TensorFlow vs Spark | Top 5 Differences to Learn (With Infographics)

Difference Between TensorFlow and Spark

TensorFlow implies a Python-friendly open-source library for numerical computation that performs machine learning faster and more straightforward. TensorFlow permits developers to design data flow graphs—structures that define how data moves over a chart, either a series of processing nodes. An individual node within a particular graph signifies some mathematical operation. Also, all points either association among nodes implies some multidimensional tensor preferentially collection of data. TensorFlow gives full about that for the developer through a way of the popular language, Python. Apache Spark is a high-speed plus general-purpose cluster computing system. It gives high-level APIs in Scala, Python, Java and R, and an optimized engine that promotes general execution graphs. It also helps a rich set of higher-level tools including MLlib for machine learning, GraphX for graph processing, and Spark SQL for SQL and structured data processing, Spark Streaming. Apache Spark has as its structural foundation the resilient distributed dataset (RDD), a read-only multiset of data items scattered across a cluster of machines, that does maintain in a fault-tolerant way.

What is TensorFlow?

This language is simple to work with and easy to learn and offers acceptable approaches to represent whereby abstractions that are high-level can be linked to Tensors, and Nodes collectively are Python objects in TensorFlow. Also, applications of TensorFlow remain themselves Python applications. In Python, The correct math operations, nevertheless, are not implemented. Modifications in the libraries which are accessible via TensorFlow are composed similarly to C++ binaries with higher performance. Python provides high-level programming abstractions by directly directing traffic among the pieces and secure them together. The applications of TensorFlow can be operated on almost every target which is accessible: a local machine, iOS, a cluster in the cloud, CPUs or GPUs and Android devices. If Google’s private cloud is familiar, for additional acceleration, you can run Google’s custom TensorFlow Processing Unit (TPU) silicon toward TensorFlow. And then, The resulting models developed by TensorFlow, though, can be deployed on most any device where they will be handled to serve predictions.

What is Spark?

In Spark 1.x, the RDD did the initial application programming interface (API), however, as of Spark 2.x utility of the Dataset, API supported even though this RDD API is not deprecated. The RDD technology still holds the Dataset API. Spark also its RDDs were formed in 2012 in response to restrictions in the MapReduce cluster computing standard, which forces an appropriate linear dataflow structure on shared programs: MapReduce programs scan input data from disk, map a function over the data, decrease the results of the map, moreover store reduction results toward the disc.

Spark’s RDDs function a working set essentially for distributed programs that contribute a (purposely) limited form of allocated shared memory. Spark promotes the implementation of both iterative algorithms, which visit their dataset various times within a loop, and interactive/exploratory data analysis, i.e., the replicated database-style querying of data. The latency of such applications may be decreased by many orders of magnitude associated with a MapReduce implementation (as was popular in Apache Hadoop stacks). With the class of iterative algorithms are the foundation algorithms for machine learning systems, which created the primary impetus for developing Apache Spark.

Head To Head Comparison Between TensorFlow and Spark(Infographics)

Below is the top 5 difference between TensorFlow vs Spark

Key Differences Between TensorFlow and Spark

Both are popular choices in the market; let us discuss some of the major Difference

Apache Spark preferentially Spark as it is commonly known as an open-source, cluster computing framework that gives an interface for whole programming clusters with implicit data parallelism also fault tolerance.TensorFlow, on the other hand, is a compact library developed by Google that assists in improving the performance of numerical computation even neural networks and generating data flow as graphs—consisting of nodes indicating operations and edges signifying data array.
Spark, essentially a large data framework, has performed it possible for a high number of corporations generating a massive quantity of user data to process it efficiently furthermore offer up recommendations at scale. Whereas, Tensorflow, Essentially a machine learning framework, it supports people create extensive learning models without the necessity for rigorous skill sets of a machine learning specialist.
In Spark, a fast and comprehensive engine for large-scale data processing allows various features like streaming and sophisticated analytics, high speed, ease of use, it can connect with SQL, can run everywhere such as Mesos, Hadoop, and cloud. On the other hand, in Tensorflow, A Google API allowing computation on great learning and machine learning, TensorFlow gives a graphical representation computation flow. The API encourages the user to write complex neural network design also tune it according to activation values.
Tensorflow Written in Python, C++, CUDA.In contrast, Spark is written in Scala, Java, Python, R
TensorFlow On Spark resolves the difficulty of deploying high learning on significant data clusters in a distributed manner which is not an entirely modern robust knowledge paradigm however preferably an upgrade to the current frameworks that needed the development of various programs for expanding intelligence on significant data groups. Connecting both TensorFlow also Spark, it provides scope to undesired system complexity as well as end-to-end learning latency.

TensorFlow vs Spark Comparison Table

Below is the 5 topmost comparison

The basis of comparison	TENSORFLOW	SPARK
Definition	TensorFlow implies an open-source software library toward dataflow programming over a range of tasks. It is a typical math library also is similarly used for machine learning applications such as neural networks. It is utilized for both examination and production at Google.‍	Apache Spark implies an open-source shared general-purpose cluster-computing framework. Basically developed at the University of California, Berkeley’s AMPLab, the Spark codebase was later granted to the Apache Software Foundation, which has managed it since. Spark gives an interface for programming whole clusters with implicit data parallelism and error tolerance.
Written in	Python, C++, CUDA	Scala, Java, Python, R
Operating system	Linux, macOS, Windows, Android, JavaScript	Microsoft Windows, macOS, Linux
Type	Machine learning library	Data analytics, machine learning algorithms
Developer(s)	Google Brain Team	Apache Software Foundation, UC Berkeley AMPLab, Databricks

Conclusion

In summary, Apache Spark implies a data processing framework, whereas TensorFlow used for great custom learning and neural network design. Therefore if a user requires to implement deep learning algorithms, TensorFlow is the solution, and for data processing, it is Spark.

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