQL Server identity columns for sequential integer keys.
• Long Integer: Custom-configured long integer surrogate keys.
• Hash Keys: Hash-based surrogate keys, typically used for Data Vault Hubs and Links.

AnalyticsCreator is designed for continuous, iterative development. You can modify models, transformations, and metadata over time and generate deployment artifacts as often as needed. It fully supports incremental changes, multi-environment deployment (Dev/Test/Prod), and integration with CI/CD pipelines. This makes it suitable for both initial deployments and ongoing enhancement cycles throughout the data warehouse lifecycle.

You can implement custom data modeling logic, SQL code snippets, and manual transformations directly within AnalyticsCreator—without the need for external tools. AC supports both:

• Wizard-driven development for common patterns
• Manual coding for custom SQL logic, views, or stored procedures

AnalyticsCreator Macros are SQL-based, reusable code templates designed to help you standardize and automate complex logic across multiple projects. Written in standard SQL language, macros allow you to define dynamic, parameter-driven logic for transformations, calculations, or ETL processes.

Key features include:

• Reusability: Macros can be shared across all AnalyticsCreator projects, ensuring consistency in business rules and transformation logic.
• Modularity: Macros can reference other macros, making it easy to build layered, composable, and maintainable SQL components.
• Flexibility: They can be applied at different stages of the data pipeline, from staging layers to business logic and presentation models.
• Automation Support: Macros help minimize manual coding while allowing customization within AnalyticsCreator’s metadata-driven framework.

Yes, reverse engineering is possible with AnalyticsCreator, but it is not a fully automated process. There are currently two supported approaches: Manual Reverse Engineering Using SQL Code Snippets:

If your existing data warehouse is built on a Microsoft SQL Server environment, you can use AnalyticsCreator code snippet examples to help transforming and mapping your existing SQL logic into the AnalyticsCreator metadata repository.
 

The metadata repository in AnalyticsCreator is relational and straightforward to understand, making it possible for your team to perform most of the reverse engineering work with guidance and support from AnalyticsCreator consultants.

Second option:  

Leveraging AI and Large Language Models (LLMs):
If your existing data warehouse or data processes are spread across non-Microsoft platforms or heterogeneous environments, you can use Large Language Models (LLMs) (e.g., OpenAI, Azure OpenAI) to analyses your existing SQL code or metadata and generate transformation rules compatible with the AnalyticsCreator metadata repository.
This AI-assisted approach helps accelerate the translation of legacy ETL logic and data models into AC-compliant metadata structures.

When you open the AnalyticsCreator project, AnalyticsCreator will automatically interpret the metadata and show in the GUI the data model and transformations.

Yes, AnalyticsCreator offers two supported approaches for making post-deployment changes to your Data Warehouse or Data Lake:

1. Modify Generated Code in External Tools (e.g., Visual Studio)
You can manually adjust the code artifacts (e.g., SQL scripts, SSIS packages, ADF pipelines) generated by AnalyticsCreator and redeploy using Microsoft development tools like Visual Studio. However, this approach bypasses AnalyticsCreator’s metadata repository and breaks synchronization with the central model.

In this case, we strongly recommend using AnalyticsCreator’s built-in capabilities to orchestrate and control execution sequences directly within the tool. AnalyticsCreator allows you to define the execution order of custom scripts, procedures, and external code snippets as part of its workflow and deployment process. This ensures that your data pipelines remain consistent, traceable, and fully integrated into the overall metadata-driven lifecycle.

2. Modify Directly in AnalyticsCreator (Recommended)
The preferred and recommended method is to make all changes directly within AnalyticsCreator’s metadata-driven environment, then regenerate and redeploy the updated code.

Key Benefits of modifying in AnalyticsCreator:

All the work you invest in AnalyticsCreator modeling should be leveraged to generate your technical artifacts—ensuring you benefit fully from the it’s metadata-driven architecture. This approach guarantees:
Speed and Efficiency - Leverage AC’s automation and wizards for rapid and error-free changes.

• Centralized Change Management- Perform all modifications in one place—ensuring that all dependent layers, objects, and scripts are automatically updated across the model and technical deployment artifacts.
• Consistent Documentation and Data Lineage - Maintain accurate, up-to-date documentation, version control, and full data lineage tracking, ensuring alignment with governance and compliance standards.
• Minimized Risk of Manual Errors - Automated impact analysis ensures that downstream dependencies are consistently adjusted, reducing deployment risks.

In AnalyticsCreator, semantic layer modeling is fully metadata-driven and embedded as a core part of the data warehouse and data lakehouse development process.

