Apache Spark ODBC Driver

Name: CData ODBC Driver for Apache Spark
Rating: 4.9 (158 reviews)
Author: CData Software

Read, Write, and Update Spark through ODBC

Easily connect live Apache Spark SQL data with BI, ETL, Reporting, & Custom Apps.

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The Spark ODBC Driver is a powerful tool that allows you to connect with Apache Spark, directly from any applications that support ODBC connectivity. The Driver maps SQL to Spark SQL, enabling direct standard SQL-92 access to Apache Spark.

Features

Maps SQL to Spark SQL, enabling direct standard SQL-92 access to Apache Spark
Fully compatible with the DataBricks Enterprise Platform
Connect to live Apache Spark SQL data, for real-time data access
Full support for data aggregation and complex JOINs in SQL queries
Secure connectivity through modern cryptography, including TLS 1.2, SHA-256, ECC, etc.
Seamless integration with leading BI, reporting, and ETL tools and with custom applications

Specifications

Supports 32-bit and 64-bit applications.
ODBC 3.8 compliant native protocol based drivers.
Comprehensive ANSI SQL-92 support.
Full Unicode Support - any language, any data.
Cross-platform ODBC. Available for Windows, Linux and macOS.

Windows

macOS

Unix

AVAILABLE IN:

ODBC DESKTOP SUBSCRIPTIONS

200+ ODBC Drivers For Real-Time BI & Reporting.

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Where can I use the Apache Spark ODBC Driver?

BI, ETL, & Reporting Tools
Custom Applications
Back-Office Integration

BI, ETL, & Reporting Tool Integration

ODBC is the most widely supported interface for connecting applications with data. All kinds of BI, Reporting, ETL, Database, and Analytics tools offer the ability to read and write data via ODBC connectivity.

Cognos BI
Crystal Reports
FileMaker Pro
FoxPro
Microsoft Access
Microsoft Excel
MicroStrategy
Oracle DB
Power Map
Power Pivot
QlikView
SAP Business Objects
Sharepoint Services
Tableau (certified)

... and many more!

Custom Applications

Build Apps That Connect To Apache Spark SQL

As the most widely used interface to relational data, ODBC interfaces are accessible from every major development technology, including PHP, Python, Delphi, Visual Basic, Labview, PowerBuilder, FoxPro, FileMaker Pro, and more.

Developers can use Apache Spark ODBC Driver to rapidly build Web, Desktop, and Mobile applications that interact with live Apache Spark SQL data!

Connect Office Tools with Apache Spark SQL

Any application that can access data through ODBC, like Microsoft Excel, PowerPivot, or Word, can leverage our ODBC Drivers to connect to real-time data.

Through the Apache Spark ODBC Driver applications like Microsoft Word allow users to directly access live Apache Spark SQL data with powerful capabilities like 'Mail Merge', eliminating the need for complicated import/export procedures.

Robust ODBC Access to Apache Spark SQL

Full-featured and consistent SQL access to any supported data source through ODBC

Standards-Compliant ODBC

ODBC 3.8 compliant native protocol based drivers.
Unicode, 32-bit and 64-bit support

Support 32-bit and 64-bit applications and unicode ODBC APIs.
Cross-platform ODBC (Windows/Linux/Mac)

Available for Microsoft Windows, Unix, and Mac (macOS / OS X) platforms.
Certified ODBC Compatibility*

Our drivers undergo extensive testing and are certified to be compatible with leading analytics and reporting applications like Tableau, Microsoft Excel, and many more.
* Please visit our knowledge base to access how-to articles.
Embedded Remoting Capabilities

Our exclusive Remoting feature allows hosting the ODBC connection on a server to enable connections from various clients on any platform (Java, .NET, C++, PHP, Python, etc.), using any standards based technology (ODBC, JDBC, etc.). ODBC Remoting is enabled using the popular MySQL and SQL (TDS) wire protocol server.

String, Date, Numeric SQL Functions

The driver includes a library of 50 plus functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions.
Collaborative Query Processing

Our drivers enhance the data source's capabilities by additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc.
Easily Customizable and Configurable

The data model exposed by our ODBC Drivers can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. These customizations are supported at runtime using human-readable schema files that are easy to edit.
Enterprise-class Secure Connectivity

Includes standard Enterprise-class security features such as TLS/ SSL data encryption for all client-server communications.
Replication and Caching

Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data.

Unparalleled Performance
Remoting (MySQL/SQL)

ODBC Driver Performance

With traditional approaches to remote access, performance bottlenecks can spell disaster for applications. Regardless if an application is created for internal use, a commercial project, web, or mobile application, slow performance can rapidly lead to project failure. Accessing data from any remote source has the potential to create these problems. Common issues include:

Network Connections - Slow network connections and latency issues are common in mobile applications.
Service Delays - Delays due to service interruptions, resulting in server hardware or software updates.
Large Data - Intentional or unintentional requests for large amounts of data.
Disconnects - Complete loss of network connectivity.

The CData ODBC Driver for Apache Spark solves these issues by supporting powerful smart caching technology that can greatly improve the performance and dramatically reduce application bottlenecks.

