{"id":1948,"date":"2019-09-13T02:29:18","date_gmt":"2019-09-13T02:29:18","guid":{"rendered":"http:\/\/kusuaks7\/?p=1553"},"modified":"2024-04-15T15:16:02","modified_gmt":"2024-04-15T15:16:02","slug":"the-graphtech-ecosystem-2019-part-2-graph-analytics","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-graphtech-ecosystem-2019-part-2-graph-analytics\/","title":{"rendered":"The GraphTech Ecosystem 2019 – Part 2: Graph Analytics"},"content":{"rendered":"\t\t
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This post is part of a series of articles about the GraphTech ecosystem. This is the second part. It covers the\u00a0graph analytics landscape<\/strong>. The first part was about graph databases<\/a> and the third part will list the existing graph visualization tools.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Actors of the graph analytics landscape in 2019<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This second layer is another back-end layer: graph analytics, or computing, frameworks. They consist of a set of tools and methods developed to extract knowledge from data modeled as a graph. They are crucial for many applications because processing large datasets of complex connected data is computationally challenging.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A need for analytics at scale<\/h1>\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The field of graph theory has spawned multiple algorithms on which analysts can rely on to find insights hidden in graph data. From Google\u2019s famous\u00a0PageRank algorithm<\/a>\u00a0to traversal and path-finding algorithms or community detection algorithms, there are plenty of calculations available to get insights from graphs.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The graph database storage systems we mentioned in\u00a0the previous article<\/a>\u00a0are good at storing data as graphs, or at managing operations such as data retrieval, writing real-time queries or at local analysis. But they might fall short on graph analytics processing at scale. That\u2019s where graph analytics frameworks step in. Shipping with common graph algorithms, processing engines and, sometimes, query languages, they handle online analytical processing and persist the results back into databases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Graph processing engines<\/h1>\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The graph processing ecosystem offers various approaches to answer the challenges of graph analytics, and historical players occupy a large part of the market.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Main graph engine processing and framework vendors<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In 2010, Google led the way with the\u00a0release of Pregel<\/a>, a \u201clarge-scale graph processing\u201d framework. Several solutions followed, such as\u00a0Apache Giraph<\/a>, an open source graph processing system developed in 2012 by the Apache foundation. It leverages\u00a0MapReduce<\/a>\u00a0implementation to process graphs and is the system used by Facebook to traverse its social graph. Other open source systems iterated on Google\u2019s, for example,\u00a0Mizan<\/a>\u00a0or\u00a0GPS<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Other systems, like\u00a0GraphChi<\/a>\u00a0or\u00a0PowerGraph Create<\/a>, were launched following GraphLab\u2019s release in 2009. This system started as an open-source project at Carnegie Mellon University and is now known as\u00a0Turi<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Oracle Lab developed\u00a0PGX<\/a>\u00a0(Parallel Graph AnalytiX), a graph analysis framework including an analytics processing engine powering Oracle Big Data Spatial and Graph.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The distributed open source graph engine Trinity, presented in 2013 by Microsoft, is now known as\u00a0Microsoft Graph Engine<\/a>.\u00a0GraphX<\/a>, introduced in 2014, is the embedded graph processing framework built on top of\u00a0Apache Spark<\/a>\u00a0for parallel computed. Some other systems have since been introduced, for example,\u00a0Signal\/Collect<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Graph analytics libraries and toolkits<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In the graph analytics landscape, there are also single-user systems dedicated to graph analytics. Graph analytics libraries and toolkit provide implementations of numbers of algorithms from graph theory.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Some libraries and toolkits offering graph analytics capabilities<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There are standalone libraries such as\u00a0NetworkX<\/a>\u00a0and\u00a0NetworKit<\/a>, python libraries for large-scale graph analysis, or\u00a0iGraph, a graph library written in C and available as Python and R packages, and library provided by graph database vendors such as Neo4j with its\u00a0Graph Algorithms Library<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Other technology vendors offer graph analytics libraries for high performance graph analytics. It is the case of the GPU technology provider NVIDIA with its\u00a0NVGraph library<\/a>. The geographic information software QGIS also built its own\u00a0library for network analysis<\/a>.<\/p>\n

