Nearly a decade after Amazon announced Redshift, its competitor to Apache’s Hadoop, the Hadoop vs. Redshift debate continues. For companies seeking a reliable data warehousing solution, both are worthy contenders, but examining the following aspects will help you decide the winner for yourself.

  1. Hadoop scales endlessly with no reshuffling required. Redshift won’t expand beyond 16 petabytes.

  2. Redshift performs faster when working with terabytes of data, but Hadoop is faster at handling petabytes of data.

  3. Both Hadoop and Redshift require a close evaluation of your deployment requirements to determine cost.

  4. Developers familiar with PostgreSQL can easily start using Redshift. However, your team will need to learn specific architecture and tools to use Hadoop.

  5. Both Redshift and Hadoop accept a variety of data types, making it a tie in this regard.

So, how does Hadoop compare with Amazon Redshift?'s powerful ETL tools seamlessly connect with your data sources no matter which provider you choose, but selecting the wrong data warehouse could still cost you big in the long term. While both Hadoop and Redshift are reliable, you're about to learn how some key differences in architecture impact cost and performance at scale. 

Table of Contents

  1. Hadoop vs. Redshift: The Basics
  2. Hadoop vs. Redshift: Scaling
  3. Hadoop vs. Redshift: Performance
  4. Hadoop vs. Redshift: Pricing
  5. Hadoop vs. Redshift: Ease of Use
  6. Hadoop vs. Redshift: Formats and Types
  7. Hadoop vs. Redshift: Data Integrations
  8. Hadoop vs. Redshift: The Winner
  9. How Can Help

If you're in the middle of the Hadoop vs. Redshift debate, here's what you need to know to make an informed decision. 

Hadoop vs. Redshift: The Basics

Apache Hadoop is an open-source framework for distributed processing and storage of big data on commodity machines. It uses HDFS, a dedicated file system that cuts data into small chunks and spreads it optimally over a cluster.

Hadoop processes the data in parallel on the machines via MapReduce (Hadoop 2.0, aka YARN, allows for other applications as well). It’s a common misconception that Apache Spark was intended to replace Hadoop. However, Spark is designed to run on top of your Hadoop cluster.

Meanwhile, Amazon Redshift’s data warehouse-as-a-service (WaaS) is technology acquired from the data warehouse vendor ParAccel. Redshift is fully managed by Amazon Web Services (AWS), which means the underlying resources are provisioned for you. Redshift is based on an old version of PostgreSQL, with three major enhancements:

  • Columnar database: A columnar database returns data by columns rather than whole rows. It provides better execution of aggregating large sets of data, making it perfect for analytical queries.

  • Sharding: Redshift supports data sharding—that is, partitioning the tables across different servers for enhanced performance.

  • Scalability: With everything running on the Cloud, Redshift clusters can be easily upsized and downsized.

Both Hadoop and Redshift live in the Cloud, so your environment will shrink or grow as needed. Additionally,’s ETL tools seamlessly plug into both Hadoop and Redshift, creating a low-code solution that supports your business intelligence, analytics, and machine learning initiatives. 

All in all, the platforms share certain qualities, but they take two distinct approaches that require closer examination before you can choose which one is right for you. To help, here’s the side-by-side comparison of Hadoop vs. Redshift you’ve been searching for.

Hadoop vs. Redshift: Scaling

In 2021, Redshift doubled its built-in maximum storage capacity for RA3.16XL and RA3.4XL node types to 128 TB per node. This means that a Redshift cluster can now manage up to 16 petabytes of data. However, in the unlikely event that you have more than this, or if you expect to exceed this limit in the near future, Redshift won’t work for you. 

At the most recent count, the average enterprise had a data volume of 2.02 petabytes. What's more, that number is increasing at a rate of 42% every year. Even though this is far from the limit of Amazon’s clusters, it's important to remember how these resources scale. In AWS Redshift, data needs reshuffling among the machines, which could take several days and plenty of CPU power—slowing down your system and blocking other operations.

