Top 6 Big Data Frameworks

Have you ever thought about how to select the best Big Data engine for business and application development? The market for Big data software is humongous, competitive, and also brimming with software that apparently accomplishes very similar things. Big Data is presently one of the most requested specialties in the development and supplement of enterprise software. The high ubiquity of Big Data technologies is a phenomenon provoked by the quick and also constant growth of data volumes. To provide the necessary bandwidth massive data arrays must be assessed, structured, and also processed. Data processing engines are getting a great deal of utilization in tech stacks for mobile applications, and also some more. So what Big Data framework will be the best pick in 2020? Let us see.

Big Data Frameworks-

There are many frameworks available in the market. Some of them are more popular and those are Spark, Hadoop, Hive and Storm. Whereas Presto score high on utility index and Flink has great potential. Also there are some others which need some mention like the Samza, Impala, Apache Pig, etc. Here we will discuss some of them.

1. ApacheHadoop-

Hadoop is a Java- based platform. This is an open-source framework which provides batch data processing and data storage services across a group of hardware machines arranged in clusters. Hadoop is great for reliable, scalable and distributed calculations also. However, it can also be exploited as common purpose file storage. It can store and process petabytes of information. Hadoop consists of three main components.

  1. HDFS file system- It is responsible for the data storage in the Hadoop cluster;
  2. MapReduce system- It is intended to process large volumes of data in a cluster;
  3. YARN- It is a core that handles resource management.

Pros-

It gives cost-effective solution, high throughput, multi-language support, compatibility with most rising technologies in Big Data services. Also supports high scalability, fault tolerance, better suited for R&D, high availability through amazing failure handling mechanism.

Cons-

It includes vulnerability to security breaks, doesn’t perform in-memory calculation hence suffers handling overheads, not appropriate for stream processing and real-time processing, issues in processing small files in huge numbers.

Organisations like Amazon, Adobe, AOL, Alibaba, EBay, and Facebook also uses Hadoop.

2. Apache Spark- 

The Spark framework was formed at the University of California, Berkeley. It is a batch processing framework with improved data streaming processing. With full in-memory computation and also handling optimization, it guarantees an extremely quick cluster computing system.

Spark framework is composed of five layers.

  • HDFS and HBASE: They form the first layer of data storage systems. 
  • YARN and Mesos: They form the resource management layer. 
  • Core engine: This forms the third layer.
  • Library: This structures the fourth layer containing Spark SQL for SQL queries while stream processing, GraphX and Spark R utilities for handling graph data and MLlib for machine learning algorithms.
  • The fifth layer contains an application program interface, for example, Java or Scala.

Spark can work as an independent cluster alongside a capable storage layer or it can give consistent integration with Hadoop. It supports some popular languages like Python, R, Java and Scala also.

Pros-

  1. Speed
  2. Ease of Use
  3. Advanced Analytics
  4. Dynamic in Nature
  5. Multilingual
  6. Apache Spark is powerful
  7. Increased access to Big data
  8. Demand for Spark Developers
  9. Open-source community

Cons-

Spark poses some cons like complexity of setup and implementation, language support limitation, not a genuine streaming engine.

3. Storm-

Apache Storm is another noticeable solution, focused on working with a huge real-time data flow. The key highlights of Storm are scalability and prompt restoring ability after downtime. You can work with this solution with the assistance of Java, Python, Ruby, and Fancy. Storm includes a few components that make it fundamentally not the same as analogs. The first is Tuple — a key data representation element that supports serialization. Then there is Stream that incorporates the scheme of naming fields in the Tuple. Spout gets data from external sources, forms the Tuple out of them, and sends them to the Stream. There is additionally Bolt, a data processor, and Topology, a package of elements with the description of their interrelation. When combined, all these elements help engineers to oversee huge flows of unstructured data.

Talking about performance, Storm gives better latency over both Flink and Spark. Notwithstanding, it has more terrible throughput. Recently Twitter moved to another framework Heron. Storm is as yet utilized by big organizations like Yelp, Yahoo!, Alibaba, and some others. It’s as yet going to have a huge client base and support in 2020.

4. Apache Flink-

Apache Flink is an open source framework, good for both batch and stream data processing also. It is best suited for cluster environments. This framework is based on transformations – streams concept. It is additionally the 4G of Big Data. It is the100 times faster than Hadoop – Map Reduce.

Flink framework consists of multiple layers-

  • Deploy Layer
  • Runtime Layer
  • Library Layer

Pros-

Low latency, high throughput, fault tolerance, entry by entry processing, ease of batch and also stream data processing, compatibility with Hadoop.

Cons-

Few scalability issues.

5. Presto-

It is the open- source distributed SQL tool most appropriate for smaller datasets. Presto engine incorporates a coordinator and also various workers. When client submits queries, these are parsed, analysed, their execution planned and distributed for handling among the workers by the coordinator.

Pros-

  1. least query degradation even in the event of increased concurrent query workload.
  2. It has a query execution rate that is three times faster than Hive.
  3. Ease in adding images and embedding links. 
  4. Highly user-friendly.  

Cons-

  1. Reliability issues

6. Samza-

Apache Samza is a stateful stream preparing Big Data system that was co-developed with Kafka. Kafka gives data serving, buffering, and fault tolerance. Both are combinedly  proposed to be utilized where rapid single-stage processing is required. With Kafka, it can be utilized with low latencies. Samza also saves local states during processing that give additional fault tolerance. It was designed for Kappa architecture but can be used in other architectures. Samza uses YARN to arrange resources. So it needs a Hadoop cluster to work, so that implies you can depend on highlights provided by YARN. This Big Data processing framework was developed for Linkedin and is also utilized by eBay and TripAdvisor for fraud discovery. A sizeable part of its code was utilized by Kafka to create a competing data processing framework Kafka streams.

Conclusion-

There is no single framework that is best fit for all business needs. But, to feature some frameworks, Storm appears to be most appropriate for streaming while Spark is the winner for batch processing. For each organization or business, one’s very own data is most significant. Putting resources into Big Data structures includes spending. Numerous frameworks are freely accessible while some accompanied a cost. Contingent upon the project needs, benefit of preliminary versions offered. For appropriate choice, understand the objectives of the business. You can experiment with the framework on a smaller scale project to understand functioning more precisely. Investing in the right framework leads to the success of a business.

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