Using Scala For Big Data Projects

Scala for big data projects.

BIG data and BIG platforms! More data = more transactions, more processing, more audit history and it is very normal to have millions of transactions in a day for one single financial institution.

Millions of transactions means that the technology being used to handle theses needs to be sophisticated enough to handle such complex operations. Here we will talk about why Scala is a good fit for big data applications.

 

Getting to know Scala

The Scala language was first introduced as a response to the criticism of Java. Being an open-source language, Scala is fully compatible with Java, and its code is converted to Java byte code, hence Java based code can be readily executed in Scala. Scala is built to be concise and is a functional and object-oriented language so that it can suit many uses. Its syntax is a good balance between conciseness and readability.

Scala for the large projects

Scala is among the top choice for developers for enterprise, data-driven applications that need to be robust, fault-tolerant, and high availability and scalability.

To gauge Scala’s suitability for large enterprise projects, we will try to understand it against the following factors:

  1. Ecosystem for support and libraries
  2. Support for data-driven applications
  3. Big data applications

Ecosystem for support and libraries

As mentioned above, Scala is readily compatible with Java.  Scala comes with currying, lazy evaluation, closures, and many other features which are not present in Java. It also runs on the Java Virtual Machine (JVM) and Every Java library is readily compatible with Scala.

This makes Scala opportunities vast and leverages the code already available in Java. IDEs such as Eclipse and IntelliJ can be used with Scala straight away. Moreover, build tools such as Maven can be used in Spark as well, with some hooks. Scala’s pattern matching feature is an extremely helpful feature that is not available in Java.

Support for data-driven applications

One reason why Scala is favoured for Fintech, Data Science and Machine Learning applications is its ecosystem of vast libraries. Scala has both general-purpose libraries as well as specific use cases such as Natural Language Processing, Data Analysis, and Data Visualisations. Spark NLP, ScalaNLP, Breeze, Summing Bird, Flink, Spark Notebook, and TensorFlow Scala are very well known for all data analysis and Machine Learning applications.

Big data applications

Scala has a vast number of frameworks that help in manipulating large datasets, stream processing, and file system processing. Below are some examples;

  • Apache Kafka

Apache Kafka is a platform for distributed streaming of data, built to process push-based messaging and is compatible with Java and Scala. Kafka is a fast, robust, and scalable solution for the distributed applications that have a large number of users with millions of data streams. Kafka is used in combination with Apache Storm, Apache HBase, and Apache Spark.

  • Apache Flink

Apache Flink is written in Scala and is suitable for streaming-based applications such as event-driven applications, stream and batch analytics and ETL purposes. Spark can be scaled at ease and benefits from low latency and in-memory computing.

  • Apache Samza

Apache Samza is used to build stateful applications that can process data from various sources which act as input devices. It is written in Scala and is fully compatible with Scala and Java. Samza provides continuous computation and output which distinguishes it from Sparkk and Hadoop.

  • Apache Spark

Apache Spark is the leading framework for data processing. Spark uses Scala for development and is fully compatible with Java Virtual Machine (JVM). Spark allows support for other languages such as Python and R amongst more. Spark boasts running workloads  x 100 times faster using scheduler and execution engine.

Conclusion

Scala is a language that is very suitable for your large enterprise projects. Its features show that Scala has libraries and support for almost every use case that big data projects might have.

Scala has a rapidly growing community and whether StackOverflow or GitHub, the Scala community have your questions covered.

Here at Code Factory, we have vast experience in both Scala based development and big data projects.