Everything modelled in AnalyticsCreator is a semantic model definition— from business terms, data types, and relationships to measures, hierarchies, and transformations. All definitions are stored in the centralized AnalyticsCreator repositories as structured, human-readable metadata.

Core Principles of Semantic Layer Modeling in AnalyticsCreator:

Once the modeling is done all the modelling is done, the sematic layer with all its definitions is stored in the AnalyticsCreator meta data repository.
At the same time of modelling user can see the code which is created on the fly. The code is immediately visible in a intended place.

Using the AnalyticsCreator deployment process the deliverable packages are created for the target platforms.

Centralized Metadata Definition – All semantic definitions—including dimensions, facts, hierarchies, measures, and relationships—are maintained within AnalyticsCreator repository.This act as the single source of truth across both both physical storage layers (Staging, Core, Data Mart) and analytical frontends (BI tools).

Benefits of AnalyticsCreator’s Metadata-Driven Semantic Layer:

• Model-Once, Deploy-Many:  Design the semantic layer once and deploy it  to multiple BI Platforms simultaneously.
• Full Metadata Consistency: The  semantic layer stays  synchronized with the physical data warehouse and data lake structures, minimizing the risk of discrepancies.
• Automated Change Propagation: Any upstream changes to the data model (e.g., new measures, columns or relationships)  are automatically reflected in the regenerated semantic artifacts.
• Documentation and Governance Ready:  All semantic definitions contribute to automatically generated documentation, lineage views, and data governance processes

AnalyticsCreator offers a comprehensive, metadata-driven and semantic modelling environment, designed to manage even the most complex data warehouse and data Lakehouse architecture with efficiency, consistency and control.

Key Features for Managing Data ModelComplexity :

• Centralized Semantic Modeling Approach - AnalyticsCreator offers a semantic modeling layer  that enables users to define business-friendly entities, relationships, and metrics within a single metadata repository. This ensures that both technical and business users work from a common, reusable, and traceable data model definition.
• Unified Development Environment -  A single, consistent UI allows you to manage the entire data modeling lifecycle—from source metadata ingestion, through staging and business layers, to presentation and semantic layers (e.g., Power BI datasets, SSAS models).
• Metadata-Driven Automation - All object definitions, transformations,   relationships and historization rules are stored  as metadata. This enables automated artifact generation, standardization, and repeatable deployments across all environments,  reducing manual coding  and risk of inconsistencies.
• Reusability of Macros, Shared Objects and Templates- Users can create reusable objects, modeling templates, code macros, and scripts—supporting scalable development across multiple projects and layers, improving speed and consistency.
• Data Lineage and Dependency Tracking - A built-in graphical data lineage and dependency viewer allows users to trace object relationships across layers, helping to quickly assess the downstream impacts before implementing changes.
• Automated Documentation - AnalyticsCreator generates up-to-date technical documentation and data lineage diagrams automatically, ensuring alignment with governance and audit requirements.
• Version Control and Model Comparison - Built-in versioning tools allow users to compare model versions, track historical changes, and facilitate rollbacks when needed.

A metadata-driven approach, as implemented in AnalyticsCreator, represents a paradigm shift from manual, code-centric data warehouse development to model-based, declarative design. Instead of hand-coding SQL, ETL processes, or data structures, users define their data architecture, transformation logic, and business rules centrally as metadata objects.

Core Benefits of Metadata-Driven Development in AnalyticsCreator:

Modeling Instead of Programming:
Users design data flows, structures (such as Staging, Core, Data Mart layers), business rules, and historization logic (e.g., SCD types) through a graphical interface.
All definitions are stored as metadata and serve as input for automated code generation.

• Automated Code Generation for Multiple Targets: From this metadata, AnalyticsCreator automatically generates all required technical artifacts—such as SQL scripts, Views, Stored Procedures, SSIS packages, Azure Data Factory pipelines, —targeted for platforms like Microsoft SQL Server, Azure Synapse Analytics, and Microsoft Fabric.
• Standardization and Governance:
  Centralized metadata definitions enforce naming conventions, logging standards, error handling, and data quality checks, ensuring architectural consistency and regulatory compliance across projects.
• Reusability and Maintainability:
  Changes made at the metadata level—such as field type updates or new business rules—automatically propagate throughout the entire architecture.
  This eliminates the need for manual code adjustments across dozens or hundreds of objects.
  Reusable templates (e.g. surrogate key handling) improve development speed and reduce error rates.
• End-to-End Traceability and Documentation: AnalyticsCreator provides automatically generated technical documentation, data lineage diagrams, and business term mappings, directly derived from the metadata repository—critical for audits and governance.
• Support for Modern Data Modeling Paradigms: AnalyticsCreator fully supports Data Vault 2.0, Kimball dimensional modeling, and hybrid architectures, all modeled and generated from metadata definitions.

