As companies come to terms with big data, they have also come to accept that conventional data processing systems simply can’t keep up with its demanding requirements. Big data and AI have come a long way, and it has benefited companies and industries that rely on data to ensure smooth business operations. Without the proper tools, however, data processing and management can be extremely time-consuming and computationally demanding. The way data is used by different organizations depends on a number of contextual factors, since it can be used in a myriad of ways depending on the nature of the business. It also doesn’t end with collecting data, organizations need to find a way to abstract it to a form that can be processed by available data processing frameworks.
The main challenge with big data is the high velocity at which data is generated and its variety; this means that data should be processed quickly while considering the fact that it could include structured, unstructured, and semi-structured data. This is where Apache Hadoop and Apache Spark come in. Through parallel processing and distributed processing, they can help simplify data processing tasks when volume gets too high. Although both parallel processing and distributed processing involve breaking up computing processes into smaller parts, there is an essential difference between them: distributed processing is disk-based and parallel processing is not. Distributed processing “distributes” the workload by using several computers, while parallel processing accesses the same memory space but uses more than one processor.
One of the top big data frameworks still in use today, Hadoop’s reputation as the first ever big data framework ever released precedes the platform itself. It enjoys wide adoption because it has its own ecosystem of tools, the most popular of which, include Hive, Flume, Pig, and HDFS. Considered by many as the simplest of all available frameworks, Hadoop processes data logically. It’s highly fault-tolerant and isn’t dependent on hardware for availability. Designed to detect faults at the application layer, Hadoop replicates data across a computer cluster so it can build the missing parts in case a piece of hardware fails.
Hadoop uses the MapReduce algorithm to split data into blocks that can be assigned to different nodes within a cluster. MapReduce then processes the blocks in parallel so the pieces can be combined into the desired result. Although Hadoop started with MapReduce, it also has the capability to go beyond the algorithm through the use of certain tools, most notable of which, is YARN. YARN is considered the Hadoop ecosystem resource management layer responsible for job scheduling and management of computing resources.
Spark was created to address the “need for speed” of many organizations. Since Hadoop is disk-based, it performs noticeably slower than Spark, which uses in-memory processing. As such, Hadoop is ideal for batch processing and Spark, for iterative processing. Spark is also designed to address the limitations imposed by the linear data flow of the MapReduce algorithm. This allows for a more flexible pipeline and provides an ideal framework for Spark’s machine learning library, tightly integrated machine learning, MLib, and distributed modeling. Spark can also perform a variety of other big data workloads, including graph computations, interactive queries, and real-time stream processing.
Although some consider Spark as a Hadoop competitor, the two frameworks can actually work to complement each other and replace MapReduce completely. Using Spark in conjunction with Hadoop can free you from the limitations of the aging MapReduce paradigm, allowing you to leverage new technologies to enable faster data processing and filling in the gaps commonly found when working with Spark or Hadoop alone.
Which Big Data Framework is for You?
Hadoop and Spark are two open-source frameworks with key differences. Spark is designed to be fast, uses in-memory processing, and has libraries that can accommodate big data analytics. It doesn’t have a distributed file storage system, however, which Hadoop has. This file system is highly scalable, and needs only the addition of servers and machines to accommodate growing volumes of data and increasing workloads. In the era of big data, the key difference between Hadoop and Spark is in their design. Hadoop is designed for efficient batch processing and is, therefore, a high-latency framework without an interactive mode. On the other hand, Spark is designed to handle real-time data and minimize read/write cycles to disk, increasing overall data processing speeds. Spark is also easier to use than Hadoop because it doesn’t require coding and comes with user-friendly API’s that can be used in Scala, Spark SQL, Java, and Python. Despite Spark’s high data processing speeds, Hadoop still has the upper hand if the resulting dataset is larger than the available RAM. In such cases, the MapReduce algorithm will still outperform Spark.
While choosing the right enterprise database systems can be a challenge and will largely depend on the needs of the business, it’s a choice that has to be made so that the business can take a strategic approach toward data processing and management. Spark may be a newer and more modern solution, but Hadoop remains a relevant and major player when it comes to big data analytics. Ultimately, the decision will boil down to what your business needs are—and what you’re prepared to accept as a computing solution’s shortcomings.