Spark, Hadoop, and Kafka Compared (Tutorial)

In the “big data” world, the terms Spark, Hadoop, and Kafka should sound familiar. However, with numerous big data solutions available, it may be unclear exactly what they are, their main differences, and which is better. Below is a comprehensive view to determine what kinds of applications, such as machine learning, distributed streaming, and data storage that you can expect to make effective and efficient by using Hadoop, Spark, and Kafka.

What is Hadoop?

Hadoop is an open-source software that stores massive amounts of data while running  large numbers of commodity-grade computers to tackle tasks that are too large for a single computer to process on its own. Hadoop can be used to write software that stores data or runs computations across hundreds or thousands of machines without needing to know the details of what each machine can do, or how it can communicate. Failures are considered a fact of life in this environment. Hadoop is designed to handle them within the framework itself, which significantly reduces the amount of error handling necessary within your solution. At its most basic level of functionality, Hadoop includes the standard libraries between its modules, the file system HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and its implementation of MapReduce. You’ll hear people refer to Hadoop in different ways. Hadoop itself is an entire set of solutions called the Hadoop Ecosystem. This ecosystem includes the basic Hadoop functionality, as well as whatever additional modules, generally referred to as submodules, that are plugged into the system. Two prominent Hadoop Ecosystem projects are Spark and Kafka.

What is Spark?

Apache Spark is a solution aiming to improve Hadoop’s MapReduce implementation by making it both easier and faster. One of the applications that initially motivated the development of Spark is the training algorithm for machine learning systems. After Spark became more popular, it added specialized modules like MLLib, Spark SQL, Spark Streaming, and GraphX. Spark SQL supports structured data, Spark Streaming performs streaming analytics, and MLLib is the machine learning framework. GraphX supports graph applications if they don’t need to be updated or maintained in a database. Spark, like Hadoop, is also fault tolerant. Its failure tolerance is in the form of its RDD (Resilient Distributed Dataset).

What is Kafka?

Another major project in the Hadoop Ecosystem is Apache Kafka. It takes information from many different sources, called producers, and organizes it all into a format that’s easier for a stream processing system like Spark to manage. The information is then made available to the receiving process, called a consumer, in a way that allows the process to browse messages by topic within the Kafka cluster. When combined, Hadoop, Spark, and Kafka become a solid foundation for a machine learning system. It can take in a significant amount of data from many different producers quickly, process it efficiently — even when the operations are iterative — and then send it back out directly to the consumers. See also: Challenges Logging Kafka at Scale

Is Hadoop Required for Spark?

The short answer is no. Spark has a standalone mode that doesn’t require any other software, although it is often easier to deploy and manage Spark using Hadoop’s YARN framework. When it is used with Hadoop it coordinates well with Hadoop YARN. There is even an option to run a “local mode.” This mode is typically only used for testing and development and allows you the flexibility to operate Spark on a single machine. Each executor is assigned its own CPU core, which enables the system to scale up to as many CPU cores as your machine can spare for Spark’s use.

Is Spark Faster than Hadoop?

As mentioned previously, Spark was initially created to resolve limitations in the MapReduce approach to big data problems. It’s possible you’ll see your latency significantly reduced when you use Spark over Hadoop’s existing MapReduce approach. In a paper out of University of California, Berkeley, researchers were able to prove that Spark can outperform Hadoop by 10x in iterative machine learning workloads and can be used interactively to scan a 39 GB dataset with sub-second latency.” If you’re targeting machine learning applications, it’s worth adding a Spark cluster to your Hadoop solution. It’s likely you’ll see significant benefits.

Getting Started with Hadoop

When you initially stand up Hadoop, you’ll need to check out some best practices and make some decisions about how you’ll want to use it. Starting small and building multiple environments are good basic advice whenever you’re working with something new. In this case, it’s essential. Large Hadoop clusters are notorious for being difficult to use and administer. There are a few options on the market that can help you deploy Hadoop much easier. Be sure to avoid these three traps that are easy to fall into when you’re first getting your feet wet:

  1. Poor Data Organization
  2. Overbuilding
  3. Virtualizing Data Nodes

Poor Data Organization

It’s easy to dump your data into a massive pile into Hadoop and decide you’ll deal with the structure of all that data later. The reality is that once the data is in there and you add more, then more, and even more, putting the organization in after the fact is impractical. Take some time in advance to consider how you want your data organized. It’ll save you a lot of time later.


There are many options when it comes to transitioning to Hadoop. Adding in one thing at a time will keep you from implementing something that isn’t an ideal choice for your application. It’s tempting when you’re working with open source to keep adding on new things. Try to hold back that impulse somewhat and only implement what you need to satisfy your base requirements initially. It’s substantially easier to add stuff in Hadoop than it is to take them out. This philosophy is especially relevant if you have an existing business architecture you’re transitioning over that’s still in active use by customers. Choosing the parts of the system that benefit most from the transition and testing them one at a time will provide a significantly higher chance of a successful transition.