Advantages:

• Accelerated Development: High levels of automation reduce time-to-delivery and development costs.
• Controlled Change Management: Modifications can be quickly propagated, tested, and deployed across all environments (Dev, Test, Prod).
• Regulatory and Audit Readiness: Standardized processes, full documentation, and traceable lineage simplify audits and support Data Governance and GDPR/DSGVO compliance.
• Cloud and On-Premises Flexibility: Supports both on-premises SQL environments and modern cloud platforms.
  
  

A metadata-driven approach, as implemented by AnalyticsCreator, delivers substantial benefits across agility, scalability, governance, automation, and data quality— all critical factors for managing modern data platforms.

• Technology Independence Through Design-Time Abstraction:  By separating design and development from the underlying execution technology, AnalyticsCreator ensures that your data models and business logic remain independent of specific ETL engines, database platforms, or semantic layer tools.
  Example:
   A project originally deployed on SSIS can later be switched to Azure Data Factory (ADF), or an OLAP model can transition from Multidimensional (Dimensional Model) to Tabular (Power BI or Fabric)—without rewriting the entire solution.
• Reduced Dependency on Tribal Knowledge: Traditional, code-based data warehouse environments often suffer from inconsistent development practices as team members change over time.
  AnalyticsCreator enforces centrally defined modeling standards, minimizing the risk of fragmentation and undocumented logic.
• Faster Response to Change: New business requirements, schema changes, or performance optimizations  made in AnalyticsCreator are implemented centrally  at the metadata level  , then automatically propagated across all dependent layers and generated artifacts.
  Outcome:
   Improved time-to-insight reduced technical debt, and easier onboarding of new developers.
• Standardization and Governance:
  AnalyticsCreator provides a framework for standardized development, ensuring that naming conventions, logging, error handling, and data quality checks are uniformly applied across the entire solution.
• Transparency and Maintainability: With built-in data lineage, automated documentation, and dependency management, AnalyticsCreator ensures full transparency of data flows, making the entire architecture auditable and easier to manage over time.
• Accelerated Technology Migration:
  Switching to new platforms becomes a matter of regenerating the technical code from the same metadata definitions—without redesigning the data model.
  • Examples:
  • Migrating from SSIS to Azure Data Factory 2.0
  • Evolving from Dimensional Models to Tabular Models (Power BI/Fabric)
  • Transitioning from Azure Synapse Analytics to Microsoft Fabric

Yes, AnalyticsCreator supports data pipelining by enabling users to design, manage, and monitor data pipelines efficiently. The platform automates the creation of data integration, transformation, and loading processes, ensuring seamless data movement across systems. It provides a structured approach to pipeline development using a visual interface, allowing users to define data flows, dependencies, and execution logic. 

Data pipelines are automatically generated after using the DWH Wizard. You simply select your tables and fields, define your dimensions and measures, and the wizard takes care of creating both a draft version of your data model and the corresponding pipelines. Any changes to the model are automatically reflected in the pipelines. There's no need to worry about the underlying technology used to build them—you can choose that later.

Data Pipelining is a broader term that includes ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and other data movement and processing patterns.

While ETL focuses specifically on extracting data from source systems, transforming it, and loading it into target systems, a data pipeline can also include steps like:

• Data validation
• Enrichment
• Data replication
• Streaming ingestion
• Monitoring and error handling

AnalyticsCreator supports both ETL and broader data pipeline architectures, by automating:

• SQL-based ETL transformations
• SSIS package generation
• Azure Data Factory pipeline creation
• Metadata-driven orchestration across layers

Quick Comparison Table

Yes, AnalyticsCreator provides a unified pipeline modeling framework that abstracts different data pipeline technologies. Users can define a high-level pipeline model, and the platform automatically generates the corresponding implementation for various execution environments, such as SQL-based transformations, cloud-based data integration services, and on-premise data movement tools. This abstraction ensures flexibility and portability across different technologies.

Yes, AnalyticsCreator includes a no-code visual pipeline designer with a clickable GUI, allowing users to build complex data pipelines without writing code. The platform provides predefined templates, reusable components, and automatic metadata-driven transformations to streamline pipeline development. Advanced users can also extend pipelines with custom scripts or configurations if needed.

The skills required depend on the approach used:

• No-code approach: Basic understanding of data structures, business requirements, and pipeline design using the visual interface.
• Low-code approach: Familiarity with SQL and basic scripting to enhance automation and customization.
• Advanced approach: Knowledge of database systems, cloud data platforms, and orchestration tools to optimize and fine-tune complex pipelines.

