Hadoop And Spark: The Difference Between Two Big Data Frameworks
Hadoop or Spark? Hadoop processing or Spark streaming? Which is best for you? And why?
There’s a lot of confusion about the differences between these two data processing giants. But don’t worry. We’re here to explain what they are, the differences between them, and what you should use them for.
What are Hadoop and Spark?
Hadoop and Spark are both big data processors. They’re both effective, efficient, and very popular tools. Both enable you to process vast amounts of data in any format, from spreadsheets to video files.
So, broadly speaking, both Hadoop and Spark do the same job. But which is better for your purposes?
Let’s delve a little deeper.
The differences between Hadoop and Spark
What is Hadoop?
To understand the differences between Hadoop and Spark, you first need to understand a bit about their history.
Hadoop came first. Hadoop is an open-source Java framework designed for processing, distributing, and storing massive datasets.
It works on a distributed basis, meaning that the datasets involved have to be distributed over several processors: they are too large for a single computer to handle.
Hadoop’s framework involves dividing data into smaller sets and distributing them across an interconnected network of nodes and clusters. Each processor computes a single cluster, but the end user experiences all the separate computations as a single, unified process.
It’s an efficient way to process huge datasets very quickly. It’s versatile, flexible, and scalable, but it’s not without its problems.
The limitations of Hadoop
Hadoop is fantastic for big data processing in many ways, but it’s not perfect. Its limitations include:
File size. Hadoop is designed to deal with vast amounts of data. So, it expects one or two huge files to deal with, rather than several small files. If your data is stored across files smaller than 128MB, Hadoop will struggle to process it.
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Latency. Hadoop is capable of delivering large batches of data, but this comes at the expense of latency. It can take a relatively long time to retrieve one record from Hadoop.
Not real-time. The latency issue means that Hadoop is not appropriate for situations in which real-time data is needed.
Complex. Hadoop is not intuitive and takes a long time to learn.
What is Spark?
To combat the limitations of Hadoop, Apache built an ecosystem of patches, fixes, and additional services. These included everything from complete monolithic application builders to data access tools like Phoenix.
One of the tools created for the Hadoop ecosystem is Apache Spark. Spark was designed to replace Hadoop MapReduce – a batch-data processer.
Spark works similarly to Hadoop. It operates in a distributed, node-and-cluster framework and can handle similarly huge volumes of data. However, there is a crucial difference in the way that Spark processes data.
Rather than spreading data across various local drives, Spark caches data in RAM. This means that Spark is able to process data much, much faster than Hadoop can. In fact, assuming that all data can be fitted into RAM, Spark can process data 100 times faster than Hadoop.
Spark also uses an RDD (Resilient Distributed Dataset), which helps with processing, reliability, and fault-tolerance.
Unlike Hadoop, however, Spark has no native storage system. It is a pure processor. That being said, data can be sent from Spark to other storage and/or testing solutions, like Apache Cassandra or an Applause alternative.
So, Spark is fast, capable of handling data in real time, and overcomes many of the limitations of Hadoop. But it’s not perfect. Spark has its own limitations.
Limitations of Spark
Price. Because Spark uses RAM, purchasing hardware for it can be expensive.
Not totally real-time. Spark is very, very close to real-time in its processing speeds. But there is still some lag.
Small file issues. Just like Hadoop before it, Spark struggles with smaller file sizes.
Should I use Hadoop or Spark?
Both Hadoop MapReduce and Apache Spark have advantages and disadvantages that make them good for specific tasks.
Hadoop is excellent if you want to process large amounts of data at low cost, and aren’t subject to pressing deadlines. Hadoop will work away slowly but efficiently and deliver the results you need at a relatively low cost.
Spark, however, is perfect for when you need your data processed in real-time (or as close to real-time as possible), and have the budget to make it happen.
About the Writer
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and e-commerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as PPC Hero.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.