Smart Caching

Smart caching is a configurable option that works by storing queried data into a local database. Enabling smart caching creates a persistent local cache database that contains a replica of data retrieved from the remote source. The cache database is small, lightweight, blazing-fast, and it can be shared by multiple connections as persistent storage.

Caching with our ODBC Drivers is highly configurable, including options for:

Auto Cache - Maintain an automatic local cache of data on all requests. The provider will automatically load data into the cache database each time you execute a SELECT query. Each row returned by the query will be inserted or updated as necessary into the corresponding table in the cache database.
Explicit Cache - Cache only on demand. Developers decide exactly what data gets stored in the cache and when it is updated. Explicit caching provides full control over the cache contents by using explicit execution of CACHE statements.
No Cache - All requests access only live data and no local cache file is created.

This powerful caching functionality increases application performance and allows applications to disconnect and continue limited functioning without writing code for additional local storage and/or data serialization/deserialization.

More information about ODBC Driver caching and best caching practices is available in the included help files.

Enterprise-Class Remoting

MySQL/SQL Database entry points for Apache Spark SQL Data

The CData ODBC drivers include powerful fully-integrated remote access capabilities that makes Apache Spark SQL data accessible from virtually anywhere. The drivers include the CData SQL Gateway, which can the optional ability to accept incoming SQL and MySQL client connections and service standard database requests.

With the CData SQL Gateway, users can interact with Apache Spark SQL data from any client that supports SQL Server or MySQL: from Web & mobile applications, to CRM and CMS systems, BI tools like SQL Server Analysis Services, and even through popular management applications like MySQL Workbench. Access Apache Spark SQL data from virtually any application that can access external data. Applications that can access SQL Server or MySQL data can now connect to Apache Spark SQL with this driver.

Connect Apache Spark SQL data with popular BI tools like SQL Server Analysis Services.
Enable enterprise Apache Spark SQL data integration through SQL Linked Server connectivity
Includes support for the MySQL and SQL (TDS) remote access protocols, with advanced wire-protocol SSL security - industry standards for remote database connectivity.
Easily connect to Spark from code (PHP, Python, Ruby, Objective-C, etc.) using one of the standard client libraries for accessing SQL/MySQL.
* Available exclusively in ODBC Server Licenses.

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Frequently Asked Spark ODBC Driver Questions

Learn more about Spark ODBC drivers for data and analytics integration

Does Spark support ODBC?

Not natively. However, CData offers an ODBC driver for Spark that allows you to connect to Spark data from any ODBC-compliant application, just like you would access a traditional database. This can be useful for tasks like:

Accessing Spark from applications: Connect to Spark data in popular tools and applications including Microsoft Excel, Tableau, Power BI, and many others.
Real-time data: You can work with live Spark data within these applications, enabling tasks like reporting and analysis.
Connecting systems: Build data integrations between Spark and other systems.

The Spark ODBC driver is an ODBC 3.8 compliant native protocol-based driver with comprehensive ANSI SQL-92 support. This means that virtually any application that can connect to data via ODBC, can use the CData ODBC driver for real-time integration. Download a fully functional free trial of the Spark ODBC driver today to get started.

Is there an ODBC driver for Spark?

Yes, the CData ODBC driver for Spark provides universal ODBC data connectivity for Spark. The Spark ODBC driver offers a simple SQL-based layer of abstraction that simplifies real-time data access for users and applications, enabling them to communicate with Spark using a standardized set of functions. Virtually any application on any platform (including Windows, macOS, or *nix) can use the CData ODBC driver for real-time integration.

How do I connect to Spark via ODBC?

Connectivity to Spark via ODBC is easy. First, download and install the Spark ODBC driver. The download page includes separate installers for Windows, macOS, and Unix, so please select the Spark ODBC driver that matches the platform where you intend to use the driver.

Once the installation is complete, navigate to the ODBC driver documentation page. Here, you'll find a wealth of information about the installed driver. The step-by-step instructions for creating a DSN and using it to connect to Spark via ODBC are just the beginning. The documentation also provides extensive configuration details for using the Spark ODBC driver with all your favorite applications and development tools, ensuring you have all the support you need.

Where can I download an ODBC driver for Spark?

All of the CData ODBC drivers, including the Spark ODBC driver are available for download online. To get started, download a fully functional free trial of the Spark ODBC driver today.

How do I install an ODBC driver for Spark?

To install the Spark driver, simply download one of the Spark ODBC driver installers available online. The download page includes separate Windows, macOS, and Unix installers, so please select the Spark ODBC driver that matches the platform where you intend to use the driver. The installers are comprehensive setup utilities that will install all the components required to use the Spark ODBC driver on your system.

Is the Spark ODBC driver 32-bit or 64-bit?

The Spark ODBC driver is available in both 32-bit and 64-bit binaries. The Spark driver installer will allow you to install both options, and you can select the ODBC driver binary that is appropriate for the system where you intend to use the driver.

Does pyODBC work with Spark?

Absolutely. The Spark ODBC driver seamlessly supports pyODBC connectivity. For example, check out this article on our online knowledgebase that demonstrates how you can connect to Spark from PyCharm using pyODBC and the Spark ODBC driver.

However, besides ODBC support, we also provide native Python data connectivity for Spark. To learn more, check out the Spark Python Connector online.