Main features of Apache Spark are:
The main benefits of Apache Spark are its generality feature, SQL module used to query structured data, uniform way of data access for multiple sources, faster stream data processing, high-quality machine learning algorithms, and easy graph analytics and computation. Here are more details:
One of the most powerful features of Apache Spark is the generality. Built with a wide array of capabilities and features, it empowers users to implement various types of data analytics that they can aggregate in one tool. The unified and open-source analytics engine covers all the required processes, from performing SQL based analytics up to complex analytics.
SQL Module for Easy Querying of Structured Data
Apache Spark can be used as a general purpose analytics platform which is why it delivers a group of libraries that can be integrated into one application. One of which is the module named Spark SQL that users can utilize to write as well as execute SQL queries so that they will be able to work and process on structured data within the programs related to Apache Spark.
Standardized Way of Accessing Data from Different Sources
With the use of DataFrame API and SQL queries, users are able to establish a standard and uniform way of data access that comes from multiple data sources. This means that regardless of how diverse the sources where data are collected from, Apache Spark makes sure that users can apply a common method for connecting to the sources.
Accelerated Stream Data Processing
Apache Spark is equipped with a component that is built specifically for accelerating stream data processing called Spark Streaming and is one of the available libraries in the system. Spark Streaming enables users to connect different data sources as well as access real-time data streams. After which, the analytics engine processes the real-time input data streams with the help of complex algorithms and then generates a live output of data streams.
High-level Machine Learning Algorithms
Another highlight of Apache Spark is the usage of high performing algorithm contained within the machine learning library or MLlib. Using these algorithms, users are able to execute and implement computational tasks and jobs a hundred times faster than Map/Reduce. The high-quality algorithms can work seamlessly on R libraries, Python, Scala, and Java and offer high performing iteration capabilities as well.
Easy Graph Computation and Analytics
Apache Spark gives users a graph processing system, called GraphX, which empowers a more intelligent and efficient performance of graph computation and analytics works within one tool. Using GraphX, users are able to visualize the data they have as graphs, then convert the collection of edges and vertices into a graph. After that, graphs are restructured and transformed into new graphs, and finally, combine them together.
The following Apache Spark integrations are currently offered by the vendor:
Apache Spark pricing is available in the following plans: