Hadoop vs Spark: The Battle of the Big Data Frameworks

Having a good big data strategy is vital for every firm today. The amount of information companies generate about their business, their customers and the wider market is growing exponentially. If you're not able to effectively tap into these resources and derive insight, you'll be missing out on huge opportunities to grow revenue or increase efficiency.

Therefore, you need solutions that can prepare very large data sets for analysis, process them quickly and manage the results. And when it comes to choosing the best technologies for achieving this, there will be two names that immediately pop up - Hadoop and Spark.

But which of these will work best for you? Here's what you need to know.

What is Hadoop?

Hadoop has been around since 2006 and is an open-source big data analytics project developed by Apache. It's a general-purpose solution for data processing and is based around distributed processing technology, which shares the load across many different machines.

It consists of four main modules that work together to deliver results. These are:

• HFDS: The Hadoop Distributed File System handles the process of distributing, storing and accessing data across multiple servers, and is built to work with both structured and unstructured data.
• YARN: Standing for Yet Another Resource Negotiator, this is Hadoop's cluster resource manager. It's responsible for executing distributed workloads, scheduling jobs and allocating compute resources where they're needed.
• MapReduce: The framework that enables Hadoop to run parallel computation of data, this manages the process of splitting large computation jobs into smaller ones that can be spread out across different cluster nodes.
• Hadoop Common: This handles the underlying set of common Java libraries that can be used across all parts of Hadoop.

What is Spark?

Spark is also an open-source analytics solution managed by Apache. Like Hadoop, it's built to organize large-scale processing of data, but it uses in-memory technology to process data using RAM. In principle, this makes it much faster than Hadoop and better able to handle certain use cases. Spark is a newer solution than Hadoop, and its major components include:

• Spark Core: This is the underlying engine that provides job scheduling and optimization, as well as coordinating basic I/O operations and connecting the tool to the correct filesystems.
• Spark SQL: The SQL module allows users to optimize the processing of structured data by running SQL queries or using Spark's Dataset API directly within the solution.
• Spark Streaming and Structured Streaming: These enable stream processes, letting Spark take data from multiple sources and divide it into batches. Structured streaming builds on this with tools to lower latency and simplify the task of programming analytics.
• MLlib: This is Spark's built-in machine learning library, which provides users with a set of algorithms and other tools for feature selection and the creation of machine learning pipelines.

Comparing the two big data frameworks

But which one is best? You may think that as Spark is newer and faster, it should easily come out on top, but it's not quite this simple as there are a range of factors you need to take into account. Here's how they compare on some of the key considerations.

1. Architecture

The main difference between the two tools' architecture lies in how they organize and process data. In Hadoop, data is split into blocks that are replicated across various servers within a cluster, which are then run as either single or multiple jobs. Spark, however, doesn't have its own file system and data is accessed from external repositories - which may include HFDS - and then run in-memory. However, it can also run on disk if required - if it runs out of on-memory space, for instance.

2. Performance

Running analytics processes using Hadoop's MapReduce can be a slow process. By comparison, Spark's in-memory analytics can be up to 100 times faster than an equivalent workload on Hadoop when processing batch jobs. However, if you want to run large numbers of jobs simultaneously, this can create memory issues that degrade performance when using Spark, which may make Hadoop the better option for these activities.

3. Scalability

Hadoop is able to scale to very large data sets, as information can be stored and processed more cost-effectively on disk drives compared with Spark's in-memory solution. This may require more resources to manage the deployment of new nodes, although this can be mitigated somewhat if you're using cloud-based tools. Spark, meanwhile, offers tools that allow users to scale nodes up and down depending on requirements, though if scaling up, users will have to ensure workloads are separated across independent nodes to avoid memory leakage.

4. Cost

As open-source projects, both Hadoop and Spark offer low initial costs. However, you still have to factor in longer-term expenses such as maintenance, hardware and IT talent to determine total cost of ownership. In general, Hadoop requires more on-disk memory, whereas Spark needs more RAM. This means setting up clusters can be more costly for Spark, while personnel with the right skills and experience can be harder to come by, and therefore also more expensive. However, while Spark has a higher cost per hour, the fact it usually takes less time to complete analytics may offset some of these expenses.

Deciding the best option for your business

Overall, Hadoop is usually thought of as more suitable for disk-heavy operations using extremely large data sets, whereas Spark offers faster speeds and more flexibility, but at a higher cost.

However, it's not always a case of choosing one over the other. Deploying both Hadoop and Spark together is an increasingly common option for many users, allowing users to choose whichever is the most appropriate based on specific circumstances.

So for example, you may use Spark when you need fast results, while still relying on Hadoop for larger operations where cost is more of a concern, or if you need to analyze archive data that may not be possible with Spark's architecture.