Apache Spark and Hadoop’s role in dealing with Big Data is prominent. Each of them has its significance. The main topic of our discussion is Apache Spark how it works in coordination with Hadoop.
Apache Spark is the framework effective to perform data analytics similar to Hadoop. Also, it enhances the speed with in-memory computation while MapReduce processes the data.
Concerning its functionality, it works on the top of the Hadoop cluster and approaches the Hadoop data store. Moreover, it exercises streaming data from Kafka, Flume, HDFS, and structured data in Hive.
Is Apache Spark worthy of replacing Hadoop?
Hadoop is a framework designed to reduce jobs. Usually, the long-running jobs take a great amount of time, ranging from minutes to hours. Apache Spark has been designed to work on top of Hadoop and works as a substitute for the batch map built traditionally. The streamlined data processing can be executed in real-time, and queries can turn fast and interactive.
Hadoop supports multiple models acting as a general framework. Spark can only be an alternative to Hadoop MapReduce but not a complete replacement for Hadoop.
So, which one to choose in this instance, is it Spark or Hadoop MapReduce?
Spark occupies more RAM and is quick compared to Hadoop. So, a high-end physical machine is essential for producing expected results.
How are Apache Spark and Hadoop MapReduce different from each other?
- Hadoop stores the data on disk, while Spark stores in-memory
- Fault tolerance: Hadoop uses replication, while Spark uses a data storage model to minimize network I/O and guarantee fault tolerance
What to learn initially: Hadoop or Apache Spark?
Spark is an independent entity and does not require learning Hadoop. Spark has gained popularity after the introduction of Hadoop 2.0 and YARN, for it can run on top of HDFS and other components of Hadoop.
Spark has turned as yet another data processing engine in the Hadoop environment, for any business can gain more ability to Hadoop stack.
Hadoop does the MapReduce job through Java class inheritance. At the same time, Spark executes parallel computation through function calls.
Apache Spark’s features :
Spark allows Hadoop cluster applications to execute quickly. Significantly, it reduces the number of reading/write instances on disk, and in-memory acts as vital to store intermediate processing data.
Resilient Distributed Dataset (RDD) is a concept to gain attention, wherein it enables data storage on memory and keeps going on the disc when needed.
Spark helps you write Java, Scala, and Python applications very quickly. Thus, developers find it easy to build and execute the applications in their favorite programming languages. At the same time, they can also develop apps that can function on two different accounts.
Supports complex analytics and runs everywhere
Spark aids streaming data, SQL queries, and complex analytics, such as machine learning to prove unusually good.
Spark works as a standalone application and on the cloud as well. It is designed to run on Mesos and Hadoop. Most importantly, some of the diverse sources that can be accessed include S3, Cassandra, HBase, and HDFS.
Further, let us know how Spark outstands
- Uses machine learning to develop iterative algorithms
- Data processing and Data mining turns more interactive
- Spark executes faster than Hive
- In-memory data is of great help due to its easy and fast processing
- Greater access to Big Data
- Supports multilingual feature
- Easy to use
- Exhibits dynamic nature
- Supports advanced analytics
- Supports enhanced speed
Apache Spark is not designed to replace Hadoop. It, however, has its advantages to act as a data processing framework for computing data available on Hadoop disk.
Spark’s processing speed is high, enabling it to perform better than Hadoop MapReduce. However, it requires more memory. Another significant difference is that Hadoop MapReduce is difficult to program while Apache Spark is more flexible and easy. One better the other in various respects. So, choosing Hadoop or Apache Spark is based on your requirements.