Data Analysis of E-Commerce Platform with Spark SQL

Spark SQL refers to an Apache Spark module that integrates functional programming Spark API with relational processing. Spark SQL remained a Spark Core part since its version 1.0. This runs Hive SQL/QL replacing or alongside hive existing deployments with connection possibilities with the BI existing tools. This has Java, Python, and Scala bindings and makes vital framework additions. It provides tight integration of procedural and relational processing with DataFrame declarative API to integrate with Spark procedural API. 

This includes extensible optimiser with Scala and makes the full use of matching strong pattern capabilities so that defining extension, and generation of control code, and addition of composable rules becomes easy. Spark SQL gives support to relational processing within the Spark programs and on data external sources. It provides support to new sources of data easily and this includes external databases and the semi-structured data. Spark SQL converts the data into highly efficient formats with different columnar formats.



It uses data partitioning, use the Data statistics for skipping the data reads, pushes predicates to the Storage System, and does late optimisation only when all possible information regarding data pipelines become available. Spark SQL supports Streaming or Batch SQL and because of the RDDs, Spark core Framework supports the batch workloads. The RDDs point to data static sets and one can use the rich API of Spark to manipulate RDDs that work on data batch sets in the memory with the lazy evaluation. 

Data export from the excel sheet and it analytics performance in real-time industry conditions is possible with Spark SQL. Data exports from the excel sheet remains highly popular format of data file maintained across various industries. It is possible to solve real-time issues with Spark SQL. This is the widely used and highly popular framework related to Spark ecosystem. One example of data analysis using Spark SQL is the following.

Consider scenario where you have customer information related to an organisation in an Excel sheet with different definitions.

  • Country defines the nationality of customers
  • Name defines customer name
  • ItemReturned defines return or its absence for purchases made
  • Itemid is item number of the purchase made by customer
  • Orderid defines customer order number as placed
  • CustomerGroup represents customers 
  • Itemprice defines purchased item prices 


The export of the dataset is as an excel sheet and for this you have to add dependencies for importing dependent libraries. As this involves Spark SQL, one has to include this in file as well. Based on the codes used the programmer might use dependant library related to spark-excel. After the inclusion of dependant library, it is time to create SparkSession for work with the DataFrame. Provide file format type for data storage such as excel, location of stored file with complete path, and input is header details. Once the data is ready, print the same to find out whether successful loading of data occurred. 

One can answer data related questions in the form of output using such codes in Spark SQL. 

Hope this blog post helped you understanding Spark sql for e commerce data analysis and stay tuned for more Big Data notes. Enroll for the big data hadoop training in Bangalore with NPN Training and become a successfull Hadoop Developer.

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