Integration Layer: This layer is responsible for extracting data from various sources, standardizing formats, and loading it into a central repository.

Transformation Layer: This layer processes raw data by applying business logic, aggregations, filtering, and structuring to make it ready for analytics and reporting.

AnalyticsCreator automates both layers, ensuring a smooth flow from raw data ingestion to structured insights.

You can easily set up data profiling as a package in your pipeline—either during pre-deployment, post-deployment, or even within any specific layer of your project. This allows you to automatically analyze data quality, detect anomalies, and gain a deeper understanding of the structure and content of your data at any stage—before or after it's loaded into your target systems.

AnalyticsCreator supports a wide range of data pipeline architectures and modeling standards, enabling organizations to tailor data processing to their specific business and technical needs.

Supported Data Pipeline Approaches and Modeling Standards:

• Data Vault 2.0:Automated generation of Hubs, Links, Satellites, including SCD historization, hash key generation, and support for Point-in-Time (PIT) and Bridge Tables.
• Dimensional Modeling (Kimball):
  Automated creation of fact tables, dimensions, and Slowly Changing Dimensions (SCD Type 0, 1, 2, and mixed types) with full control over historization behavior.
• 3rd Normal Form (Inmon):
  Support for normalized, relational data warehouse designs.
• Hybrid and Custom Architectures:
  Mix and match modeling layers or define your own custom data pipeline logic using AnalyticsCreator wizards, macros, or manual SQL code.
• ETL and ELT Patterns:
  Flexible support for Extract → Transform → Load (ETL) and Extract → Load → Transform (ELT) processing workflows, deployable through SSIS, Azure Data Factory, or native SQL execution.
• Delta and Full Load Handling:
  Users can configure pipelines for delta loads, full loads, or change-based calculations, depending on data volume and refresh requirements.
• Automated Field Conversion and Data Type Management:
  AC automatically handles field type conversions, default values, and data standardization during pipeline generation.

With AnalyticsCreator, you can standardize, automate, and customize your data pipeline architecture—whether you follow established methodologies like Data Vault 2.0 and Kimball, or design your own modular, metadata-driven pipelines.

In AnalyticsCreator, the data catalog refers to the centralized metadata repository that documents all data structures, transformations, relationships, and semantic definitions across your data warehouse or analytics architecture. It provides a searchable inventory of data assets and their lineage, supporting discoverability, documentation, and governance.

Yes. AnalyticsCreator features an embedded metadata repository that functions as a data catalog, storing structural and semantic metadata for:

• Source data
• Core data warehouses / data lakes
• Staging and business models
• ETL transformations
• Data lineage
• Analytical frontends (Power BI, SSAS, Tableau)

The catalog includes:

• Technical metadata (table structures, columns, data types)
• Business metadata (object descriptions, naming conventions, KPIs)
• Lineage and relationships (foreign keys, dependency chains)
• Transformation logic (ETL steps, calculated columns, historization types)
• Deployment metadata (environments, versioning, documentation links)

AnalyticsCreator automatically generates data lineage visualizations based on its metadata repository, which contains the data catalog. It maps how data flows from source systems through staging, transformation, and presentation layers, down to analytical models. This supports:

• Impact analysis
• Audit trails
• Governance reporting

Yes. Users can search and browse metadata using the built-in graphical interface, which includes:

• Object filtering by type (tables, views, columns)
• Dependency views
• Semantic model navigation
• Cross-layer object traceability

Yes . AnalyticsCreator allows export of metadata and catalog artifacts to:

• Azure DevOps
• Fabric Purview
• GitHub or Git-based version control systems
• Documentation platforms (as Word, Visio, Excel)

A data catalog ensures:

• Consistency across data layers
• Improved data discoverability for analysts and business users
• Lineage transparency for auditors and compliance teams
• Accelerated onboarding for developers and data engineers
• One place of understanding data and business model.
• Foundation for building trusted, reusable data products
• Versioning of changes

Data management in AnalyticsCreator is a comprehensive, metadata-driven approach to governing and operationalizing the entire data lifecycle—from source ingestion to data product delivery. It encompasses the centralized control of data models, transformation logic, metadata, and deployment assets, all within an automated and version-controlled framework. By leveraging AnalyticsCreator’s automation engine, organizations can ensure data consistency, quality, and integrity at every stage, while maintaining full traceability, embedded documentation, and secure access controls. This enables teams to rapidly develop, test, and deploy robust data products with confidence, turning complex data landscapes into agile, governed, and high-performing analytics environments.

AnalyticsCreator is built on a metadata-driven engine, which means that every object—be it a source table, transformation rule, or data product—is defined, managed, and tracked centrally through metadata. This architecture ensures that governance is not an afterthought or a bolt-on component, but a foundational capability. Every change, every dependency, and every data flow is automatically captured and documented, enabling organizations to build data products that are not only fast to deploy, but also fully governed by design.

Unified Standards Across the Data Lifecycle

By centralizing the control of data definitions, naming conventions, data types, and transformation logic, AnalyticsCreator enforces standardization across the entire data lifecycle. Whether your team is ingesting raw source data, building a data vault model, or delivering dimensional models to a business intelligence layer, the same rules and standards apply. This consistency significantly reduces errors, simplifies maintenance, and ensures that data consumers across the organization can trust the outputs they receive.

End-to-End Lineage and Impact Analysis

A major pillar of effective data governance is traceability. AnalyticsCreator automatically generates full end-to-end data lineage, showing where each data point originated, how it was transformed, and where it is consumed. This visual and quarriable lineage is available at every layer—from source systems to data marts—and allows teams to quickly perform impact analysis before implementing changes. This capability is critical for maintaining compliance with data regulations, ensuring auditability, and giving data stewards the transparency they need to manage risk.

Built-in Documentation and Audit Trails

Documentation is often overlooked in traditional data warehouse projects due to its manual nature. AnalyticsCreator eliminates this problem by generating real-time documentation directly from metadata. As models are built or updated, the system produces complete and accurate documentation that includes data model diagrams, field definitions, transformation logic, version histories, and more. These materials are crucial for onboarding new team members, conducting audits, and ensuring knowledge transfer across departments.

Granular Access Control and Secure Deployments

Data governance also involves security and access management, especially when working across multiple environments or handling sensitive data. AnalyticsCreator supports role-based access controls that define who can create, modify, or deploy specific objects. This ensures that only authorized users can make changes to production systems, and it helps prevent accidental or malicious modifications. Additionally, sensitive data can be flagged and treated with specialized logic—such as masking, encryption, or restricted visibility—ensuring compliance with internal policies and external regulations like GDPR or HIPAA.

Data Quality Management and Validation Rules

Delivering trusted data starts with enforcing data quality at every stage. AnalyticsCreator allows teams to define and automate data validation rules that check for accuracy, completeness, consistency, and uniqueness. These rules can be applied at ingestion, during transformation, or prior to final data product delivery. Alerts can be configured to notify stakeholders if data breaches thresholds or if pipeline execution fails quality checks. This proactive approach helps maintain trust in analytics outputs and ensures that business decisions are made based on reliable information.

Version Control and Auditability

Changes to models, parameters, and scripts are automatically tracked with full version control. This supports agile development practices, enabling teams to iterate quickly while maintaining oversight. Each change is logged with a timestamp, user identity, and description, making it easy to understand the evolution of a data product over time. In the event of errors or compliance reviews, teams can easily roll back to previous versions or provide a detailed audit trail to stakeholders.

Data Governance as a Business Enabler

Ultimately, AnalyticsCreator transforms governance from a compliance-heavy burden into a business enabler. By automating governance functions and embedding them into every step of data product development, organizations can move faster, reduce risk, and ensure that their analytics platforms remain scalable, auditable, and aligned with business needs. This holistic approach empowers data teams to deliver value quickly—without compromising on control, quality, or transparency.

Yes. AnalyticsCreator  can be deployed in as many environments as is required without any additional cost or license.

In AnalyticsCreator, all development work is stored as structured, human-readable metadata in a centralized SQL Server–based repository. This repository serves as the single source of truth for your entire data warehouse, data lakehouse, and semantic layer modeling.

Key Aspects of Metadata Management in AnalyticsCreator:

• Centralized Storage:  All metadata—including source definitions, table structures, keys, relationships, transformations, business rules, and semantic models—is stored in a SQL Server database. This ensures consistency, version control, and traceability across all projects.
• Full Access and Extensibility:  You have full read and write access to the repository—either through the AnalyticsCreator user interface or directly via SQL Server Management Studio (SSMS).
  This allows you to query, extend, or customize the metadata to meet specific business or technical requirements.
• Custom Metadata Support: You can store your own custom metadata within the repository, enabling the development of custom add-ons, automation scripts, or integration components.
• Open for External Tool Integration:  Because the repository schema is open and accessible, organizations can build custom documentation tools, data lineage extractors, or even generate additional deployment artifacts for target platforms beyond those natively supported by AnalyticsCreator.
• Automation and Governance:  The metadata repository acts as the foundation for automated code generation, documentation, impact analysis, and data governance workflows.

