Apache Spark Dataframes: Features, RDD & Comparison

Have you ever wondered about the concept behind spark dataframes? The spark dataframes are the extension version of the Resilient Distributed Dataset, with a high level of abstractions. Dataframes are similar to structured traditional databases with the advancement in optimization techniques.

In this blog, we will discuss apache spark dataframes. 

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What is Apache Spark?

Apache spark is a general open-source cluster computing framework. It is a leading platform for stream processing, batch processing, and large scale SQL. Spark is known as lightning-fast cluster computing in an Apache project. It is programmed in the Scala language. Spark lets you operate programs faster than Hadoop. Also, it is a quick data processing platform. Currently, Spark supports APIs in Python, Java, and Scala, and its core is suited for a set of high level and powerful libraries such as GraphX, SparkSQL, MLlib, and Spark Streaming.

• SparkSQL: SparkSQL provides querying data through hive query or via SQL. It also offers several data sources to work with SQL with code.

• GraphX: GraphX library supports the manipulation of graphs. It offers a uniform tool for graph computation, analysis, and ETL. It also supports standard graph libraries such as Pagerank.

• MLlib: It is a library of machine learning that supports several algorithms for regression, filtering, cluster classification, clustering, etc.   

• Spark Streaming: Spark streaming provides real-time streaming data processing. It divides the input of data streams into batches. 

Read: Apache Spark Tutorial for Beginners

What is Resilient Distributed Dataset?

Spark initiates the concept of Resilient Distributed Dataset, also known as RDD. It is a distributed and immutable collection of objects that can be run in parallel. There are two operations supported by RDD, transformations operations, and action operations. The transformations operations are performed on RDD, such as union, map, join filter, etc. The actions operations return a value on RDD, such as count, reduce, first, and many more. 

Learn: 6 Game Changing Features of Apache Spark

Why do we need dataframes?

Apache Spark 1.3 version came with spark dataframes. There were two main limitations of the resilient distributed dataset. First is RDD cannot manage structured data, and second is RDD does not support any in-built optimization engine. The concept of Spark dataframes resolved the limitations of RDD.

Although, a Resilient Distributed Dataset cannot improve the system efficiently. So, to overcome the limitations of the Spark Resilient Distributed Dataset, Spark dataframes were introduced. Dataframes are organized into columns and rows. Each data frame column has an associated name and type. 

Difference between Spark Resilient Distributed Dataset and Spark DataFrames?

The below table shows the difference between Spark RDD and Spark DataFrames.

What are the features of Spark DataFrames?

• Provides management of data structure. It supports a systematic approach to view data. When the data is being stored in data frames, it has some meaning to it. 

• Spark dataframes provide scalability, flexibility, and various APIs such as java, python, R, and Scala programming.

• Utilization of optimization engines known as catalyst optimizers to process data in an efficient manner. 

• Spark data frames can also process different sizes of data.

• Dataframes support different sets of data formats such as CSV, Cassandra, Avro, and ElasticSearch. 

• Supports custom memory management and decreases the overload of garbage collection. 

Check out: Apache Spark Developer Salary in India

The Verdict

Apache is very effective and fast. Apache Spark helps to compute an in-depth high volume of processing tasks in real-time. Dataframes are useful for developing query plans. Dataframe API improves and enhances the performance of Spark. 

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