Fig. 1 High-level architecture of Apache Spark stack Fig. 2 Key entities for running a Spark application (Source: https:// spark.apache.org/docs/latest/cluster- overview.html) Table 1 Examples of models expressible using RDDs Fig. 3. Lazy evaluation of RDDs: transformations on RDDs are lazily evaluated, meaning that Spark will not compute RDDs until an action is called. Spark keeps track of the lineage graph of transformations, which is used to compute each RDD on demand and to recover lost data (image adapted from: http://www.slideshare.net/GirishKhanzode/ apache-spark-core) Fig. 4 Key features of Spark’s MLIib: a spark.mllib is built on top of RDDs, b spark.ml is built on top of DataFrames Fig. 6 Key features of Spark Streaming
