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• Spark SQL (Shark)
• Spark Streaming
• GraphX
• MLlib
• SparkR

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Spark SQL is a Spark interface to work with structured as well as semi-structured data. It has the capability to load data from multiple structured sources like “text files”, JSON files, Parquet files, among others. Spark SQL provides a special type of RDD called SchemaRDD. These are row objects, where each object represents a record.

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Not directly but we can register an existing RDD as a SQL table and trigger SQL queries on top of that.

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Spark MLib- Machine learning library in Spark for commonly used learning algorithms like clustering, regression, classification, etc.

Spark Streaming – This library is used to process real time streaming data.

Spark GraphX – Spark API for graph parallel computations with basic operators like join Vertices, subgraph, aggregate Messages, etc.

Spark SQL – Helps execute SQL like queries on Spark data using standard visualization or BI tools.

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SQL Spark, better known as Shark is a novel module introduced in Spark to work with structured data and perform structured data processing. Through this module, Spark executes relational SQL queries on the data. The core of the component supports an altogether different RDD called SchemaRDD, composed of rows objects and schema objects defining data type of each column in the row. It is similar to a table in relational database.

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Parquet is a columnar format file supported by many other data processing systems. Spark SQL performs both read and write operations with Parquet file and consider it be one of the best big data analytics format so far.

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Spark SQL is capable of:

• Loading data from a variety of structured sources
• Querying data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). For instance, using business intelligence tools like Tableau
• Providing rich integration between SQL and regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more

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Spark is a parallel data processing framework. It allows to develop fast, unified big data application combine batch, streaming and interactive analytics.

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Hive is a component of Hortonworks’ Data Platform (HDP). Hive provides an SQL-like interface to data stored in the HDP. Spark users will automatically get the complete set of Hive’s rich features, including any new features that Hive might introduce in the future.

The main task around implementing the Spark execution engine for Hive lies in query planning, where Hive operator plans from the semantic analyzer which is translated to a task plan that Spark can execute. It also includes query execution, where the generated Spark plan gets actually executed in the Spark cluster.

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“Parquet” is a columnar format file supported by many data processing systems. Spark SQL performs both read and write operations with the “Parquet” file.

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Catalyst framework is a new optimization framework present in Spark SQL. It allows Spark to automatically transform SQL queries by adding new optimizations to build a faster processing system.

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BlinkDB is a query engine for executing interactive SQL queries on huge volumes of data and renders query results marked with meaningful error bars. BlinkDB helps users balance ‘query accuracy’ with response time.

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Hadoop MapReduce requires programming in Java which is difficult, though Pig and Hive make it considerably easier. Learning Pig and Hive syntax takes time. Spark has interactive APIs for different languages like Java, Python or Scala and also includes Shark i.e. Spark SQL for SQL lovers – making it comparatively easier to use than Hadoop.

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• Parquet file
• JSON Datasets
• Hive tables

SparkSQL is a Spark component that supports querying data either via SQL or via the Hive Query Language. It originated as the Apache Hive port to run on top of Spark (in place of MapReduce) and is now integrated with the Spark stack. In addition to providing support for various data sources, it makes it possible to weave SQL queries with code transformations which results in a very powerful tool. Below is an example of a Hive compatible query.

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• Due to the availability of in-memory processing, Spark implements the processing around 10-100x faster than Hadoop MapReduce. MapReduce makes use of persistence storage for any of the data processing tasks.
• Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks form the same core like batch processing, Steaming, Machine learning, Interactive SQL queries. However, Hadoop only supports batch processing.
• Hadoop is highly disk-dependent whereas Spark promotes caching and in-memory data storage
• Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.

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SparkSQL is a special component on the spark Core engine that support SQL and Hive Query Language without changing any syntax. It’s possible to join SQL table and HQL table.