AnalyticsCreator ensures pipeline consistency by:

• Generating all layers from a single metadata source
• Enforcing transformation logic inheritance
• Using reusable templates and data modeling standards
• Supporting calculated columns, naming conventions, and keys centrally

You can easily set up data profiling as a package in your pipeline—either during pre-deployment, post-deployment, or even within any specific layer of your project. This allows you to automatically analyze data quality, detect anomalies, and gain a deeper understanding of the structure and content of your data at any stage—before or after it's loaded into your target systems.

AnalyticsCreator supports manual detection of schema changes during metadata refresh. Developers are notified of:

• New or removed columns
• Renamed columns

Yes. AnalyticsCreator offers role-based security at the application and data model level. Developers can:

• Lock objects for exclusive editing
• Define user roles for editing, reviewing, and deploying
• Enforce access rules within generated OLAP cubes or semantic models

AnalyticsCreator includes built-in mechanisms for:

• Data anonymization (real masking)
• Pseudonymization using hash algorithms
• Field-level privacy logic embedded into ETL transformations

Yes. All modeling objects, transformations, and deployment definitions are version-controlled via:

• Internal object history in the metadata repository
• Integration with Git or Azure DevOps
• The Data Warehouse model and all the artifacts is included in external CI/CD pipelines

AnalyticsCreator combines:

• Metadata-driven automation
• Centralized data governance
• Environment-aware deployments
• Compliance support
• Integration with enterprise DevOps tools

You can use any Analytics frontend technology to get access to your Data Analytics stack which was build with AnalyticsCreator.

AnalyticsCreator supports the automatic generation of models and datasets for:

• Power BI (PBIP File for Desktop, XMLA for Power BI Service)
• Tableau (TWBX)
• Qlik Sense(QVD)
• Excel PowerPivot(XLSX)
• SQL Server Analysis Services (SSAS) – both Tabular and Multidimensional models

With pull-based reporting, you can connect any analytics frontend directly to the data warehouses and data lakes created using AnalyticsCreator.

Please note: AnalyticsCreator does not provide connection strings itself. All connections are made directly to your data warehouse environment

Yes. AnalyticsCreator deploy the models in Power BI Service using XMLA endpoint or generete the Pbix file with the data model.

Yes. AC can generate both SSAS Tabular (OLAP) and Multidimensional (MOLAP) models by:

• Creating XMLA query Defining KPIs, hierarchies, perspectives, and calculated measures
• Integrating with role-based security definitions

AnalyticsCreator ensures consistency through:

• Single metadata source used to define tables, relationships, and KPIs
• Reusable modeling templates for semantic layers
• Standardized naming conventions and descriptions
• Automated documentation of all semantic objects

Yes. While AC generates semantic models automatically, you can:

• Add custom measures and hierarchies
• Include or modify metadata descriptions

AnalyticsCreator enforces governance by:

• Generating role-based security definitions in OLAP and tabular models
• Embedding data lineage and model documentation
• Supporting field-level anonymization and masking
• Centralizing KPI definitions for consistent usage

Yes. By delivering clean, governed semantic models, AnalyticsCreator enables:

• Business users to explore trusted data in Power BI, , Tableau, Qlik or any other Analytics Frontend
• Reuse of certified datasets across domains
• Domain-aligned data products supporting data mesh strategies
• Ad hoc analysis without requiring deep technical expertise
• Domain’s self modelling of prequalified datasets
• Security roles for Domains users 

AnalyticsCreator provides built-in, metadata-driven visualization tools within its comprehensive GUI for understanding and managing data lineage and data model structures.

• Graphical Data Lineage Viewer:
   Automatically generated visual diagrams allow users to trace data flows from source systems through staging, business (semantic) layers, and presentation layers
• Automated Documentation Generation:  AnalyticsCreator can export data lineage diagrams and data model structures to external documentation formats like Microsoft Visio and Word, providing visual representations of data flows and entity relationships.
• Change Impact Awareness:  In the AC GUI, AC shows metadata-level impact analysis tools that flag object dependencies during model editing and deployment preparation.

AnalyticsCreator supports scalability by:

• Offering Data Warehouse / Data lake concepts
• Modern Analytics Architectures
• Advanced Analytics capabilities
• Hierarchical data storage determination
• Consistent data pipelining
• Automating the creation of reusable BI datasets
• Supporting tabular models optimized for large data volumes
• Enabling partitioned tables and column store indexes in the backend
• Allowing separation of semantic models by domain or product line.

