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DataFrame vs. Spark SQL: Differences and Comparison

There are a wide variety of open-source libraries and resources available for Python. Some of the several popular packages in analytics, engineering, and science that might already be familiar to an analyst, engineer, or scientist are Numpy, Pandas, Scikit-learn, Keras, and TensorFlow. 

Together, these modules bring value to the analytics field by extracting value from data. The Apache Spark framework is an excellent choice for processing Big Data as data grows and becomes more complex. 

We love SQL because it provides a nice declarative language for queries and enforces essential structure and constraints. However, relational databases have always had a problem with scalability. Today, most enterprises have rich data repositories and stores and want to get the most out of their Big Data for actionable insights. 

A good Big Data management strategy is needed if we want to scale relational databases well. The variable types that must be considered in a comprehensive plan are constraints, extract-transform-loads (ETL), volumes, sources, schemas, access patterns, and query patterns.

A key component of data science is maximizing the value of data by utilizing machine learning. Therefore, it becomes important to study its distribution and statistics to extract useful insights from the data. Structured or unstructured data can be considered in a broader sense.

Semistructured and unstructured data include formats such as JSON, audio, video, and social media postings, while structured data follows a clearly defined schema. 

DataFrames represent tables of rows and columns, regardless of the programming language. Spark DataFrames and Pandas DataFrames have many differences, however. Let’s discuss Spark SQL vs. DataFrame and the difference between the two in depth.

Relational Databases

Database systems like relational database management systems store and retrieve data as tables with rows and columns. Several databases are based on the relational DBMS principles, including SQL, My-SQL, and Oracle.

Data about related objects are stored in tables in Relational Databases. There are attributes in each column, while keys are unique information recorded in each row. Thus, it is easier to understand how different data points relate.

SQL stands for Structured Query Language, which manages relational databases or RDBMS. By performing operations such as JOIN, TRUNCATE, etc., SQL codes are used to retrieve information from relational databases.

The HeroVired Certificate program in Data Engineering integrates learning of relational database tools such as SQL and No SQL to get hands-on practice on relational databases.

Open-source Python library Pandas implements NumPy’s functions. With this Python package, you can manipulate both numerical data and time series using various data structures and operations.

Overall, it simplifies the import and analysis of data. In data frames, the axes are labeled, and the dimensions can be heterogeneous. There are three major components to a Pandas DataFrame: rows, data, and columns.  

What is Spark SQL?

Spark SQL supports query SQL natively, allowing queries on distributed datasets and external data sources. There is little difference between RDDs and relational tables when using Spark SQL vs Spark DataFrame.

This powerful abstraction enables developers to combine SQL commands with complex analytics within a single application, all within the same framework. As a result of Spark SQL, developers will be able to:

• Parquet files and Hive tables can be used to import relational data.
• Import existing RDDs and run query SQL on them.
• Parquet or Hive tables can be created easily from RDDs.

The cost-based optimizer of Spark SQL is complemented with code generation and columnar storage. Due to the Spark engine’s scalability, it is possible to run a query across thousands of nodes over many hours, allowing for full fault tolerance for everything from mid-query queries to historical queries.  

There is a little difference between Spark SQL vs Spark DataFrame. Although both perform the same, still Spark SQL has shown a slight advantage during sorting and aggregation.

Features of DataFrame

• Storage of data of any type (heterogeneous).
• There is a possibility of changing the data size.
• Change of values.
• It is possible to label both indexes.
• Numbers, strings, characters, and Boolean values can represent index values.
• ‘Axis’ can also be referred to as the index of a data frame. Column indexes are indicated by axis = 1, whereas row indexes are indicated by axis = 0.

Features of Spark SQL

There are many valuable features included in Spark DataFrame:

• Hive can work with various data formats, such as CSV, XML, JSON, RDDs, Cassandra, Parquet, and RDDs.
• Integration support for a variety of Big Data tools.
• On smaller machines, kilobytes of data can be processed, while petabytes can be processed on clusters.
• Data processing optimizer for multiple languages with catalyst.
• Schematic view of data for structured data handling.
• As opposed to RDDs, this memory management offers more flexibility to reduce overload and increase performance.
• An API for Java, Python, R, and Spark is available.

Next, we will check the difference between DataFrame and Spark SQL and conclude which one you should choose for your next project.

Let’s compare DataFrame and Spark SQL

Let us understand the differences between Spark SQL vs DataFrame in depth with the help of a table.

