Apache Spark vs Redshift 101: Which is Best for Big Data?

By: Published: January 12, 2022

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Apache Spark has especially been a popular choice among developers as it allows them to build applications in various languages such as Java, Scala, Python, and R. Whereas, Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools.

In this article, you will be introduced to the comparative study of Spark vs Redshift for Big Data. Moreover, you will also be introduced to Apache Spark, Amazon Redshift, and their key features. Read along to learn more about the comparative study of Spark vs Redshift for Big Data.

Table of Contents

What is Apache Spark?

Spark vs Redshift - Spark Logo
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Apache Spark is an Open-Source, lightning-fast Distributed Data Processing System for Big Data and Machine Learning. It was originally developed back in 2009 and was officially launched in 2014. Attracting big enterprises such as Netflix, eBay, Yahoo, etc, Apache Spark processes and analyses Petabytes of data on clusters of over 8000 nodes. Utilizing Memory Caching and Optimal Query Execution, Spark can take on multiple workloads such as Batch Processing, Interactive Queries, Real-Time Analytics, Machine Learning, and Graph Processing.

Spark was made to overcome the challenges faced by developers with MapReduce, the disk-based computational engine at the core of early Hadoop clusters. Unlike MapReduce, Spark reduces all the intermediate computationally expensive steps by retaining the working dataset in memory until the job is completed. It has become a favorite among developers for its efficient code allowing them to write applications in Scala, Python, Java, and R. With Built-in parallelism and Fault Tolerance, Spark has assisted businesses to deliver on some of the cutting edge Big Data and AI use cases.

Key Features of Apache Spark

Over the years Apache Spark has evolved and provided rich features to make Data Analytics a seamless process. Some of its salient features are as follows:

1) Accelerated Processing Capabilities

Spark processes data across Resilient Distributed Datasets (RDDs) and reduces all the I/O operations to a greater extent when compared to MapReduce. It performs 100x faster in-memory, and 10x faster on disk. It’s also been used to sort 100 TB of data 3 times faster than Hadoop MapReduce on one-tenth of the machines.

2) Ease-to-use

Spark supports a wide variety of programming languages to write your scalable applications. It also provides 80 high-level operators to comfortably design parallel apps. Adding to its user-friendliness, you can even reuse the code for batch-processing, joining streams against historical data, or running ad-hoc queries on stream state.

3) Advanced Analytics

Spark can assist in performing complex analytics including Machine Learning and Graph processing. Spark’s brilliant libraries such as SQL & DataFrames and MLlib (for ML), GraphX, and Spark Streaming have seamlessly helped businesses tackle sophisticated problems. You also get better speed for analytics as Spark stores data in the RAM of the servers which is easily accessible.

4) Fault Tolerance

Owing to the Sparks RDDs, Apache Spark can handle the worker node failures in your cluster preventing any loss of data. All the transformations and actions are continuously stored, thereby allowing you to get the same results by rerunning all these steps in case of a failure.

5) Real-Time Processing

Unlike MapReduce where you could only process data present in the Hadoop Clusters, Spark’s language-integrated API allows you to process and manipulate data in real-time.

Spark has an ever-growing community of developers from 300+ countries that have constantly contributed towards building new features improving Apache Spark’s performance. To reach out to them, you can visit the Spark Community page. 

What is Amazon Redshift?

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Amazon Web Services (AWS) is a subsidiary of Amazon saddled with the responsibility of providing a cloud computing platform and APIs to individuals, corporations, and enterprises. AWS offers high computing power, efficient content delivery, database storage with increased flexibility, scalability, reliability, and relatively inexpensive cloud computing services.

Amazon Redshift, a part of AWS, is a Cloud-based Data Warehouse service designed by Amazon to handle large data and make it easy to discover new insights from them. Its operations enable you to query and combine exabytes of structured and semi-structured data across various Data Warehouses, Operational Databases, and Data Lakes.

Amazon Redshift is built on industry-standard SQL with functionalities to manage large datasets, support high-performance analysis, provide reports, and perform large-scaled database migrations. Amazon Redshift also lets you save queried results to your S3 Data Lake using open formats like Apache Parquet from which additional analysis can be done on your data from other Amazon Web Services such as EMR, Athena, and SageMaker.

For further information on Amazon Redshift, you can follow the Official Documentation.

Key Features of Amazon Redshift

The key features of Amazon Redshift are as follows:

1) Massively Parallel Processing (MPP)

Massively Parallel Processing (MPP) is a distributed design approach in which the divide and conquer strategy is applied by several processors to large data jobs. A large processing job is broken down into smaller jobs which are then distributed among a cluster of Compute Nodes. These Nodes perform their computations parallelly rather than sequentially. As a result, there is a considerable reduction in the amount of time Redshift requires to complete a single, massive job.

2) Fault Tolerance

Data Accessibility and Reliability are of paramount importance for any user of a database or a Data Warehouse. Amazon Redshift monitors its Clusters and Nodes around the clock. When any Node or Cluster fails, Amazon Redshift automatically replicates all data to healthy Nodes or Clusters.

