Apache Spark is an open-source, lightning fast, cluster computing framework that provides a fast and powerful engine for large-scale data (Big Data) processing. It runs programs up to 100x faster in-memory and 10x faster on disk when compared to Hadoop’s MapReduce system. The reason for Spark’s success is its ability to process data in-memory (using RAM) that allows faster retrieval of data as compared to querying and searching on disk. It is to be noted that Spark is still the fastest technology for disk processing and holds a world-record for large scale on-disk sorting. It has the ability to break down complex queries into multiple computations for parallel processing, which makes it the perfect choice for Big Data analytics and Machine Learning applications. Large organizations love Spark due to its simplicity, flexibility and high-performance data processing power.
Spark’s faster adoption in large number of Fortune 500 companies across various industries shows how remarkable it is. According to a survey done in 2016, more than 1,000 companies used Apache Spark in production, out of which some well-known players like Amazon, Uber, Netflix, eBay, Yahoo and many more are at the top of the list. They have deployed Spark at a large scale, processing petabytes of data. Spark’s largest known cluster so far has been over 8,000 nodes.
Though Apache Spark has been completely written in Scala, it provides high-level APIs for languages like Scala, Java, Python and R. It gives almost 100 high-level operators that makes it easy to build parallel apps. Spark runs on Hadoop YARN, Apache Mesos, Cloud and standalone cluster mode and can access diverse data sources including HDFS, Cassandra, HBase, and S3.
RDD stands for Resilient Distributed Dataset and is the primitive type in Apache Spark that holds immutable collection of objects, which can be processed in parallel across multiple nodes of the cluster. Basically, RDDs are read-only and can be created through rough operations like map, filter, group-by, etc. on data from stable or external storage.
Existing computing systems that use MapReduce for processing data need some kind of a storage system (ex: HDFS) and the process of replication, serialization and disk IO in such a system makes it time consuming. RDDs on the other hand enable fault tolerant distributed in-memory computations. In case some part of RDD is lost, it can easily be recovered through transformation on the partition rather than replicating data across multiple nodes, thus RDD reduces loads of data management and replication efforts.
To know more about RDD, breaking it down into these three words help us understand it better:
Apache Spark has an ecosystem that supports multiple programming languages, components/libraries, cluster and storage management. The diagram below represents this ecosystem. In this ecosystem, Spark Core is the main engine and the most important component. It contains components/libraries that help in task scheduling, memory management, fault recovery, interacting with storage systems, etc.
Apache Spark provides a wide range of benefits over other Big Data technologies like Hadoop and MapReduce. It provides advanced Big Data analytics with the support of its libraries.
Apache Spark and Hadoop are the 2 most buzzed about technologies for Big Data Analytics and Machine Learning. There are quite a lot of differences between the two, but when they are combined together, they perform exponentially better.
Hadoop which is widely known as a distributed data infrastructure, stores data across multiple nodes within a cluster of inexpensive servers. Other than storing, it also indexes and keeps track of stored data that helps in big data analytics. Spark, on the other hand, is a data processing tool that operates with distributed datasets over various storage mechanism.
Hadoop’s MapReduce feature, which is responsible for data processing persists data in its distributed file system called HDFS. This process of handling and manipulating data over a physical storage mechanism is time consuming. In contrast, Spark has the concept called RDD that enables processing datasets in-memory that makes it extremely fast when compared to MapReduce.
Both of these technologies are fault-tolerant, but their mechanism of achieving this is different. In the case of Hadoop, when data is lost due to node failure, it can quickly recover it since it is replicated across multiple nodes. Spark, though similar, takes a different approach. The data objects, which are stored in-memory when lost can be recovered through RDD. The difference is how they handle storage; one recovers data from a disk and the other one from memory.
These technologies are not interdependent. Hadoop along with HDFS has its processing component called MapReduce, which is responsible for data processing.
This means that it does not require Spark to process data. Apache Spark on the other hand can process data in memory, but needs a storage mechanism. Other than HDFS, Spark can persist data on Cloud, RDBMS or NoSQL databases and its standalone cluster. Though this is the difference, when Hadoop and Spark are combined together, they provide benefits like extremely fast data processing and a cheap storage mechanism.
Apache Spark is known as a distributed data processing engine whereas Apache Kafka is a message broker that receives real-time streams of data. Kafka's messaging broker contains a producer and consumer, but they do not have any idea about each other. Kafka acts as a mediator between the two and passes data in a specific format. Apache Spark's Streaming API, which waits for live data stream can receive this data from Kafka. The streaming API can then process that data and either return it back to Kafka or persist it in its storage system. This is how Spark and Kafka can be combined to work together.
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