AnalyticsCreator is used by a range of data professionals, including:

• Data Engineers – for automating ingestion, transformation, and ETL pipelines
• Data Architects – for designing scalable, governed data warehouses
• BI Developers – for generating semantic models and feeding analytics tools
• Data Stewards – for managing metadata and enforcing standards
• Governance and Compliance Teams – for enabling traceability and data privacy
• Product Owners / Domain Teams – for managing decentralized data products in data mesh environments

AnalyticsCreator enables data engineers to:

• Standardization in the development process
• Modern automated modelling standards (Data Vault 2.0, Kimball, Mixed, etc.)
• Automate repetitive ETL and modeling tasks
• Generate SSIS packages or ADF pipelines from metadata
• Detect changes in source structures
• Use version control and CI/CD workflows for reliability
• Focus on logic and performance tuning, not boilerplate coding
• Fast Prototyping
• Automated Testing and Documentation
• Clear Data Lineage
• And many more...

Data architects benefit by:

• Modern Data Analytics Architectures
• Enforcing consistent modeling standards (e.g., Data Vault, Kimball)
• Designing scalable multi-layered architectures (staging, business, semantic)
• Managing environment-specific deployments
• Ensuring lineage visibility and impact analysis
• Supporting data product modularization for domain ownership

BI developers and Data Engineers can use AnalyticsCreator to:

• Automatically generate Power BI semantic models, SSAS Tabular Cubes or Qlik/Tableau structures
• Maintain semantic consistency across reporting layers
• Standardization in the development process
• Modern automated modelling standards (Data Vault 2.0, Kimball, Mixed, etc)
• Reuse governed datasets for multiple dashboards
• Reduce dependency on manual model building
• Ensure that data sources are pre-validated and lineage-tracked

For data governance and compliance stakeholders, AnalyticsCreator provides:

• A central metadata repository with full lineage tracking
• Anonymization and masking features for sensitive data
• Role-based access and audit support
• Automated documentation and data catalog generation
• Integration with GDPR/DSGVO compliance patterns

IT and platform leaders gain:

• Faster project delivery with automation
• Reduced development costs via reuse and standardization
• Improved data quality and consistency
• Support for hybrid cloud architectures
• CI/CD compatibility for modern DevOps alignment
• Easy to switch to other and newer technologies because of the meta data driven approach where all the data and business models are stored independent of the technology used

Yes. In data mesh or decentralized models, business domain teams can:

• Build and own their own data products
• Reuse governed semantic models generated by AnalyticsCreator
• Query trusted data through Power BI/Tableau/Qlik without deep backend knowledge
• Contribute to model evolution through metadata-driven collaboration

For regulated sectors (e.g., finance, healthcare, government), AnalyticsCreator supports:

• Data privacy enforcement through anonymization/masking
• Lineage and audit tracking
• Controlled deployment processes (Dev/Test/Prod separation)
• Versioning of data models and ETL logic
• Documentation automation for compliance submissions

The return on investment comes from:

• 80–90% reduction in manual modeling and ETL scripting
• Fewer deployment errors and faster time-to-production
• Improved reuse and modularization of data assets
• Simplified compliance and audit workflows
• Lower total cost of ownership compared to maintaining siloed tools and hand-coded pipelines

AnalyticsCreator enhances collaboration by:

• Enabling multi-user development with object-level locking
• Supporting any source control integration (e.g.:Git, Azure DevOps)
• Providing shared metadata and documentation
• Allowing modular data product development across domains
• Offering reusable templates and prebuilt transformation logic

A database is an organized collection of structured data, or information, that is stored electronically in a computer system. Databases are designed to store, retrieve, and manage data efficiently. 

A data lakehouse is a unified data architecture that combines the flexibility and cost-effectiveness of a data lake with the data management and ACID transactions of a data warehouse. Data lakehouses are designed to store and process all types of data, including structured, semi-structured, and unstructured data. They provide a single platform for data scientists, analysts, and engineers to work with data in a variety of ways.

A data lake is a repository that stores all types of data in its raw format. Data lakes are designed to store large amounts of data in a cost-effective way. They are often used to store data that is not yet well understood or that may not be needed for immediate analysis. Conclution: A data lake is capable of storing raw data in various formats, including structured, semi-structured, and unstructured types.

A data hub is a central repository that consolidates data from multiple sources and makes it available for a variety of purposes. Data hubs are designed to provide a single source of truth for data and to make it easier for organizations to access, analyze, and share data. A Data Hub provides already structured, cleansed, and integrated data.

A data mart is a subset of a data warehouse that is designed to support a specific department (domain) or business unit. Data marts are typically smaller and more focused than data warehouses, and they often contain data that is tailored to the specific needs of the user group. Data marts can be part of a data product strategy, serving as domain-specific access points that can be made available to multiple business domains. For more details, see the FAQ on Data Products. Typically, data marts are consumed through analytics frontends such as Power BI.

