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Enterprises Using Spark NLP


Why Enterprises Using Spark NLP the Most

There are top five reasons that explain the need of Spark NLP in enterprises. Let’s learn about them in detail.


Spark NLP is an open source NLP library built natively on Apache Spark and TensorFlow. The library includes simple, performant and precise NLP notations for ML pipelines that can scale with an ease in a distributed environment. Spark NLP library is reusing the Spark Machine Learning pipeline along with integrating NLP functionality.

Recently, a survey has identified several trends among enterprise companies related to AI adoption. The results revealed the ranking of the trends out of which Spark NLP library grossed seventh rank amongst all AI frameworks and tools. It is one of the most widely used NLP library and famed to be AI library after TensorFlow, Sci-kit-learn, Keras, and PyTorch.

While most enterprises are using Apache NLP library the most, we try to analyze top reasons behind its popularity-

Here are the top 5 key issues:

1. Accuracy

The Spark NLP 2.0 library claims to deliver state-of-the-art accuracy and speed that allows uninterrupted production in the latest scientific advances. Apache NLP library also has production-ready implementation of BERT embeddings for named entity recognition. As compared to SpaCy, which makes double errors, Spark NLP is the first choice of the enterprise.

2. Speed

In Spark NLP, experts have done optimizations in a way that the common NLP pipelines could run orders of magnitude at faster rate as compared to the inherent design limitations of legacy libraries provide. The second generation Tungsten engine is used for vectorised in-memory columnar data, extensive profiling, no copying of text inside memory, and code optimization of Spark and TensorFlow, along with optimization for interference and training. This is why the speed of Spark NLP is faster than any other competitive product.

3. Scalability

Apache NLP library can be used to scale model training, inference and complete AI pipelines from a local machine to a cluster with minor or zero changes to code. Being natively designed and made on Apache Spark ML, the library is allowed to scale on any Spark cluster, on-premise or in any cloud provider. The major reason behind scalability is the zero code changes to scale AI pipeline to any Spark cluster.

4. Out of box performance

The features included in Spark NLP library provide full java API, scala AI, python API, and support various things like training on GPU, user-defined deep learning networks, Spark natively, Hadoop (YARN and HDFS).

The library offers the concepts of annotators and includes more things as compared to other NLPs, such as sentence detections, stemming, tokenization, lemmatization, POS Tagger, dependency parse, NER, Date matcher, text matcher, sentiment detector, chunking, pre-trained models, and training models.

5. Complete Python, Java, and Scala APIs

A multi-lingual library not only attracts audiences but also allows developers to leverage implemented models without moving data back and forth between the runtime environments.


Apache Spark NLP services are built on the Spark ML. It is reusing the Spark ML pipeline and NLP functionality. The library is extending Spark ML to deliver scalable, fast, and unified natural language processing to developers. Spark NLP implements core NLP algorithms. Therefore, things like, spell checking, dependency parsing, lemmatization, speech tagging, entity recognition would become quite easy. The algorithms will be used to develop popular pipelines, with the help of PySpark.

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