With the impressive amount of information generated by modern society, big data becomes more than just an investigation field: it is a powerful force that changes business practices and marketing strategies. According to BCG, big data can help decentralized retailers to increase sales by 3% to 4%.
Are you curious about when to use Spark vs Hadoop? In our article, we will compare these two popular software frameworks so you can decide which one suits your project the best.
What should you know about Hadoop?
Launched in 2006 by Apache Software Foundation, Hadoop is a set of open-source software that enables data processing and storage across computer clusters. Primarily developed as an analytical tool, Hadoop proved to work particularly well for big data analysis. It can process both structured and unstructured data, has massive storage capabilities, and allows handling almost unlimited parallel tasks.
Hadoop consists of four main modules:
- Distributed File System – also known as HDFS, it enables storing data across a network of linked storage devices;
- MapReduce – reads, transforms and analyses data from the database;
- Hadoop Common – a set of tools and libraries which complement other modules and ensure compatibility with user’s computer systems;
- YARN – a clusters system manager.
The cluster storage system fastens data processing, as it can work at many devices simultaneously. This makes Hadoop vital for any project that has to deal with large datasets. Also, this framework has great flexibility and can be scaled to any company’s needs.
Use cases of Hadoop:
- Customer analytics – makes it possible to provide personalized services, offers, and ads based on insights from user’s data;
- Enterprise projects – to effectively manage and process data stored on various servers;
- Data lakes – Hadoop enables creating expansive storages of raw data from different streams of information that later can be structured and analyzed.
Read also: Top 5 Big Data Frameworks
What should you know about Spark?
Another open-source project from Apache, Spark doesn’t compete with the entire Hadoop ecosystem. It is a cluster-computing framework that has functionality similar to MapReduce and doesn’t even have its own distributed file system. In fact, the biggest difference between Spark and Hadoop is that the former works in-memory while the latter writes files to HDFS.
At the same time, Spark runs tasks up to 100 times faster. It was able to sort 100TB of data in just 23 minutes, which set a new world record in 2014.
In addition to the core engine, Spark has the following capabilities:
- Cluster management – compatible with diverse cluster management systems, including Hadoop YARN;
- Spark Streaming – a tool for data analysis in real-time;
- Spark SQL – integrates relational processing;
- GraphX – extends Spark functionality with graph-parallel computation;
- MLlib – a library devoted to machine learning.
Spark use cases:
- Stream processing – enables near real-time analysis of data from multiple sources, making it possible to act upon the information when it arrives;
- Machine learning applications – Spark is the right choice for training algorithms as it can rapidly run repeated queries;
- Data integration – helps to clean and standardize data from various sources.
Differences between Hadoop and Spark
So, which framework should you choose for your project, and why? To find out the answers, let’s compare Spark vs Hadoop in detail, their main features and functionality.
For years Hadoop MapReduce was setting a world record of processing speed, but now Spark is the indisputable leader. It is 100 times faster when it comes to in-memory processing and 10 times faster for disc-based operations. This became possible due to several reasons.
First of all, Spark isn’t restrained by input-output concerns at every single step, which grants better performance for applications. It also enables cyclical connections between processing steps, optimizing analysis.
At the same time, Spark may be less effective when running with other shared services and processing massive datasets. That can even result in RAM overhead memory leaks. Therefore, in the case of batch processing it is better to use Hadoop due to its stable performance during large data analysis.
Ease of Use
Hadoop is quite complex to work with, especially for beginners. It requires hand-coding of every operation, making it harder to use this framework for complex projects at scale. However, there are some add-ons like Pig that have a more convenient interface.
On the other hand, Spark itself has user-friendly APIs for most languages, making it easy to use. It also features an interactive mode that allows customers to get an immediate reaction to their queries.
As open-source projects, both Spark and Hadoop are free and in theory, will require zero expenses. However, you should also take into account that hiring a professional team, additional software purchases, and maintenance will also affect the final cost of your project.
So, what is the difference between Hadoop and Spark here? Since both these frameworks usually run in tandem, it is hard to exactly split the prices. In general, setting up Spark is more expensive as it requires more RAM and there are fewer professionals in this sphere. However, you can always opt for a cloud service such as Cloudera for Hadoop to reduce costs.
Security and fault tolerance
While both these frameworks support Kerberos authentication, Hadoop is considered more secure due to effective controls for its Distributed File System. Moreover, there is a special project called Apache Sentry dedicated to HDFS-level security.
Spark enables authentication via shared secret but has less solid security model overall.
As Hadoop replicates data across numerous nodes, each file is stored on many machines and can be easily rebuilt when one computer goes down, making this framework highly fault-tolerant.
Spark prevents data from corruption using Resilient Distributed Datasets (RDDs), which can reference external storage systems and be rebuilt if needed.
Comparison of Apache Spark vs Hadoop
To sum it up, we have prepared a table listing the advantages and disadvantages of these two projects.