Open source technology for big data analytics
Open-source big data analytics refers to the use of open-source software and tools for analyzing huge quantities of data in order to gather relevant and actionable information that an organization can use in order to further its business goals. The biggest player in open-source big data analytics is Apache’s Hadoop – it is the most widely used software library for processing enormous data sets across a cluster of computers using a distributed process for parallelism.
Techopedia Explains Open-Source Big Data Analytics
Open-source big data analytics makes use of open-source software and tools in order to execute big data analytics by either using an entire software platform or various open-source tools for different tasks in the process of data analytics. Apache Hadoop is the most well-known system for big data analytics, but other components are required before a real analytics system can be put together.
Hadoop is the open-source implementation of the MapReduce algorithm pioneered by Google and Yahoo, so it is the basis of most analytics systems today. Many big data analytics tools make use of open source, including robust database systems such as the open-source MongoDB, a sophisticated and scalable NoSQL database very suited for big data applications, as well as others.
Open-source big data analytics services encompass:
- Data collection system
- Control center for administering and monitoring clusters
- Machine learning and data mining library
- Application coordination service
- Compute engine
- Execution framework
With more and more companies storing more and more data and hoping to leverage it for actionable insights, big data is making a big splash these days. Open source technology is at the core of most big data initiatives, but projects are proliferating so quickly it can be hard to keep track of them all. Here are 15 key open source big data technologies to keep an eye on.
Originally developed by Matel Zaharia in the AMPLab at UC Berkeley, Apache Spark is an open source Hadoop processing engine that is an alternative to Hadoop MapReduce. Spark uses in-memory primitives that can improve performance by up to 100X over MapReduce for certain applications. It is well-suited to machine learning algorithms and interactive analytics. Spark consists of multiple components: Spark Core and Resilient Distributed Datasets (RDDs), Spark SQL, Spark Streaming, MLlib Machine Learning Library and GraphX. Spark is a top-level Apache project.
Written primarily in the Clojure programming language, Apache Storm is another distributed computation framework alternative to MapReduce geared to real-time processing of streaming data. It is well suited to real-time data integration and applications involving streaming analytics and event log monitoring. It was originally created by Nathan Marz and his team at BackType, before it was acquired by Twitter and released to open source. Storm applications are designed as a “topology” that acts as a data transformation pipeline. Storm is a top-level Apache project.
Apache Ranger is a framework for enabling, monitoring and managing comprehensive data security across the Hadoop platform. Based on technology from big data security specialist XA Secure, Apache Ranger was made an Apache Incubator project after Hadoop distribution vendor Hortonworks acquired that company. Ranger offers a centralized security framework to manage fine-grained access control over Hadoop and related components (like Apache Hive, HBase, etc.). It also can enable audit tracking and policy analytics
Apache Knox Gateway
Apache Knox Gateway is a REST API Gateway that provides a single secure access point for all REST interactions with Hadoop clusters. In that way, it helps in the control, integration, monitoring and automation of critical administrative and analytical needs of the enterprise. It also complements Kerberos secured Hadoop clusters. Knox is an Apache Incubator project.
Apache Kafka, originally developed by LinkedIn, is an open source fault-tolerant publish-subscribe message broker written in Scala. Kafka works in combination with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data. It’s ability to broker massive message streams for low-latency analysis — like messaging geospatial data from a fleet of long-haul trucks or sensor data from heating and cooling equipment — makes it useful for Internet of Things applications. Kafka is a top-level Apache project.
Born from a National Security Agency (NSA) project, Apache Nifi is a top-level Apache project for orchestrating data flows from disparate data sources. It aggregates data from sensors, machines, geo location devices, clickstream files and social feeds via a secure, lightweight agent. It also mediates secure point-to-point and bidirectional data flows and allows the parsing, filtering, joining, transforming, forking or cloning of data streams. Nifi is designed to integrate with Kafka as the building blocks of real-time predictive analytics applications leveraging the Internet of Things.
Apache Hadoop is an open source software framework for data-intensive distributed applications originally created by Doug Cutting to support his work on Nutch, an open source Web search engine. To meet Nutch’s multimachine processing requirements, Cutting implemented a MapReduce facility and a distributed file system that together became Hadoop. He named it after his son’s toy elephant. Through MapReduce, Hadoop distributes Big Data in pieces over a series of nodes running on commodity hardware. Hadoop is now among the most popular technologies for storing the structured, semi-structured and unstructured data that comprise Big Data. Hadoop is available under the Apache License 2.0.
R is an open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is rapidly becoming the go-to tool for statistical analysis of very large data sets. It has been commercialized by a company called Revolution Analytics, which is pursuing a services and support model inspired by Red Hat’s support for Linux. R is available under the GNU General Public License.
An open source software abstraction layer for Hadoop, Cascading allows users to create and execute data processing workflows on Hadoop clusters using any JVM-based language. It is intended to hide the underlying complexity of MapReduce jobs. Cascading was designed by Chris Wensel as an alternative API to MapReduce. It is often used for ad targeting, log file analysis, bioinformatics, machine learning, predictive analytics, Web content mining and ETL applications. Commercial support for Cascading is offered by Concurrent, a company founded by Wensel after he developed Cascading. Enterprises that use Cascading include Twitter and Etsy. Cascading is available under the Apache License.
Scribe is a server developed by Facebook and released in 2008. It is intended for aggregating log data streamed in real time from a large number of servers. Facebook designed it to meet its own scaling challenges, and it now uses Scribe to handle tens of billions of messages a day. It is available under the Apache License 2.0.
Developed by Shay Banon and based upon Apache Lucene, ElasticSearch is a distributed, RESTful open source search server. It’s a scalable solution that supports near real-time search and multitenancy without a special configuration. It has been adopted by a number of companies, including StumbleUpon and Mozilla. ElasticSearch is available under the Apache License 2.0.
Written in Java and modeled after Google’s BigTable, Apache HBase is an open source, non-relational columnar distributed database designed to run on top of Hadoop Distributed Filesystem (HDFS). It provides fault-tolerant storage and quick access to large quantities of sparse data. HBase is one of a multitude of NoSQL data stores that have become available in the past several years. In 2010, Facebook adopted HBase to serve its messaging platform. It is available under the Apache License 2.0.
Another NoSQL data store, Apache Cassandra is an open source distributed database management system developed by Facebook to power its Inbox Search feature. Facebook abandoned Cassandra in favor of HBase in 2010, but Cassandra is still used by a number of companies, including Netflix, which uses Cassandra as the back-end database for its streaming services. Cassandra is available under the Apache License 2.0.
Created by the founders of DoubleClick, MongoDB is another popular open source NoSQL data store. It stores structured data in JSON-like documents with dynamic schemas called BSON (for Binary JSON). MongoDB has been adopted by a number of large enterprises, including MTV Networks, craigslist, Disney Interactive Media Group, The New York Times and Etsy. It is available under the GNU Affero General Public License, with language drivers available under an Apache License. The company 10gen offers commercial MongoDB licenses.
Table of contents
Right from the moment you begin your day till the time you hit your bed, you are dealing with data in some form. This article will give you the top 10 open-source big data tools that do this job par excellence. These tools help in handling massive data sets and identifying patterns.
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Even if you are a beginner in this field, we are sure that this is not the first time you’ve read about Hadoop. It is recognized as one of the most popular big data tools to analyze large data sets, as the platform can send data to different servers. Another benefit of using Hadoop is that it can also run on a cloud infrastructure.
This open-source software framework is used when the data volume exceeds the available memory. This big data tool is also ideal for data exploration, filtration, sampling, and summarization. It consists of four parts:
- Hadoop Distributed File System: This file system, commonly known as HDFS, is a distributed file system compatible with very high-scale bandwidth.
- MapReduce: It refers to a programming model for processing big data.
- YARN: All Hadoop’s resources in its infrastructure are managed and scheduled using this platform.
- Libraries: They allow other modules to work efficiently with Hadoop.