Fortunately, Hadoop scales to as many petabytes as you want. Twitter, for example, reportedly has a 300-petabyte Hadoop cluster that it hosts in the Google Cloud. What’s more, scaling Hadoop doesn’t require reshuffling, since new data saves to the new machines. If you do want to balance the data, there is a Hadoop rebalancer utility available.

The first round goes to Hadoop. 

Hadoop vs. Redshift: Performance

According to several performance tests done by the team over at Airbnb, a Redshift 16-node dw.hs1.xlarge cluster performs a lot faster than a Hive/Elastic MapReduce 44-node cluster. For example, in a simple range query against 3 billion rows of data, Hadoop took 28 minutes to complete, while Redshift took just 6 minutes. Another Hadoop vs. Amazon Redshift benchmark for FlyData, a data synchronization solution for Redshift, confirms that Redshift performs faster when working with terabytes of data.

Nonetheless, there are some constraints to Redshift’s super speed. Certain Redshift maintenance tasks have limited resources, so procedures like deleting old data could take a while. Although Redshift shards data, it doesn’t do it optimally. You might end up joining data across different compute nodes and miss out on the improved performance.

Plus, Hadoop still has some tricks up its utility belt. FlyData’s benchmark concluded that while Redshift performs faster for terabytes, Hadoop performs better for petabytes. Airbnb agrees and states that Hadoop does a better job of running big joins over billions of rows.

Unlike Redshift, Hadoop doesn’t have hard resource limitations for maintenance tasks. As for spreading data across nodes optimally, saving it in a hierarchical document format should do the trick. It may take extra work, but at least Hadoop has a solution.

In this round, we have a tie: Redshift wins for terabytes, while Hadoop takes the prize for petabytes.

Hadoop vs. Redshift: Pricing

The question of Hadoop vs. Redshift pricing is a tricky one to answer. Amazon claims that “Redshift costs less to operate than any other data warehouse.” However, Redshift’s pricing depends on the choice of region, node size, storage type, and whether you work with on-demand or reserved resources.

Paying $1000/terabyte/year might sound like a good deal, but it only applies for three years of a reserved XL node with 2 terabytes of storage in the U.S. East (North Virginia) region. Working with the same node and the same region on demand costs $3,723/terabyte/year, more than triple the price, and choosing the region of Asia Pacific costs even more.

On-premises Hadoop is definitely more expensive. According to Accenture’s "Hadoop Deployment Comparison Study", the total cost of ownership of a bare-metal Hadoop cluster with 24 nodes and 50 terabytes of HDFS is more than $21,000 per month. That’s about $5,040/terabyte/year, including maintenance. However, it doesn’t make sense to compare pears with pineapples, so let’s stick to comparing Redshift with Hadoop as a service (HaaS).

Pricing for Hadoop as a service isn’t exactly transparent, since it depends on how much juice you need. FlyData claims that running Hadoop via Amazon’s Elastic MapReduce is 10 times more expensive than Redshift.

Using Hadoop on Amazon’s EC2 is a different story, however. Running a relatively low-cost m1.xlarge machine with 1.68 terabytes of storage for three years (heavy reserve billing) in the U.S. East region costs about $124 per month, so that’s about $886/terabyte/year. Working on demand or in a different region, or using SSD drive machines, will increase prices.

There's no clear winner in this round—it all depends on your needs.

Hadoop vs. Redshift: Ease of Use

Redshift automates data warehouse administration tasks, as well as automatic backups to Amazon S3. Transitioning to Redshift should be a piece of cake for PostgreSQL developers since they can use the same queries and SQL clients they’re used to.

Handling Hadoop, whether in the Cloud or not, is another story. Your system administrators will need to learn Hadoop-specific architecture and tools, and your developers will need to learn coding in Pig or MapReduce. Heck, you might even need to hire new staff with Hadoop expertise. There are, of course, Hadoop-as-a-service solutions that can save you all that trouble (ahem). However, most data warehouse devs and admins will still find it easier to use Redshift.

