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Why Scala and Apache Spark are key skills for Big data engineering?

Apache Spark is one of the most popular frameworks for big data analysis. This framework is written in Scala, because it is functional language and very scalable. It also can be quite fast because it’s statically typed, and it compiles in a known way to the JVM. Hence, most of data engineers are today adopting Spark and Scala, while Python and R remain popular with data scientists. Fortunately, an engineer doesn’t need to master Scala to use Spark effectively. Beside Scala, Spark has API’s for Python, Java and R, but the most used languages are the former two. Java does not support Read-Evaluate-Print-Loop, and R is not a general-purpose language.

 

Why Scala is mostly used in Spark?

Scala Programming language, with his high functionality, provides the confidence to design, develop, code and deploy things the right way by making the best use of capabilities provided by Spark and other big data technologies. When it comes to processing big data and machine learning – Scala programming has dominated the big data world. Here are some reasons:

  1. Scalability. Apache Spark is written in Scala and because of its scalability on JVM. Scala is mostly used programming language, by big data developers for working on Spark projects. Scala helps developers to dig deep into Spark’s source code so that they can easily access and implement the newest features of Spark.
  2. High productivity and performance. Most of the big data developers are from Python or R programming background. On the other hand, Scala programming retains a perfect balance between productivity and performance. For a new Spark developer with no prior experience, it is enough to know the basic syntax collections and lambda to become productive in big data processing using Apache Spark. Also, the performance achieved using Scala is better than many other traditional data analysis tools like R or Python.
  3. Safety of code. For organisations safety of code is very important together with expressive power of dynamic programming language. Here Scala programming is having good potential, and this can be judged from its increasing adoption rates in the enterprise.
  4. Parallelism and concurrency. Scala has excellent built-in concurrency support and libraries like Akka, which make it easy for developers to build a truly scalable application.
  5. Corresponding data types. Many Scala data frameworks follow similar abstract data types that are consistent with Scala’s collection API’s. Developers just need to learn the standard collections and it would be easy to work with other libraries.
  6. Small data size with high complexity. Scala programming language provides the best path for building scalable big data applications in terms of data size and program complexity. With support for immutable data structures, for-comprehensions, immutable named values- Scala provides remarkable support for functional programming.
  7. Libraries design. Scala has well-designed libraries for scientific computing, linear algebra and random number generation.
  8. Scala is fast and efficient making it an ideal choice of language for computationally intensive algorithms. Compute cycle and memory efficiency are also well tuned when using Scala for Spark programming.
  9. No API lags. Other programming languages, like Python or Java, have lag in the API coverage. Scala has bridged API coverage gap, which is existing with other programming languages like Python or Java. The thumb rule here is that developers can write most concise code and they can achieve the best runtime performance. The best trade-off is to use Scala for Spark as it makes use of all the mainstream features, instead of developers having to master the advanced constructs.

Scala & Data analytics / engineering

The benefits of Apache Spark and Scala to Business

  • Provides highly reliable and fast in memory computation.
  • Efficient in interactive queries and iterative algorithm.
  • Fault tolerance capabilities because of immutable primary abstraction named RDD.
  • Inbuilt machine learning libraries.
  • Provides processing platform for streaming data using spark streaming.
  • Highly efficient in real time analytics using spark streaming and spark sql.
  • Graphx libraries on top of spark core for graphical observations.
  • Compatibility with any API JAVA, SCALA, PYTHON, R makes programming easy.

 

What does Apache Spark/Scala developer need to know?

In the role of Spark/Scala Developer, one will interface with key stakeholders and apply his technical proficiency across different stages of the Software Development Life Cycle, including Requirements Elicitation, Application Architecture definition and Design. In a daily work developer will deliver high quality code deliverables for a module, lead validation for all types of testing and support activities related to implementation, transition and warranty.

Typical responsibilities:

  • Create Scala/Spark codes for data transformation and aggregation
  • Produce unit tests for Spark transformations and helper methods
  • Write documentation with all code
  • Design data processing pipelines

Commune skills required:

  • Scala (with a focus on the functional programming paradigm)
  • Scala test, JUnit, Mockito {{, Embedded Cassandra}}
  • Apache Spark 2.x
  • {{Apache Spark RDD API}}
  • {{Apache Spark SQL Data Frame API}}
  • {{Apache Spark MLlib API}}
  • {{Apache Spark GraphX API}}
  • {{Apache Spark Streaming API}}
  • Spark query tuning and performance optimization
  • SQL database integration {{Microsoft, Oracle, Postgres, and/or MySQL}}
  • Experience working with {{HDFS, S3, Cassandra, and/or DynamoDB}}
  • Deep understanding of distributed systems (e.g. CAP theorem, partitioning, replication, consistency, and consensus)

Apache Spark & Data Engineering

What is the future of Apache Spark?

Spark optimization techniques are used to modify the settings and properties of framework, in order to help in processing data efficiently. The most popular Spark optimization techniques are listed below:

  1. Data Serialization

Here, an in-memory object is converted into another format that can be stored in a file or sent over a network. This improves the performance of distributed applications.

  1. Caching

This is used when the data is required more often. Cache and persist are the methods used in this technique.  These methods can help in reducing costs and saving time as repeated computations are used.

  1. Memory Management

The memory used for storing computations, such as joins, shuffles, sorting, and aggregations, is called execution memory. The storage memory is used for caching and handling data stored in clusters. When the execution memory is not in use, the storage memory can use the space. This is one of the most efficient Spark optimization techniques.

Read more on Scala Programming Language

 

Events you might want to atttend

Scala for Statistical Computing and Data Science

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Sources used for article:

KDnuggetes

https://www.dezyre.com/article/why-learn-scala-programming-for-apache-spark/198

Just Enough Scala for Spark

https://www.simplilearn.com/apache-spark-scala-course-overview-tutorial-video

5 Spark Optimization Techniques Every Data Scientist Should Know About

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