Improved performance and reliability of batch and real-time processing
Which technology provides the best performance for processing Big Data—Hadoop or Spark? A lot of people want to know. I did a Google search on “Hadoop versus Spark†and received over 30 million results!
The fact that this is a popular search phrase isn’t surprising, considering that the right Big Data framework can make a difference between business success and failure. Companies want the fastest engine for number crunching and to power machine learning and analytics for several different purposes. This can help them to optimize day-to-day operations, create new product ideas, make investment suggestions, and put captivating advertisements in front of consumers, to name a few.
However, it doesn’t make sense to pit one technology against the other. The optimal solution is achieved when Hadoop and Spark work side by side and tools are put in place to optimize their synergies for even better results.
Hadoop and Spark Compared
Hadoop and Spark have different architectures designed for different purposes. Hadoop is a framework that enables the storage of Big Data in a distributed environment so that batch processing can be executed on multiple datasets in parallel. It processes huge amounts of data on different data nodes and then gathers the results from each node manager. Spark data processing is based on stream processing – the fast delivery of real-time information which allows businesses to quickly react to changing business needs in real time. Because it processes everything in memory, it’s processing speeds are hundreds of times faster than Hadoop.
Due to the more economical use of hard disks, Hadoop is ideal for the number crunching of huge volumes of archived data for long running batch processing, where an immediate action is not necessary, such as payroll and billing systems and report generation. Spark’s in-memory processing speeds makes it more suitable for applications that require real-time responses, such as credit card processing, loan approvals and fraud detection.
Both Hadoop and Spark are open-source, so there’s no cost for the software. However, there is a cost for hardware, software and maintenance, including the personnel required to manage the system. Generally speaking, Hadoop requires more memory on disk while Spark keeps data in-memory, requiring more costly RAM, which makes Spark clusters typically more expensive. But since Spark technology requires less hardware for some installations, there can be a point where a Spark solution is more economical. For support costs, Spark can be higher, since Spark experts can be harder to find and therefore demand a higher labor rate.
Hadoop has built-in fault tolerance capabilities because it was designed to replicate data across many nodes. Each file is split into blocks and replicated numerous times across multiple machines, so that if a machine goes down, the file can be rebuilt from blocks residing elsewhere and the process can start again from where it left off. Spark processes data in memory, across operations. Although this architecture speeds up processing, equipment failure requires Spark to start the dropped processes from the beginning.
Hadoop and Spark Combined
Hadoop and Spark perform different but complementary functions which are critical in a world that runs on Big Data. Spark doesn’t come with a file management system of its own. It must rest on top of a read-write storage platform like the Hadoop ecosystem. From day one, Spark was designed to read and write data from HDFS (Hadoop Distributed File System) as well as other storage systems, such as HBase and Amazon’s S3.
There are different ways that Spark and Hadoop can work side by side based on business needs. Hadoop can be used independently for batch processing of archived historical data, and Spark can be utilized for fast data processing while pulling data from HDFS. This is a very common set up due to its simplicity.
This combination is especially useful in the healthcare and finance sectors, where HDFS’s access control lists and file level permissions provide Spark with a security bonus. Hadoop also enables Spark workloads to be deployed on the available resources in a distributed cluster, without having to manually allocate and track every task. Spark is not designed to deal with the data management and cluster administration tasks associated with running data processing and analysis workloads at scale.
Integrating the Two Systems
Another possibility is to integrate the two systems more closely, where Spark can be used instead of MapReduce (Hadoop’s data processing engine) to provide faster read/writes from HDFS. This is a preferable confirmation for machine learning and AI applications that require extreme processing speeds. However, even when Hadoop and Spark combine forces, there can still be inefficiencies that can be improved to bring performance to the next level.
Spark running on Hadoop has certain hiccups which slow down processing time. Data isn’t available for analytics in HDFS for a long time after creation, due to long ETL processes. And loading data to Spark memory from Hadoop archive data store slows down analytics, even when it’s done in a lazy manner (on-demand loading to reduce unnecessary processing time before the data is actually needed).
A unified transactional and analytical processing platform can remove the bottlenecks to accelerate processing time by reducing time-consuming ETL processes and eliminating unnecessary data duplication.
Choosing the right analytics platform provider comes down to evaluating which is the best way to store, manage and analyze massive amounts of data efficiently and cost effectively. Depending on the application requirements, Spark, Hadoop, or a combination can be the optimal solution. Either way, adding another speed layer can fill the gaps and ensure that companies receive the best of both worlds—faster and smarter analytics and cost-effective batch processing to leverage Big Data efficiently for better business results.