3) Redshift ML

Amazon Redshift houses a functionality called Redshift ML that gives data analysts and database developers the ability to create, train and deploy Amazon SageMaker models using SQL seamlessly.

4) Column-Oriented Design

Amazon Redshift is a Column-oriented Data Warehouse. This makes it a simple and cost-effective solution for businesses to analyze all their data using their existing Business Intelligence tools. Amazon Redshift achieves optimum query performance and efficient storage by leveraging Massively Parallel Processing (MPP), Columnar Data Storage, along with efficient and targeted Data Compression Encoding schemes.

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Spark vs Redshift: Which is best for Big Data?

To analyze the comparative study of Spark vs Redshift for Big Data, you can take into consideration the following factors.

1) Spark vs Redshift: Usage

Apache Spark

The Apache Spark Streaming platform is a Data Processing Engine that is open source. Spark enables the real-time processing of batch and streaming workloads. It is developed in Java, Scala, Python, and R, and it builds applications using pre-built libraries.

It is a quick, simple, and scalable platform that accelerates development and makes applications more portable and faster to operate.

Amazon Redshift

Amazon Redshift is a Fully Managed Analytical Database that handles tasks such as constructing a central Data Warehouse, conducting large and complicated SQL queries, and transferring the results to the dashboard.

Redshift receives raw data and processes and transforms it. Amazon Redshift is a service that allows you to analyze larger and more complicated datasets.

2) Spark vs Redshift: Data Architecture

In simple terms, you can use Apache Spark to build an application and then use Amazon Redshift both as a source and a destination for data.

A key reason for doing this is the difference between Spark and Redshift in the way of processing data, and how much time each of the tools takes.

  • With Spark, you can do Real-time Stream Processing: You get a real-time response to events in your data streams.
  • With Redshift, you can do near-real-time Batch Operations: You ingest small batches of events from data streams and then run your analysis to get a response to events.

Let’s take a real-life application to showcase the usage of both Apache Spark and Amazon Redshift.

Fraud Detection: You could build an application with Apache Spark that detects fraud in real-time from e.g. a stream of bitcoin transactions. Given its near-real-time character, Amazon Redshift would not be a good fit for the given use case.

Suppose if you want to have more signals for your Fraud Detection, for better predictability. You could load data from Apache Spark into Amazon Redshift. There, you join it with historic data on fraud patterns. But you can’t do that in real-time, the result would come too late for you to block the transaction. So you use Spark to e.g. block a transaction in real-time, and then wait for the result from Redshift to decide if you keep blocking it, then you can send it to a human for verification, or approve it.

You can also refer to the Big Data blog, “Powering Amazon Redshift Analytics with Apache Spark and Amazon Machine Learning” published by Amazon that showcases the usage of both Apache Spark and Amazon Redshift. The post covers how to build a predictive app that tells you how likely a flight will be delayed. The prediction happens based on the time of day or the airline carrier, by using multiple data sources and processing them across Spark and Redshift.

The image given below illustrates the process flow for the following blog.

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In the above process, it can be seen that the separation of Applications and Data Warehousing showcased in the article is in reality an area that’s shifting or even merging.

3) Spark vs Redshift: Data Engineering

The traditional difference between Developers and BI analysts/Data Scientists is starting to fade, which has given rise to a new occupation: data engineering.

Maxime Beauchemin, creator of Apache Superset and Apache Airflow states “In relation to previously existing roles, the Data Engineering field [is] a superset of Business Intelligence and Data Warehousing that brings more elements from software engineering, [and it] integrates the operation of ‘big data’ distributed systems”.

Apache Spark is one such “Big data” Distributed system, and Amazon Redshift comes under Data Warehousing. And Data engineering is the field that integrates them into a single entity.

4) Spark vs Redshift: Final Decision

In this Spark vs Redshift comparative study, we’ve discussed:

  • Use Cases: Apache Spark is designed to increase the speed and performance of application development, whereas Amazon Redshift is designed to crunch big datasets more rapidly and effectively.
  • Data Architecture: Apache Spark is best suited for real-time stream processing, whereas Amazon Redshift is best suited for batch operations that aren’t quite in real-time.
  • Data Engineering: The discipline of “Data Engineering,” which includes Data Warehousing, Software Engineering, and Distributed Systems, unites Apache Spark with Amazon Redshift.

You’ll almost certainly end up utilizing both Apache Spark and Amazon Redshift in your big data architecture, each for a distinct use case that it’s best suited for. We can stick to the notion that they serve diverse use cases and that the tool to employ is determined by the use cases.


In this article, you have learned about the comparative study of Spark vs Redshift for Big Data. This article also provided information on Apache Spark, Amazon Redshift, and their key features.

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Share your experience of understanding the comparative study of Apache Spark vs Redshift for Big Data in the comment section below! We would love to hear your thoughts.

Former Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has written more than 100 articles on diverse topics related to data industry.

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