Apache Spark Review

Generality: Perform SQL, Streaming, And Complex Analytics In The Same Application

Generality is among the powerful features offered by Apache Spark. It is built with a broad range of features and capabilities that allow users to perform different types of data analytics which they can even combine in a single tool. Whether they are doing SQL-based analytics, stream data analysis, or complex analytics; the open source and unified analytics engine covers all of them.

Easily Work On Structured Data Using The SQL Module

Being a general-purpose analytics solution, Apache Spark delivers a stack of libraries that can be all incorporated into a single application. One of these libraries is a module called Spark SQL. With this module, users will be able to write and execute SQL queries so they can process and work on structured data within Apache Spark-related programs.

Take Advantage Of The DataFrame API

Aside from providing the ability to run SQL queries, Spark SQL uses a DataFrame API which is used for collecting data from various data sources such as Hive, Avro, Parquet, ORC, JSON, and JDBC; and organizing them in a distributed manner. This distributed collection of data is called a DataFrame. A DataFrame is a data set which  is arranged and structured into labelled or named columns. For users who are familiar with the relational database management system, DataFrame is similar to the table being used in such system. It is also equivalent to a data frame in R/Python.

Uniform And Standard Way To Access Data From Multiple Sources

So what’s the importance of using SQL queries and the DataFrame API? Basically, this enables users to establish a uniform and standard way of accessing data from multiple data sources. In other words, no matter how diverse the data sources they are collecting data from, Apache Spark ensures that they are able to apply a common method to connect to such sources and access all the data they need for analysis.

Supports Both Batch Data And Real-Time Data Processing

Apache Spark is an analytics engine which can handle both batch data processing and real-time data processing. Batch data processing is a big data processing technique wherein a group of transactions are gathered throughout a period of time. Then, the input data from this set of transactions are processed and batch results are generated. This technique normally requires a longer time. Thus, insights are not produced immediately, as users need to wait first until such time that all the transactions in the batch are processed.

On the other hand, real-time data processing, which is also referred to as stream data processing or real-time analytics, maintains a continuous flow of input, process, and output data, thereby allowing users to gain insights into their data within a small period of time. This data processing technique enables organizations and teams to spot issues and problems immediately and address and solve them as quickly as possible.

Stream Data Processing

Luckily, Apache Spark has component exclusively built to accelerate stream data processing This component is called Spark Streaming, and it is among the libraries available in Apache Spark. Spark Streaming lets users connect to various data sources and access live data streams. Then, the analytics engine processes the live input data streams through the aid of complex algorithms and generates live output data streams. The output or processed data can be extracted and exported to file systems, databases, and live dashboards.

Built Interactive, Scalable, And Fault-Tolerant Streaming Applications

With Spark Streaming, users will be able to create streaming applications and programs that are scalable, fault-tolerant, and interactive. As they build such applications, they can write and activate streaming jobs and tasks within the applications using high-level operators. In addition, this component of the analytics engine permits them to write and run the same codes which they can reuse for batch data processing, enabling them to run ad-hoc batch data queries against live data streams and apply real-time analytics to historical data.

High-Quality Machine-Learning Algorithms

Another great feature of Apache Spark is its utilization of powerful and high-performance algorithms which are contained in a machine learning library known as MLlib. With these algorithms, users can implement and execute computational jobs and tasks which are 100 times faster than Map/Reduce, a computing framework and paradigm which was also developed by The Apache Software Foundation for distributed processing of large data sets. These high-quality algorithms can seamlessly work on Java, Scala, Python, and R libraries; and offer high-level iteration capabilities. As a result, users will be able to process and analyze data more accurately and quickly.

Graph Analytics And Computation Made Easy

Apache Spark provides a graph processing system that makes it easy for users to perform graph analytics tasks. But what is graph analytics all about? Graph analytics is a type of data analysis method that allows users to explore and analyze the dependencies and relationships between their data by leveraging the models, structures, graphs, and other visualizations that represent those data. In other words, it enables them to analyze graph data.

Apache Spark’s graph processing system called GraphX permits users to efficiently and intelligently perform graph analytics and computation tasks within a single tool. Here, they can visualize their data as graphs, convert a collection of vertices and edges into a graph, restructure graphs and transform them into new graphs, and combine graphs together. This system is also built with graph operators which provides users with the capability to manipulate and control graph data in multiple ways. Furthermore, GraphX is equipped with graph algorithms that simplify how they apply analytics to graph data sets and identify patterns and trends in their graphs.