2. Apache Spark
The next hype in the industry among big data tools is Apache Spark. See, the reason behind this is that this open-source big data tool fills the gaps of Hadoop when it comes to data processing. This big data tool is the most preferred tool for data analysis over other types of programs due to its ability to store large computations in memory. It can run complicated algorithms, which is a prerequisite for dealing with large data sets.
Proficient in handling batch and real-time data, Apache Spark is flexible to work with HDFS and OpenStack Swift or Apache Cassandra. Often used as an alternative to MapReduce, Spark can run tasks 100x faster than Hadoop’s MapReduce.
Apache Cassandra is one of the best big data tools to process structured data sets. Created in 2008 by Apache Software Foundation, it is recognized as the best open-source big data tool for scalability. This big data tool has a proven fault-tolerance on cloud infrastructure and commodity hardware, making it more critical for big data uses.
It also offers features that no other relational and NoSQL databases can provide. This includes simple operations, cloud availability points, performance, and continuous availability as a data source, to name a few. Apache Cassandra is used by giants like Twitter, Cisco, and Netflix.
To know more about Cassandra, check out Cassandra Tutorial to understand crucial techniques.
MongoDB is an ideal alternative to modern databases. A document-oriented database is an ideal choice for businesses that need fast and real-time data for instant decisions. One thing that sets it apart from other traditional databases is that it makes use of documents and collections instead of rows and columns.
Thanks to its power to store data in documents, it is very flexible and can be easily adapted by companies. It can store any data type, be it integer, strings, Booleans, arrays, or objects. MongoDB is easy to learn and provides support for multiple technologies and platforms.
High-Performance Computing Cluster, or HPCC, is the competitor of Hadoop in the big data market. It is one of the open-source big data tools under the Apache 2.0 license. Developed by LexisNexis Risk Solution, its public release was announced in 2011. It delivers on a single platform, a single architecture, and a single programming language for data processing. If you want to accomplish big data tasks with minimal code use, HPCC is your big data tool. It automatically optimizes code for parallel processing and provides enhanced performance. Its uniqueness lies in its lightweight core architecture, which ensures near real-time results without a large-scale development team.
6. Apache Storm
It is a free big data open-source computation system. It is one of the best big data tools that offers a distributed, real-time, fault-tolerant processing system. Having been benchmarked as processing one million 100-byte messages per second per node, it has big data technologies and tools that use parallel calculations that can run across a cluster of machines. Being open source, robust and flexible, it is preferred by medium and large-scale organizations. It guarantees data processing even if the messages are lost, or nodes of the cluster die.
7. Apache SAMOA
Scalable Advanced Massive Online Analysis (SAMOA) is an open-source platform used for mining big data streams with a special emphasis on machine learning enablement. It supports the Write Once Run Anywhere (WORA) architecture that allows seamless integration of multiple distributed stream processing engines into the framework. It allows the development of new machine-learning algorithms while avoiding the complexity of dealing with distributed stream processing engines like Apache Storm, Flink, and Samza.
With this big data analytical tool, you can access all available platforms from one place. It can be utilized for hybrid techniques and qualitative data analysis in academia, business, and user experience research. Each data source’s data can be exported with this tool. It provides a seamless approach to working with your data and enables the renaming of a Code in the Margin Area. It also assists you in managing projects with countless documents and coded data pieces.
9. Stats iQ
The statistical tool Stats iQ by Qualtrics is simple to use and was created by and for Big data analysts. Its cutting-edge interface automatically selects statistical tests. It is a large data tool that can quickly examine any data, and with Statwing, you can quickly make charts, discover relationships, and tidy up data.
It enables the creation of bar charts, heatmaps, scatterplots, and histograms that can be exported to PowerPoint or Excel. Analysts who are not acquainted with statistical analysis might use it to convert findings into plain English.
These were the top 10 big data tools you must get hands-on experience with if you want to get into the field of data science. Looking at the popularity of this domain, many professionals today prefer to upskill themselves and achieve greater success in their respective careers.
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