4 Ways Hadoop & Spark Can Play Nicely Together
As organizations buy into big data, a huge part of the process is selecting the tools to store, maintain, process, and analyze the data. Hadoop and Spark are often billed as an either-or scenario. Either you use one, or you use the other. However, there are reasons why you should consider using both. In many situations, they complement each other beautifully. Here is what each does and how they are different, but can often be used together as complementary tools.
1. Hadoop includes a Distributed Storage Framework, Spark Provides In-Memory Data Processing
Hadoop can play nicely in a pack, but to be a complete big data solution, it needs to include its native data processing component, MapReduce, or be paired with another data processing product like Spark.
Hadoop includes HDFS, a distributed file framework, which allows you to distribute enormous collections of data across nodes within a cluster of servers. This eliminates the need for lots of custom hardware. Hadoop also indexes and tracks the data, which allows for processing and analyzing those massive data collections more efficiently and effectively. Spark does not distribute storage, it only processes the data. Hence, both Hadoop and Spark can work effectively as a big data system that combines a required distributed file system with Spark’s multi-stage, in-memory data processing.
2. Though Hadoop & Spark Work Well Together, It Isn’t Necessary to Have Both
In addition to Hadoop’s storage component (which, by the way, is called the Hadoop Distributed File System or HDFS), it offers MapReduce for processing purposes. This would eliminate the necessity for Spark, and is used this way in many big data infrastructures. Similarly, Spark can be used with a file management system other than Hadoop. But since Spark was designed to be used with Hadoop, the two are great companions. Plus, MapReduce is known for being difficult to program in. Spark is simpler and faster.
3. Spark is Faster Than MapReduce
Spark isn’t essential for Hadoop, but if you need to work in real time, it is between ten and 100 times faster than MapReduce.
When considering a big data infrastructure, if speed is a consideration (such as when data streaming is required), Spark is faster than MapReduce. Spark can deliver near real-time analysis and Spark looks at all of the data, whereas MapReduce reads data from one cluster, performs an operation, then writes the results in a systematic method that slows the operations down considerably. Depending on the setup, Spark often performs 100 times faster than MapReduce.
4. Both Hadoop & Spark are Resilient to System Failures
Hadoop writes data to disk following each operation, making it resilient when a fault or failure occurs in the system. Spark also has a resilient design, it just works differently. Spark stores data objects in resilient distributed datasets or RDD, which are distributed across the clusters. The data might be stored in memory, or stored on the disks. RDD assures full recovery following a fault or failure. Hence, if you are using Hadoop and Spark separately, there is still a built-in resilience. Together, however, this duo makes for a sound infrastructure for big data processing and analytics.
Editor’s Note: Whether you’re working with Hadoop and/or Spark, your first job is getting your data from your existing data infrastructure into Hadoop in a usable format. This can be trickier than it sounds – especially if your data sources include mainframes. You can explore Syncsort’s Big Data solutions to see how their expertise in Hadoop, Spark, mainframes and data warehouse optimization can help.