Top Big Data Frameworks in 2021
Top Big Data Frameworks in 2021 Big Data is one of the most promising advancements in the field of information technology. The term ‘’ Big Data’’ itself explains that it refers to a large volume of data growing exponentially with time. Also, such a large amount of data combined with much complexity limits the conventional data management tools to process and store it accurately.
Top Big Data Frameworks in 2021 example of big data is the New York Stock Exchange, where at on average, one terabyte of data generates per day. Another example that you can relate to big data is the social media platform. Moreover A single application, just like Facebook, takes in 500+ terabytes of new data of various types into their databases. Big data categorize into two type’s structured and unstructured data.
Three special V’s often distinguishes big data, which are:
- Variety – ingestion of all types of data.
- Velocity – the speed at which the data is being collect and process.
- Volume – the collection of a large amount of data from various sources.
Many organizations fail to implement a successful big data practice in their organizations. It becomes hectic and quite frustrating to deal with a huge volume of data with multiple complexities. However, the evolution of big data frameworks made it much easier for organizations to utilize their provided structure to benefit from the potential of big data.
The organization interested in implementing big data practice uses this structure provided by the big data framework to closely look at all organizational capabilities. In addition, this framework even aims to improve the knowledge of individuals or organizations interested in big data practice.
Top Big Data Frameworks in 2021. A successful big data practice in an organization can only exist if equivalent attention provides to all parts of the big data framework. In this article, you will be provided with a list of the top big data frameworks of 2021. In addition, All the listed frameworks provide different functionalities that make them unique and give them an edge over others.
Apache Hadoop is one of the most popular big data frameworks that primarily works on the Map Reduce pattern. Scalable and distribute calculations are easily processed through Hadoop. Moreover, This framework can operate the fastest processes within a fraction of seconds.
Hence, a huge volume of information for many petabytes store and process with the help of this framework. Also, The performance gets a boost with an increase in data storage space. In addition, This framework aims to scale up from single servers to several machines that offer storage and local computation. Also, This big data framework comes up with a distributed environment consisting of some main
- HDFS (Hadoop Distributed File System): Data is store in Hadoop Cluster by the
- hardware layer.
- YARN (Yet Another Resource Negotiator): It handles resource management.
- MapReduce System: To process a huge amount of data in clusters.
MapReduce works as a search engine for the Hadoop framework. Also, This search engine takes inputs as
entries, processes them in three different stages. Moreover, Some of the key features of this search engine are
efficient balancing and automated paralleling of data.
Spark is another big data framework whose request is expanding day by day. Also, The demand for this big
data framework sees by its rapid growth in recent years. Also, Apache Spark is an in-memory data processing engine with outstanding development APIs. This quality enables data workers to execute machine learning, structured query language, and streaming jobs that require rapid iterative ingress to datasets efficiently. In addition, This big data framework is used for in-memory computing for ETL, data science workloads to Hadoop, and machine learning.
Facebook created the Apache Hive framework to combine the scalability of the most popular big data
frameworks. Apache Hive framework converts structured query language requests into chains of the MapReduce function. This big data framework can be considered as a data processing tool on Hadoop that works as a querying tool for HDFS with similar syntax as structured query language. Moreover, The open-source software attribute helps programmers to analyze enormous datasets on Hadoop.
Flink is an open-source single stream-processing engine based on Kappa architecture. This big data
framework is worth learning as it is the next-generation big data engine for stream processing. Moreover, This
robust framework works for batch processing as well as for stream. Moreover, Apache Flink holds the capacity to
graph, table, process along with the capability of running ML algorithms. Also, Flink has shown a nice growth
rate over the years. Many renowned companies like Alibaba, Uber, and many other organizations have
already shifted to apache Flink to process their big real-time data.
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This type of big data framework primarily works with the huge real-time data flow. Twitter introduced
such an impressive big data framework to the world that has also been adopted by many other
renowned names like Yahoo and Alibaba. Also, the Storm framework is highly scalable and purposely built to
handle low latency. Storm framework is a fault-tolerant big data framework and is platform-
This big framework is based on the master-slave concept that consists of two nodes:
- Master Node
- Worker Node
Apache Samza is an open-source big data framework tool for streaming handling data. One of the major
reasons for the evolution of this framework was to solve the issue of batch processing latency. In addition, Samza
allows for the development of applications with the tendency of processing real-time data from various
sources. The key attributes of the Samza big data framework are that it is horizontally scalable with rich
APIs like Samza SQL and Streams DSL. Also, This big data framework is more reliable because of better
isolation between tasks.
Although several big data frameworks are available in the market, only a few of them are highly
demanded and popular among most developers. Each big data framework varies from another
framework having different purposes and features.
We cannot say that a specific framework can be
used for all tasks and projects. This is because each project comes with different requirements, and
hence, we need the framework that best suits our project. We hope that this list of Big Data Frameworks
can help you find the best framework for your project.