Apache Spark Tutorial

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Apache Spark Tutorial

This tutorial gives you an overview and talks about the fundamentals of Apache Spark.

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The Spark project consists of multiple components:- Spark core and Resilient distributed datasets(RDD’s), Spark SQL, Spark Streaming, MLlib Machine learning library and GraphX.

Spark Core and Resilient Distributed Datasets (RDDs):

Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities. The fundamental programming abstraction is called Resilient Distributed Datasets, a logical collection of data partitioned across machines. RDDs can be created by referencing datasets in external storage systems, or by applying coarse-grained transformations (e.g. map, filter, reduce, join) on existing RDDs.The RDD abstraction is exposed through a language-integrated API in Java, Python, Scala similar to local, in-process collections. This simplifies programming complexity because the way applications manipulate RDDs is similar to manipulating local collections of data.

Spark SQL:

Spark SQL is a component on top of Spark Core that introduces a new data abstraction called Schema RDD, which provides support for structured and semi-structured data. Spark SQL provides a domain-specific language to manipulate SchemaRDDs in Scala, Java, or Python. It also provides SQL language support, with command-line interfaces and ODBC/JDBC server.

Spark Streaming:

Spark Streaming leverages Spark Core’s fast scheduling capability to perform streaming analytics. It ingests data in mini-batches and performs RDD transformations on those mini-batches of data. This design enables the same set of application code written for batch analytics to be used in streaming analytics, on a single engine.

MLlib Machine Learning Library:

MLlib is a distributed machine learning framework on top of Spark that because of the distributed memory-based Spark architecture is, according to benchmarks done by the MLlib developers, ten times as fast as Hadoop disk-based Apache Mahout and even scales better than Vowpal Wabbit. It implements many common machine learning and statistical algorithms to simplify large scale machine learning pipelines, including:

  • summary statistics, correlations, stratified sampling, hypothesis testing, random data generation
  • classification and regression: SVMs, logistic regression, linear regression, decision trees, naive Bayes
  • collaborative filtering: alternating least squares (ALS)
  • clustering: k-means
  • dimensionality reduction: singular value decomposition (SVD), principal component analysis (PCA)
  • feature extraction and transformation
  • optimization primitives: stochastic gradient descent, limited-memory BFGS (L-BFGS)

                                                 Checkout Apache Spark Interview Questions

This article is just an overview to enlighten you over Apache Spark software. The Spark training sessions are however designed to be more composed, knowledgeable and in-depth.

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Ravindra Savaram
About The Author

Ravindra Savaram is a Content Lead at His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter. Protection Status