Virtualizing Hadoop

Virtualization can add value if you wish to use it with your master nodes. Virtualizing your data nodes doesn’t make much sense. You can do this if you want to and the implementation will still work. Instead, it would be better to put a single large data node on the server instead of virtualizing the server and breaking it up into several smaller ones. Hadoop can handle the large data volume, and it’s best to let the software choose how to get your data distributed efficiently to the hardware.

Adding Spark

If you’ve decided to skip Hadoop and implement Spark as its own cluster, then you’ll start here. If you did stand up Hadoop, you might be wondering whether to add Spark to your Hadoop setup. Spark uses more resources for processing, but the overall performance is better. If you’re working on a machine learning application you should give Spark a try using the MLLib library specifically for iterative machine learning applications.

Organizing Data Input with Kafka

Now that you know what Spark and Hadoop are, it’s time to look at Kafka. For machine learning applications, you frequently have many producers of data that need to be organized in a way that can be processed efficiently. One option is to use Kafka to capture your data streams, format, and record them in HDFS (Hadoop Distributed File System). Once everything is stored, it can then be processed by batches in either Hadoop with MapReduce or with Spark in scenarios like machine learning applications. The commit log approach allows the rest of your system to subscribe to and receive data as broadly as possible on a continuous and timely basis. Once you have everything connected and working correctly, your producers will rapidly write into the Kafka cluster. Spark will operate on the data and write the results back to Kafka where the consumer can subscribe to receive those results in real time.


The way Kafka utilizes a commit log also has another excellent benefit — observability. You’ll be able to implement a monitoring feature which subscribes to the data coming in from your producers and going out to your consumers. Real-time access to these two parts of the system will allow you to see everything, and filter down the information as needed. You’ll even be able to catch up on messages if you fall behind. The advantages over a similar, more traditional approach to this problem can be described in three main benefits as follows:

  1. No message broker needed
  2. The order is maintained in the messages, even with parallel consumers operating at the same time
  3. You’ll still be able to read your old messages

See Also: The Future of DevOps Observability

Keep Scale in Mind

The combination of Hadoop, Spark, and Kafka are an excellent solution for heavy duty applications where there is a large amount of data and reliability is critical. Big data and machine learning applications on a large scale are unique examples of how this setup will provide excellent benefits in overall performance. For smaller applications, or if you’re not sure whether this combination is right for you, start small with just Hadoop or Spark. You can add Kafka later, and you can still plug Spark into Hadoop if you decide you need the additional performance.

Choosing the Best Cluster Hosting Services

Once you have a thorough understanding of Hadoop, Spark, and Kafka, it’s a good idea to look around and see if there are other parts of the Hadoop Ecosystem that might benefit your application. As an open source project, Hadoop provides the flexibility to experiment with different sub-modules and interface other solutions with minimal software costs. Establishing that your selection of software is the best set for your application will save you a significant amount of pain and re-work in the long run. There are a few options for cluster hosting services, so it’s best to shop around. It’s worth checking with the rest of your organization to see if there are any special rate options or credits you can use to experiment. Spark itself can initially be run on a standalone machine. As you scale up, you’ll want to give it its own cluster and let it stretch its legs to show you what it can really do in a closer-to-live environment. In addition, if you’re considering standing up a live system soon, you may be able to get some free development time- assuming you’re willing to commit to using the same service when you go live.

Hadoop vs Spark vs Kafka – Things to Consider

Consider how you’ll implement security features before you make a final push to build a fully functional system. Designing security friendly applications begins early in the design and development process. For example, if you wait until the end to think about how you want to provide the security, you may discover some design choices that significantly limit what the system can accomplish in a securely. User authentication can be achieved in multiple ways, and the security profile is different across a variety of options. Also, the sensitivity of the data may push you to store it differently, e.g. local data storage versus cloud data storage is a critical design choice which will drive how you handle the security of your data. The Hadoop Ecosystem is a continuously evolving space with new ideas coming in all the time. Keep an eye out for more updates and information related to Hadoop and its related projects. If you can’t find a feature you need right now, someone might already be working on it. If not, maybe you can start an open source project of your own. One of the best ways to give back for your use of open source projects like Hadoop is to contribute ideas and implement useful features into the code base for others to use.

Log Data Becomes Big Data as You Scale

If you’re looking into Hadoop, Spark or Kafka because you’re looking for a solution to manage, scale, and search logs because it’s becoming unmanageable, LogDNA can help you with that. The largest data set in any organization easily becomes the log data from all of your IT and application infrastructure and used by developers, operations, security, business, and product. However, to take advantage of it, the infrastructure to collect, store, parse, search logs, and be alerted is not trivial to create.  LogDNA is a modern multi-cloud centralized logging solution with per-GB pricing plan that grows as you grow.

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