Data Fabric is a design concept and architecture that aims to simplify data management by integrating and connecting data across various platforms and locations. It provides a unified, intelligent, and automated data management framework that ensures data can be accessed, shared, and governed seamlessly.

Data Mesh is an architectural approach that decentralizes data ownership and management. Instead of having a centralized data team, it distributes data responsibilities to domain-specific teams within an organization. Each team, or domain, is responsible for its own data as a product, ensuring that they manage, govern, and provide access to their data effectively.

A data pipeline is a set of processes that move data from one system to another. Data pipelines are typically used to extract data from source systems, transform it into a usable format, and load it into a target system.

Data governance is the process of managing and controlling the availability, usability, integrity, and security of data. Data governance is important for ensuring that data is accurate, reliable, and compliant with regulations.

Data Management refers to the comprehensive practice of collecting, storing, organizing, and maintaining data throughout its lifecycle to ensure it is accurate, accessible, secure, and usable for decision-making. Data management is important for ensuring that data is used effectively and efficiently.

Key Areas of Data Management:

1. Data Governance
   Establishes policies, roles, and responsibilities to ensure data quality, security, and compliance.

2. Data Quality
   Ensures data is accurate, complete, consistent, and up-to-date.

3. Data Integration
   Combines data from multiple sources into a unified view.

4. Master Data Management (MDM)
   Manages critical business entities (e.g., customers, products) to maintain a single, consistent reference.

5. Metadata Management
   Handles data about data, including lineage, definitions, and context.

6. Data Security & Privacy
   Protects data from unauthorized access and ensures compliance with privacy regulations.

7. Data Architecture
   Designs the overall structure and organization of data assets.

8. Data Storage & Infrastructure
   Manages databases, data warehouses, data lakes, and cloud storage solutions.

9. Data Lifecycle Management
   Governs the retention, archiving, and disposal of data over time.

10. Data Operations (DataOps)
    Focuses on automation, monitoring, and continuous improvement of data processes.

Data warehouse automation (DWA) is the use of software and tools to automate the process of building and maintaining a data warehouse. Data warehouse automation can help to improve the efficiency, accuracy, and reliability of data warehouses.

Data modeling is the process of creating a visual representation of data. Data models are used to communicate the structure and relationships of data to stakeholders. They can also be used to design and implement data warehouses, data marts, and other data storage systems.

Structured data is data that is organized in a predefined format and can be easily stored and queried. Examples of structured data include data in a database table or spreadsheet.

Slowly changing dimensions are dimensions in a data warehouse that change over time. There are four main types of slowly changing dimensions:

• Type 1: Overwrite the existing value with the new value.
• Type 2: Add a new row to the dimension table with the new value and a new effective date.
• Type 3: Create a new historical record with the old value and an effective date range.
• Type 4: Merge the old value with the new value into a new record.

A fact table is a table in a data warehouse that stores quantitative data. Fact tables are typically used to store facts about business transactions or events.

A dimension table is a table in a data warehouse that stores descriptive data. Dimension tables are typically used to store information about the entities or categories that are represented in the fact tables.

A data catalog serves as a comprehensive inventory of an organization’s data assets. It provides a centralized repository where data professionals can discover, understand, and access relevant data sources. Key features of a data catalog include:

• Metadata Enrichment: Data catalogs capture metadata about data tables, columns, relationships, and lineage. This contextual information enhances data discovery and promotes collaboration among data users.
• Search and Exploration: Users can search for specific datasets, explore data lineage, and understand data dependencies. A well-organized data catalog simplifies the process of finding relevant data for analysis.
• Business Glossary: A data catalog often includes business-friendly descriptions, data definitions, and terms. This bridges the gap between technical metadata and business context.

A metadata repository is a structured storage mechanism that houses metadata related to data assets. It serves as the backbone of a data warehouse metadata framework. Key functions of a metadata repository include:

• Centralized Storage: A metadata repository consolidates technical, process, and business metadata. It ensures consistency and provides a single source of truth for data-related information.
• Version Control: Metadata repositories maintain historical versions of metadata artifacts. Changes are tracked, allowing teams to understand how metadata evolves over time.
• Data Lineage: By defining relationships between data sources, transformations, and downstream tables, a metadata repository establishes data lineage. This lineage information is critical for impact analysis and understanding data flow.

A Metadata Framework is a set of rules, standards, and guidelines for describing and organizing data within an organization. It defines how data elements are identified, classified, and documented.