If Big Data Analytics Is Your Mainstay, Why Should You Care About Spark?
Introducing Apache Spark
The framework of Apache Spark essentially performs analytics of general data on distributed cluster for computing, Hadoop for example. It enables computations in memory of increased rate of data processing in comparison with map reduce. Apache Spark executes over and above hadoop cluster and the data store of hadoop can be accessed and is able to process data structures in Streaming data and Hive, the source being Twitter, Kafka, Flume, HDFS and so on.
The architecture of Spark
The question that is in every techies mind is whether Apache Spark can be considered Hadoop’s true replacement. As a framework for analogous data processing, Hadoop has a long tradition of being used to execute map or bring about a reduction in jobs. Such jobs run for a protracted period of time until they are complete. The design of Spark is such that it can execute above Hadoop. Moreover, it’s an option for the erstwhile batch map or the diminish model which is used for streaming data in real-time and quick interactive questions and answers that only takes seconds to execute. Hadoop therefore supports both erstwhile map or reduce along with Spark. Hadoop’s framework is general purpose supporting several models while Spark essentially is an option of Hadoop MapReduce instead of Hadoop’s replacement.
The Echo System of Hadoop
Hadoop MapReduce or Spark – decisions, decisions, decisions
Spark requires more RAM rather than disk input or output and network and it’s comparatively faster than Hadoop. Since a lot of RAM is required in Spark, a state-of-the-art dedicated physical device that would produce results that would have an effect is necessary. There are too many variables to consider as they continue to alter dynamically over time.
Hadoop Mapreduce vs Apache Spark
- Data is stored in Spark in-memory while data is saved on disk in Hadoop.
- Replication is used in Hadoop to enhance fault tolerance levels. Spark on the other hand uses various data storage models, resilient distributed datasets or RDD, and subtly guarantee tolerance of fault that would minimize network input or output. Fault tolerance in RDDs is achieved through the idea of lineage in the sense that losing a RDD partition is not a hassle as there is sufficient data to reconstruct only the partition that has been lost. Therefore, data need not be replicated for the purpose of tolerance of fault.
Is Hadoop a prerequisite for learning Apache Spark?
The straight answer is that learning Hadoop is not a prerequisite for training in Spark. Spark used to be a standalone project. However, subsequent to Hadoop 2.0 and YARN, Spark became well-known as it ran above HDFS with other components of Hadoop. Spark is an entirely new platform to process data in Hadoop ecosystem. This, for businesses and communities alike is good as there is far more leverage to the Hadoop stack.
From a developer’s perspective, Hadoop and Spark do not overlap. Hadoop is really a framework where MapReduce jobs are written with java classes that are inherited. Spark is essentially a library enabling parallel processing through function calls. The general skills of operators such as deploying code configuring and monitoring should overlap to be able to run a cluster.
The features of Apache Spark
Some of the notable features of Spark in the world of Big data are:
Applications in Hadoop clusters that are Spark enabled would run in memory at the most 100x faster and even on disk would run 10x faster. With Spark successive read or write to disc can be reduced. This intermediate data is stored in memory. Resilient Distributed Dataset or RDD concept is implemented which makes data storage to memory or disc transparent. Thus, write and read on disc is reduced which takes up a lot of time while processing data.
ii) Can be used easily
With Spark, applications in Java, Scala, or Python can be written easily. Developers in particular are immensely benefited as they can create as well as run applications on the programming languages that they are familiar with and build parallel applications easily. Equipped with an in-built set of more than eighty top-level operators Apache Spark can be used in an interactive manner for data query purposes inside the shell as well.
iii) Complex analytics, streaming and SQL are all combined
Along with simple operations like map and reduce, Spark also supports streaming data, SQL queries, along with complex analytics like groundbreaking graph algorithms and machine learning. Furthermore, seamless combinations of capabilities integrated in a workflow can be achieved.
iv) Runs anywhere
Spark is compatible on standalone, Mesos, Hadoop as well as on cloud. Varied sources of data can be accessed including S3, HBase , Cassandra and HDFS. User experiences of Spark over Hadoop are:
- Machine Learning involving Iterative Algorithms
- Data Processing and Data Mining where there is interaction
- Spark is a wholly compatible Apache Hive data warehouse that runs a lot faster compared with Hive.
- Streaming: Fraud detection and log processing in real-time streams for analysis, aggregates and alerts
- Sensor processing: Fetch data from more than one source including dataset that is in-memory which is helpful as data processing is fast and easy.