TensorFlow is an open-source AI library from Google that allows for data flow graphs to build models. Apache Spark is a real-time data processing system with support for diverse data sources and programming styles, providing a framework for machine learning. Together, Apache Spark and TensorFlow allow for the training and application of deep learning models such as neural networks.
What Is TensorFlow?
TensorFlow is an open-source project library from Google for machine learning — primarily deep learning. TensorFlow uses multi-dimensional arrays of data at higher dimensions (“tensors”), which are useful for handling large amounts of data and works based on data flow graphs with nodes and edges making it easier to execute code across a cluster of computers.
TensorFlow supports multiple languages, though Python is the most suitable and commonly used. Applications of TensorFlow can be operated on and deployed to nearly any device. Some common uses including performing ETL on large data sets, processing streaming data, complex analysis, and machine learning tasks.
TensorFlow Learning on Spark Clusters
The open-source TensorFlowOnSpark framework allows scalable TensorFlow learning, enabling experimentation for algorithm designs and supporting scalable training and inferencing on Spark architecture. It supports all TensorFlow functionalities, including synchronous and asynchronous learning, model and data parallelism, and TensorBoard. Its Python API integrates easily with existing Spark libraries such as MLlib.
Snowflake's Data Cloud supports data science and machine learning. Snowflake’s architecture enables easy data preparation for machine learning model building. The Snowflake Connector for Spark brings Snowflake into the Spark ecosystem, enabling Spark to read data from and write data to Snowflake, making it a best-in-class data source to build real-time pipelines based on Spark and machine learning frameworks like TensorFlow.