Open Source Technology for Big Data: Tools - 【2020】
big data open source

Open Source Technology for Big Data: Tools

Big data analysis is an essential part of any business process today. To get the most out of it, we recommend using these popular open source data solutions, for every phase of data processing.

Why choose large open source data tools instead of proprietary solutions? The reason has become obvious in the last decade: open source software makes it popular.

Developers prefer to avoid vendor lock-in and often use free tools to accommodate versatility and have the opportunity to contribute to the development of their popular platform. Open source products offer the same, if not better, level of documentation, along with much more dedicated support from the community, who are also product developers and great data professionals who know what they need from a product. So this is a list of 8 Big Data hot tools to be used by 2020, based on popularity, feature set and benefits.

Contents

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Apache Hadoop

The long-time champion in the field of large data processing, known for its ability to process enormous data. This Big Data open source framework can run on-premise or in the cloud and has relatively low hardware requirements. The main benefits and features of the Hadoop are as follows:

  • HDFS – Hadoop Distributed File System, designed to work with massive bandwidth.
  • Reduce the map – a highly configurable model for processing Big Data
  • GARN – a resource planner for Hadoop resource management
  • Hadoop libraries – the glue needed to make third-party modules work with Hadoop

Apache Spark

Apache Spark is the alternative and in many ways the successor to Apache Hadoop. Spark was built to address Hadoop’s shortcomings, and it does so incredibly well. For example, it can handle both batch and real-time data, and is 100 times faster than MapReduce.

Spark provides in-memory data processing capabilities that are much faster than the disk processing used by MapReduce. In addition, Spark works with HDFS, OpenStack and Apache Cassandra both in the cloud and on-premise, adding another layer of complexity to Big Data operations for your business.

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Apache Storm

Storm is another Apache product, a real-time framework for data flow processing that supports all programming languages. The Storm Scheduler balances the workload across multiple nodes based on the topology configuration and works well with Hadoop HDFS. Apache Storm provides the following benefits:

  • High horizontal scalability
  • Built-in fault tolerance
  • Automatic restart in case of accident
  • Written Closure
  • Works with DAG (Direct Acyclic Chart) topology
  • The output files are in JSON format

Cassandra Apache

Apache Cassandra is one of the pillars of Facebook’s massive success because it can handle structured data sets distributed across a large number of nodes worldwide. Because of its architecture with no single points of failure, it works well under heavy workloads and offers unique features that no other NoSQL or relational database has, such as.

  • High scalability of the coating
  • Simplicity of operations through the use of a simple query language
  • The constant replication through the nodes
  • Easily add and remove nodes from a running group
  • High fault tolerance
  • Built-in high availability
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MongoDB

MongoDB is another excellent example of a feature-rich, open-source NoSQL database that is compatible with many programming languages. IT Svit uses MongoDB for a variety of cloud computing and monitoring solutions. A module for automated backup of MongoDB with Terraform has been specifically developed. The most popular features of MongoDB are

Stores any type of data, from text and integers to strings, arrays, dates and booleans.
Cloud deployment and high configuration flexibility
The division of data across multiple nodes and data centers
Significant cost savings as dynamic schemes allow for processing of data in motion

R++ Programming Environment

R is mainly used in conjunction with Julia, Python, R, to allow full statistical analysis and data visualization. JupyteR Notebook is one of the 4 most popular visualization tools for large data, as it assembles literally any analysis model from over 9,000 algorithms and modules from the Comprehensive R Archive Network (CRAN), runs them in a convenient environment, customizes and inspects analysis results on the fly. The main advantages of using the R are as follows:

  • R can run on the SQL server
  • R works on Windows and Linux servers
  • R supports Apache Hadoop and Spark
  • R is very portable
  • R scales easily from a simple test machine to large Hadoop data lakes
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Neo4j

Neo4j is an open source graphical database with interconnected node-data relationships that follows the pattern of key values when storing data. IT Svit recently used Neo4j to build a robust AWS infrastructure for one of our customers, and the database works well under a heavy network data and graphical query workload. The key features of Neo4j are as follows:

  • Built-in support for ACID transactions
  • Other language for consulting graphics
  • High availability and scalability
  • Flexibility due to the absence of schemes
  • Integration with other databases

Apache SAMOA

This is another tool of the Apache family for processing Big Data. Samoa specializes in the development of distributed streaming algorithms for successful Big Data Mining. This tool has a plug-in architecture and should be used in other Apache products such as Apache Storm. Other features used for machine learning include

  • Grouping
  • Classification
  • Standardization
  • Regression
  • Programming elements to create user-defined algorithms

Using Apache Samoa, distributed stream processing modules can provide these tangible benefits:

  • Program once and use everywhere
  • Use existing infrastructure for new projects
  • No restart or deployment downtime
  • Time-consuming backups or updates are not necessary
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Final thoughts on the list of great data tools for 2020

The large data industry and data science are developing rapidly and have made great progress recently. Several Big Data projects and tools were launched in 2017. This is one of the hottest trends in IT in 2020, along with Io, block chain, AI and ML.

Big data analysis is increasingly common in a variety of industries, from the use of ML in banking and financial services to healthcare and government. Open source tools for Big Data form the mainframe of any Big Data architect’s tool.

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