In either case, setting up a data integration solution on the new ETL platform should be fast and seamless.'s ETL, Reverse ETL, and lightning-fast CDC platform provide pre-built connectors, a robust API, automated workloads, and deep e-commerce capability—all designed to get you up and running fast whether you choose Redshift or Hadoop.

With all of that said, Redshift wins when it comes to ease of use. 

Hadoop vs. Redshift: Formats and Types

When it comes to file formats, both Redshift and Hadoop are fairly cooperative. Redshift accepts both flat text files and formats such as CSV, Avro, JSON, Parquet, ORC, and shapefiles. Hadoop, like Redshift, accepts a wide variety of file formats, including text files, CSV, SequenceFiles, Avro, Parquet, RCFile, and ORC.

In terms of data types, things get a little more complicated. If your choice of Hadoop or Redshift doesn’t support the data types you need, you’ll need to spend time converting your data before you can use it with Redshift. 

Redshift supports the following data types:

  • SMALLINT (two-byte integers)
  • INTEGER (four-byte integers)
  • BIGINT (eight-byte integers)
  • DECIMAL (exact numeric of selectable precision)
  • REAL (single-precision floating point)
  • DOUBLE PRECISION (double-precision floating point)
  • BOOLEAN (true/false)
  • CHAR (fixed-length string)
  • VARCHAR (variable-length string)
  • DATE (calendar date)
  • TIMESTAMP (date and time without time zone)
  • TIMESTAMPTZ (date and time with time zone)
  • GEOMETRY (geospatial data)
  • HLLSKETCH (special data type for HyperLogLog)
  • TIME (time of day without time zone)
  • TIMETZ (time of day with time zone)

Hadoop supports the following data types:

  • INT
  • CHAR
  • DATE
  • MAP

As you can see, both services offer very similar data types, including integers and floating-point numbers, strings, and time-based data. But they’re not completely identical: Redshift has data types for geospatial data and the HyperLogLog algorithm, while Hadoop has complex data types such as arrays, structs, and maps.

It’s another tie—we’ll let you decide which file formats and data types are most important.

Hadoop vs. Redshift: Data Integrations

The most common data integrations for Redshift are loading data from Amazon S3 or DynamoDB. However, you can also load data into Redshift from Amazon Elastic MapReduce (EMR) or from remote hosts.

Unless you load all of your Redshift data from a remote host, you’ll need to store it within the AWS ecosystem. Not only will you have to use more of Amazon’s services, but you’ll also spend extra time preparing and uploading the data.

Redshift loads data via a single thread by default, so it could take some time to load. Amazon suggests certain best practices to speed up the process, such as splitting the data into multiple files, compressing the files, using a manifest file, etc. Moving the data to DynamoDB is, of course, a bigger headache, unless it’s already there.

If your data is already stored in Redshift, eliminates some of the pain with its low-code solution. Still, life is more flexible with Hadoop. You can store data on local drives, in a relational database, or in the cloud (including in S3), and then import it straight into the Hadoop cluster—and you can still enjoy seamless solutions from 

Another round for Hadoop.

Hadoop vs. Redshift: The Winner

In truth, there is no clear winner in the Hadoop vs. Redshift debate. Each platform has its distinct advantages and characteristics that make it more suitable for certain use cases than others. 

When you need relatively cheap data storage, or you're processing batches in petabytes or non-relational formats, opt for Hadoop-Signal. When you need analytics, fast performance for terabytes, and an easier transition for your PostgreSQL team, look to Amazon Redshift. 

As Airbnb concluded: "We don’t think Redshift is a replacement of the Hadoop family due to its limitations, but rather it is a very good complement to Hadoop for interactive analytics." We couldn't have said it better ourselves.

How Can Help

Whether you’re using Redshift or Hadoop, provides a powerful ETL and reverse ETL toolset, along with a lightning-fast CDC to drive your company’s growth. 

Our new cloud-based ETL platform provides simple visual data pipelines for building automated data workflows across a wide range of sources and destinations. Plus, we provide the fastest real-time data replication on the market with a CDC that’s trusted by the world’s largest companies. 

Interested in learning more about Contact our team today to schedule an intro call and see the platform in action for yourself.