Some of these libraries also propose graph visualization tools to help users build graph data exploration interfaces, but this is a topic for the third post of this series.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Graph query languages<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Finally, one important piece of analytics frameworks that was not mentioned yet: graph query languages.<\/p>\n

As for any storage system, query languages are an essential element for graph databases. These languages make it possible to model the data as a graph, and their logic is very close to the graph data model. In addition to the data modeling process, graph query languages are used to query data. Depending on their nature they can be used against databases systems and as domain-specific analytics language. Most of the high-level computing engines allow users to write using these query languages.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Some of the existing graph query languages and similar projects<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Cypher<\/a>\u00a0was created in 2011 by Neo4j to use on their own database. It has been\u00a0open-sourced in 2015<\/a>\u00a0as a separate project named\u00a0OpenCypher<\/a>. Other notable graph query languages are:\u00a0Gremlin<\/a>\u00a0the graph traversal language of Apache TinkerPop query language created in 2009 or\u00a0SPARQL<\/a>, the SQL-like language created by the W3C to query RDF graphs in 2008. More recently, TigerGraph developed its own graph query language name\u00a0GSQL<\/a>\u00a0and Oracle created\u00a0PGQL<\/a>, both SQL-like graph query languages.\u00a0G-Core<\/a>\u00a0was proposed by the Linked Data Benchmark Council (LDBC) in 2018 as a language bridging the academic and industrial worlds. Other vendors such as OrientDB went for the\u00a0relational query language SQL<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Last year, Neo4j launched an initiative to unify Cypher, PGQL and G-Core under a single standard graph query language:\u00a0GQL (Graph Query Language)<\/a>. The initiative will be discussed during a\u00a0W3C workshop in March 2019<\/a>. Some other query languages are especially dedicated to graph analysis such as\u00a0SociaLite<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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While not originally a graph query language, Facebook\u2019s\u00a0GraphQL<\/a>\u00a0is worth mentioning. This API language has been extended by graph database vendors to use as a graph query language.\u00a0Dgraph uses it natively<\/a>as its query language, Prisma is planning to\u00a0extend it to various graph databases<\/a>\u00a0and Neo4j has been pushing it into\u00a0GRANDstack<\/a>\u00a0and its query execution layer\u00a0neo4j-graphql.js<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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I listed and presented a vast majority of\u00a0graph analytics actors\u00a0<\/strong>in the following presentation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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I<\/strong>n the next post, I\u2019ll look at the actors of the third and last layer of the GraphTech landscape: the graph visualization tools.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

This post is part of a series of articles about the GraphTech ecosystem. This is the second part. It covers the\u00a0graph analytics landscape. The first part was about graph databases and the third part will list the existing graph visualization tools. Actors of the graph analytics landscape in 2019 This second layer is another back-end<\/p>\n","protected":false},"author":130,"featured_media":3926,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[95],"ppma_author":[2643],"class_list":["post-1948","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data-amp-technology"],"authors":[{"term_id":2643,"user_id":130,"is_guest":0,"slug":"elise-devaux","display_name":"Elise Devaux","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Devaux","first_name":"Elise","job_title":"","description":"Elise Devaux is Marketing Project Manager at chez Linkurious SAS, which provided the data analytics software behind the Paradise Papers, to help cover the emerging graph technology use cases. Prior to Linkurious, she worked in the humanoid robotics industry to support and foster Softbank Robotics developer community."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1948","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/130"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1948"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1948\/revisions"}],"predecessor-version":[{"id":36609,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1948\/revisions\/36609"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3926"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1948"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1948"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1948"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1948"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}