Advantages and disadvantages of DataFrame

Advantages

• The features applicable to data frames with Pandas include indexing, renaming, sorting, and merging.
• Pandas make it more convenient to update, add, and delete columns.
• Pandas Dataframe supports multiple file formats.
• The inbuilt function causes a high processing time.

Disadvantages

• When dealing with a large dataset, manipulation becomes more complex.
• Manipulations can take a long time to process.

Advantages and disadvantages of Spark SQL

Advantages

• Several APIs available for Spark makes it easy to operate large datasets.
• Besides MAPs and reduces, it can also support graph algorithms, machine learning (ML), SQL queries, streaming data, and a lot more.
• A lot of computation happens in-memory (RAM) when Spark is running.
• To develop parallel applications with this framework, it offers 80 high-level operators.

Disadvantages

• It is not possible to automate the optimization process.
• Algorithms are very few.
• A problem with small files has been reported.

Use cases and application of DataFrame

• Aspects of economics
  

Data analysis is always in demand in economics. For economists to understand how the economy is performing in various sectors, they need to analyze data and form patterns. Python and Pandas have therefore become popular among economists for analyzing huge datasets. Several functions are provided by Pandas, including handling files and data frames. 

• Developing a recommendation system
  

Spotify and Netflix provide brilliant recommendations, and we’ve all been stunned by their incredible accuracy. Deep Learning has made these systems possible. Pandas library is most commonly used for providing recommendation models. Pandas is Python’s most commonly used library when handling data in these models.

• Prediction of stock prices
  

There is a great deal of volatility in the stock market. Even so, it is not impossible to predict. It is easy to make models that predict stocks’ movement with Pandas and other libraries such as NumPy and Matplotlib. 

• Neuroscientific approach
  

Throughout history, humankind has been fascinated by the nervous system due to the number of mysteries about our bodies. Pandas and their various applications have helped this field immensely through machine learning. 

• Statistical data
  

In pure mathematics itself, Pandas has been making great strides with its various applications. Due to statistics’ reliance on data, libraries like Pandas which handle data, have been helpful in many ways. 

Use cases and application of Spark SQL

• Financial sector
  

Across different functional and technological domains, Spark is used in the Finance industry.

It is common to build a Data Warehouse for batch processing and daily reporting. It has been used to ingest data from multiple sources with different formats using the Spark data frames abstraction.

In addition to creating and training fraud detection models, financial services companies use Apache Spark MLlib. Money transfer text is being classified by some banks using Spark.

• Medical care
  

The healthcare industry uses big data and machine learning to provide hi-tech services to patients. The latest Healthcare applications are driven by Apache Spark, which is penetrating rapidly. Based on patients’ medical histories and learning, hospitals use Spark-enabled healthcare applications to identify potential health issues.

Spark also solves the challenge of quickly processing massive amounts of healthcare data. 

• Retail sector

The usual problem for big retail chains is optimizing their supply chain to minimize costs and wastage, improve customer service and gain insights into customer shopping behaviors. Spark will enable them to serve better and, in the process, maximize profits.

Keeping the inventory current based on sales and predicting sales during promotional events and sales seasons are some of the challenges these retailers have to overcome to achieve these goals. Keeping track of customer orders must also be done. All these pose huge technical challenges. 

Companies use Apache Spark and MLlib to ingest real-time sales and invoice data, figure out inventory, and capture it. Real-time tracking of an order’s transit and delivery can also be done using the technology. Spark MLlib analytics and predictive models help match inventory during a sales promotion. 

How to choose which one to use?

A typical exploratory project is unlikely to warrant the implementation of Spark, given modern hardware specifications and Pandas’ ability to optimize calculations. We have even looked at the Spark SQL vs DataFrame performance. Spark is, however, a good choice in certain cases.  

1. In addition to being highly distributed and persistent, Spark is also suitable for pipelined applications. When Pandas matures, it may be appropriate to start with a small sample and migrate to Spark later. This Spark technology predicts market trends, customizes customer experiences, and forecasts the weather.
2. Usually, these applications entail significant calculations with large and long data. Spark can be used for these applications.
3. Spark provides high-level APIs for Machine Learning, which allows for creating Machine Learning pipelines using MLlib. Spark’s framework is ideal for running Machine Learning Pipelines because model tuning is an iterative process.

Pandas DataFrame is very capable of rapid calculation. The syntax of Spark DataFrame is designed to match Pandas, making implementation easier, but ad-hoc analysis may not be desirable. It is very powerful with well-established data processes, especially when large amounts of data are expected.

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