The data processing software used to manage data in earlier times has become traditional for this modern world. The amount of datasets that need to be managed now is very huge and is referred to as big data analytics. Traditional data processing software isn’t made for such large data. Big data helps to pick out trends and patterns in the economy to provide a useful future prediction. These huge amounts of datasets need robust computer software for processing. Therefore, the concept of a Data Processing Framework came into existence. Details about some of the most preferred frameworks used for processing data have been summed up below.
Hadoop
Hadoop is an open-source batch processing framework that provides distributed storage and can therefore be used for processing big data sets. The framework depends on computer clusters and modules designed to work at the time of hardware failure. The four main types of modules used by Hadoop are Hadoop Common, Hadoop Distributed File System, Hadoop Yet Another Resource Negotiator, and Hadoop MapReduce.
Hadoop splits files into large blocks of data to distribute them across the nodes present in a computer cluster. The code then gets transferred to the nodes, so that data processing can be done in parallel. Datasets are processed productively at a fast rate because of a concept known as data locality in Hadoop. It refers to how tasks are performed on the node which stores the data. Each node has different blocks of data to process, so the works get efficiently divided. An important benefit of Hadoop is that it can be used in a traditional onsite data center and through the cloud as well.
Apache Spark
Apache Spark is a hybrid framework because it can do batch as well as stream processing of big data. It’s very easy to use and applications can be easily written in it in Python, Java, Scala, and R. It’s an open-source cluster-computing framework, which is designed to be used for machine learning purposes. It requires a cluster manager and a distributed storage system. Multiple machines aren’t needed to run Spark as they can be run on a single machine. However, each CPU core of that single machine would require an executor.
Apache Spark can work on its own but it can also be used with other frameworks like Hadoop and Apache Mesos, which increases its potential to be used for versatile businesses. It relies on a data structure called a Resilient Distributed Dataset, which is a multi-dataset distributed over the entire machine cluster. RDD is a working set for distributed programs that provide a restricted distributed shared memory. Spark can access data sources such as HDFS, HBase, Cassandra, etc. for a distributed storage. Furthermore, Spark can also support a pseudo-distributed local mode for development and testing.
Apache Storm
Apache Storm is an open-source framework that is used for providing distributed real-time stream processing of data. It is written in Clojure but it can be used with any other programming language. As far as the topology of the application is concerned, it is designed in the shape of a Directed Acyclic Graph, with spouts and bolts as its vertices. The work of Storm is to define and compose small and discrete operations into a topology, which behaves like a pipeline for data transformation.
In Storm, streams are unbounded data that is continuously arriving at the system, sprouts are data streams’ sources present at the end of topology, and bolts are the processing part in which an operation is applied to the data streams. Streams present on the edges of the graph direct data from one node to the other. Sprouts and bolts define information sources and allow batch distributed real-time streaming.
Samza
Samza is an open-source real-time asynchronous framework that is used for distributed stream processing. Transformations create new streams that get consumed by other components without having any effect on the initial stream, these are known as immutable streams and the Samza framework is designed to handle them. Samza can work in conjunction with components of other frameworks like Apache Kafka and Hadoop YARN.
Samza uses the Kafka semantics for defining how it handles a streamline. Each stream of data sent to a Kafka system is called a Topic. The individual nodes combined for the creation of a Kafka cluster are called Brokers. Any component that writes to and reads from a Kafka Topic is called a Producer and a Consumer respectively. To divide incoming messages to distribute a Kafka Topic among various nodes Partitions are used.
Flink
Flink is another widely used open-source hybrid framework that can do batch as well as stream processing of big data. This is possible because of its pipelined runtime system that allows the execution of both processing programs, whether they are batch or stream. Furthermore, the pipelined runtime system also natively supports the execution of iterative algorithms. The applications of Flink are fault-tolerant and support exactly-once semantics as well. Programs can be written in a variety of languages namely Java, Scala, SQL, and Python. Flink also provides support for event-time processing and state management.
The parts of Flink’s stream processing model are streams, operators, sinks, and sources. Streams are immutable unbounded datasets that enter into the Flink system through sources and exit it through sinks and further go into either a database or another system. Operators are functions used on streams to create more streams. The batch processing system of Flink is an extension of its stream processing model. However, Flink doesn’t provide its storage system and it can be used only in conjunction with some other framework. The good part is that Flink is designed in a way that it can work with many types of frameworks.
No single data framework can fulfill the purpose of all and that’s why every data framework is essential. For example, Hadoop is best for massive scalability and Spark for machine learning and stream processing. It is important to understand the right framework for different parts of data processing per the business.