At the heart of the Spark architecture is the core engine of Spark, commonly referred to as spark-core, which forms the foundation of this powerful architecture. Spark-core provides services such as managing the memory pool, scheduling of tasks on the cluster (Spark works as a Massively Parallel Processing (MPP) system when deployed in cluster mode), recovering failed jobs, and providing support to work with a wide variety of storage systems such as HDFS, S3, and so on.

Spark-Core abstracts the users of the APIs from lower-level technicalities of working on a cluster. Spark-Core also provides the RDD APIs which are the basis of other higher-level APIs, and are the core programming elements on Spark. We'll talk about RDD, DataFrame and Dataset APIs later in this book.

Spark SQL is one of the most popular modules of Spark designed for structured and semi-structured data processing. Spark SQL allows users to query structured data inside Spark programs using SQL or the DataFrame and the Dataset API, which is usable in Java, Scala, Python, and R. Because of the fact that the DataFrame API provides a uniform way to access a variety of data sources, including Hive datasets, Avro, Parquet, ORC, JSON, and JDBC, users should be able to connect to any data source the same way, and join across these multiple sources together. The usage of Hive meta store by Spark SQL gives the user full compatibility with existing Hive data, queries, and UDFs. Users can seamlessly run their current Hive workload without modification on Spark.

Spark SQL can also be accessed through spark-sql shell, and existing business tools can connect via standard JDBC and ODBC interfaces.

More than 50% of users consider Spark Streaming to be the most important component of Apache Spark. Spark Streaming is a module of Spark that enables processing of data arriving in passive or live streams of data. Passive streams can be from static files that you choose to stream to your Spark cluster. This can include all sorts of data ranging from web server logs, social-media activity (following a particular Twitter hashtag), sensor data from your car/phone/home, and so on. Spark-streaming provides a bunch of APIs that help you to create streaming applications in a way similar to how you would create a batch job, with minor tweaks.

As of Spark 2.0, the philosophy behind Spark Streaming is not to reason about streaming and building data application as in the case of a traditional data source. This means the data from sources is continuously appended to the existing tables, and all the operations are run on the new window. A single API lets the users create batch or streaming applications, with the only difference being that a table in batch applications is finite, while the table for a streaming job is considered to be infinite.

MLlib is Machine Learning Library for Spark, if you remember from the preface, iterative algorithms are one of the key drivers behind the creation of Spark, and most machine learning algorithms perform iterative processing in one way or another.

Spark MLlib allows developers to use Spark API and build machine learning algorithms by tapping into a number of data sources including HDFS, HBase, Cassandra, and so on. Spark is super fast with iterative computing and it performs 100 times better than MapReduce. Spark MLlib contains a number of algorithms and utilities including, but not limited to, logistic regression, Support Vector Machine (SVM), classification and regression trees, random forest and gradient-boosted trees, recommendation via ALS, clustering via K-Means, Principal Component Analysis (PCA), and many others.

GraphX is an API designed to manipulate graphs. The graphs can range from a graph of web pages linked to each other via hyperlinks to a social network graph on Twitter connected by followers or retweets, or a Facebook friends list.

Spark provides a built-in library for graph manipulation, which therefore allows the developers to seamlessly work with both graphs and collections by combining ETL, discovery analysis, and iterative graph manipulation in a single workflow. The ability to combine transformations, machine learning, and graph computation in a single system at high speed makes Spark one of the most flexible and powerful frameworks out there. The ability of Spark to retain the speed of computation with the standard features of fault-tolerance makes it especially handy for big data problems. Spark GraphX has a number of built-in graph algorithms including PageRank, Connected components, Label propagation, SVD++, and Triangle counter.

Apache Spark runs on both Windows and Unix-like systems (for example, Linux and Mac OS). If you are starting with Spark you can run it locally on a single machine. Spark requires Java 7+, Python 2.6+, and R 3.1+. If you would like to use Scala API (the language in which Spark was written), you need at least Scala version 2.10.x.

Spark can also run in a clustered mode, using which Spark can run both by itself, and on several existing cluster managers. You can deploy Spark on any of the following cluster managers, and the list is growing everyday due to active community support: