DocumentDB-ODBC-Treiber
The best way to connect BI applications of choice to your DocumentDB data.
Connect to DocumentDB From Your BI Tools
Simba DocumentDB ODBC driver with SQL Connector is the best way to connect your BI and reporting tools to your DocumentDB data. Our Simba DocumentDB ODBC connector gives you relational access to your NoSQL data, so you can leverage existing SQL expertise to query NoSQL data. The connector can also virtualize your data, ensuring that your traditional BI tools work effectively (even with unstructured data). This Azure DocumentDB ODBC driver enables direct SQL query translation to the data stored in DocumentDB, providing users with unparalleled performance at scale. The built-in Collaborative Query Execution (CQE) feature passes down filters and aggregations to provide high-performance access to DocumentDB.
Warum sollte man Simba und die dazugehörigen Treiber herunterladen?
Simba connectors allow you to make quick analytic insights and to leverage back-end data sources and high-performance calculation capabilities for your favorite BI client. The Simba DocumentDB driver enables BI, analytics, and reporting on data that is stored in Microsoft Azure DocumentDB. It is ODBC 3.80-compliant and adds important functionality like Unicode, 32- and 64-bit support, and more for high-performance computing environments on all platforms.
DocumentDB ODBC Driver Features
SQL Access for Azure DocumentDB: Query Azure DocumentDB data through SQL from analytics tools such as Microsoft Excel, Power BI, QlikView, Tableau, and other SQL-based applications.
Broad BI Tool Compatibility: Enables analysts to work in familiar reporting and visualization environments without needing to adopt a separate NoSQL-specific workflow.
Cross-Platform ODBC Connectivity: Provides a Unicode-enabled, ODBC 3.8-compliant connector for Windows, Mac OS X, and Linux environments.
32- and 64-Bit Application Support: Supports both 32-bit and 64-bit deployments, making it easier to fit into a wide range of existing system architectures.
Automatic Schema Generation: Creates a relational schema automatically from Azure DocumentDB data to reduce manual setup and simplify analysis.
Array Normalization as Virtual Tables: Converts arrays into virtual tables so nested and repeating data is easier to query in SQL-based tools.
Enhanced Schema Editor Workflows: Includes an updated Schema Editor that improves re-normalization workflows for more flexible schema management.
Data Preview for Faster Modeling: Allows users to preview data during schema setup so they can validate structures before finalizing them.
Array Flattening for Easier Analysis: Supports array flattening to make complex NoSQL structures more usable in downstream BI and reporting workflows.
Comprehensive Data Type Support: Supports all Azure DocumentDB data types and converts them into SQL data types for smoother integration with relational tools.
Simplified Type Conversion: Reduces manual transformation work by bridging the gap between schema-flexible DocumentDB data and SQL-based analytics environments.
DocumentDB ODBC Driver Specs
32- and 64-Bit Application Compatibility: Supports both 32-bit and 64-bit applications for flexible deployment across varied technical environments.
ODBC 3.8 Standards Compliance: Complies with the latest ODBC 3.8 specification to support reliable, standards-based connectivity.
ANSI SQL-92 Support: Supports ANSI SQL-92 to give users access to familiar SQL syntax for querying and analysis.
Unicode Support: Supports Unicode to ensure accurate handling of multilingual and extended character data.
Overcome DocumentDB Challenges With Simba Drivers
JSON Structure is Hard for SQL-First BI
Azure DocumentDB stores data as JSON, so properties can be nested and values can be arrays. While this is natural for apps, it tends to be awkward for traditional BI tools and analysts who expect rows and columns. Instead of spending time flattening documents, unpacking arrays, and reshaping data, Simba provides relational access to NoSQL data with schema generation, array normalization as virtual tables, and a Schema Editor.
Can’t Easily Query from Existing Tools
For teams that use common tools like Excel, Tableau, and Qlik, DocumentDB can be valuable but hard to operationalize when users need a different query model or custom integration. To prevent data from remaining locked behind developer-heavy workflows, Simba exposes Azure DocumentDB data via SQL. This makes it easy to connect from familiar interfaces and tools and use existing SQL skills instead of NoSQL processes.
Large Datasets Hamper Performance
Connecting to DocumentDB at scale requires queries to efficiently execute against the source. Without this, analytics becomes slow, expensive, and frustrating, all of which diminish the practical use of analytics against live data. Thankfully, Simba uses direct SQL query translation and Collaborative Query Execution to push filters and aggregations down to minimize movement and processing for much better performance at scale.