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Big data as a service – How does it work?

Apèsi sou lekòl la: In today’s technology world, software as a service (SaaS) is a common model. The service if offered to the subscribers as per need basis. Big data is also following the service model. In this article, Mwen pral pale sou modèl la sèvis ki te swiv nan gwo domèn teknoloji domèn,,en,Isit la se yon deskripsyon pou kèk modèl sèvis byen li te ye pou Done Big kòm yon sèvis,,en,Grap Rackspace Hadoop te kapab kouri Hadoop sou Rackspace jere serveurs dedye,,en,nwaj piblik oswa nwaj prive,,en,OneMetal pou done gwo nwaj yo bay nan Rackspace pou Apache etensèl ak Hadoop,,en,Li ofri yon,,en,konplètman jere,,en,fè-platfòm metal pou nan-memwa pwosesis,,en,Rackspace elimine pwoblèm yo ak jere ak kenbe done gwo manyèlman,,en,Li vini ak karakteristik sa yo,,en,Li redwi chay operasyon an nan bay 24x7x365 fanatik sipò,,en,Ofri Platfòm Done konplè Hortonworks,,en,aksè zouti,,en,ki gen ladan kochon,,en,ak Hcatalog,,en,Li gen yon konsepsyon rezo fleksib ak rezo tradisyonèl jiska 10GB,,en.

Here is a description for some well-known service models for Big Data as a service








Rackspace:

Rackspace Hadoop clusters could run Hadoop on Rackspace managed dedicated servers, public cloud or private cloud.

OneMetal for cloud big data is provided by Rackspace for Apache Spark and Hadoop. It offers a fully managed bare-metal platform for in-memory processing.

Rackspace eliminates the issues with managing and maintaining big data manually. It comes with the following features

• It reduces the operation burden by providing 24x7x365 fanatical support.
• Provides full Hortonworks Data Platforms (HDP) toolset access, including Pig, Hive, HBase, Sqoop, Flume, and Hcatalog.
• It has a flexible network design with traditional networking up to 10GB.

Ale pou nwaj prive vini ak nwaj piblik pouvwa ak efikasite ak sekirite ak kontwòl nan nwaj Prive,,en,Pi gwo dezavantaj pou itilize nwaj prive se ke li difisil pou jere e bezwen ekspè yo pou yo ajou,,en,patch epi kontwole yon anviwònman nwaj,,en,Rackspace bay yon sipò ekselan nan ka sa a epi pa gen okenn bezwen enkyete sou jesyon nwaj,,en,Joyent,,en,Baze sou Apache Hadoop,,en,Joyent se yon nwaj ki baze sou anviwònman hosting pou pwojè done gwo,,en,Se solisyon sa a bati lè l sèvi avèk Hortonworks Done platfòm,,en,Li se yon enfrastrikti segondè-pèfòmans veso ki natif natal pou bezwen jodi a nan aplikasyon pou mobil ak entènèt an tan reyèl,,en,Li pèmèt kouri antrepriz-klas Hadoop sou pèfòmans segondè nwaj la Joyent,,en,Avantaj sa yo ta ka ki nan lis kounye a,,en. The major disadvantage of using private clouds is that it’s difficult to manage and need experts to upgrade, patch and monitor a cloud environment. Rackspace provides an excellent support in this case and there is no need to worry about cloud management.








Joyent:

Based on Apache Hadoop, Joyent is a cloud based hosting environment for big data projects. This solution is built using Hortonworks Data Platform (HDP).

It is a high-performance container-native infrastructure for today’s need of mobile applications and real-time web. It allows running of enterprise-class Hadoop on the high performance Joyent cloud.

The following advantages could be listed now:

• De tyè nan depans sa yo enfrastrikti ta ka koupe pa solisyon ki ofri pa Joyent ak tan an repons menm,,en,x pi vit disk / I tan repons pa grap Hadoop sou Joyent Cloud,,en,Li akselere fwa yo repons nan distribye ak pwosesis paralèl,,en,Li te tou amelyore dekale nan Hadoop grap egzekite entansif done analytics aplikasyon an,,en,Syantis Done ap resevwa rezilta pi vit ak pi bon tan repons,,en,Big aplikasyon pou Done yo konsidere koute chè ak difisil yo itilize,,en,Joyent se vize nan direksyon pou chanje sa a pa bay yon pi bon mache ak solisyon pi vit,,en,Joyent bay enfrastrikti piblik ak ibrid sou nwaj pou aplikasyon pou entènèt ak mobil pou an tan reyèl,,en,Kliyan li yo gen ladan LinkedIn,,en,ap grandi,,da,Qubole,,zh-CN,Pou pwojè Big Data,,en.
• 3x faster disk I/O response time by Hadoop clusters on Joyent Cloud.
• It accelerates the response times of distributed and parallel processing.
• It also improves the scaling of Hadoop clusters executing intensive data analytics application.
• Data scientists are getting faster results with better response time.

Anjeneral, Big Data applications are considered expensive and difficult to use. Joyent is targeting towards changing this by providing a cheaper and faster solutions

Joyent provides public and hybrid cloud infrastructure for real-time web and mobile applications. Its clients include LinkedIn, Voxer, elatriye.

Qubole:

For Big Data projects, se yon gwoup Hadoop ki ofri pa Qubole ak bati-an konèkteur done ak editè grafik,,en,Pèmèt itilize varyete de baz done tankou MySQL,,en,Oracle ak etabli Hadoop gwoup sou oto-pilòt la,,en,Li bay yon editè rechèch pou ruch,,en,Kochon ak MapReduce,,en,Li bay tout bagay-kòm-yon-sèvis i.e,,en,editè rechèch pou ruch,,en,yon evalyatè ekspresyon,,en,Dach itilizasyon,,en,ETL ak konstriksyon tiyo done yo,,en,Karakteristik li yo ki nan lis anba a,,en,Kouri pi vit pase Amazon EMR,,en,Fasil yo sèvi ak entèfas ak bati-an konektè ak san pwoblèm enfrastrikti nwaj elastik,,en,Optimizasyon nan alokasyon resous ak jesyon fèt pa motè QDS hadoop lè l sèvi avèk demon,,en,Li bay yon motè Hadoop avanse pou pi bon pèfòmans,,en,Sèvi ak teknik tankou kach avanse ak akselerasyon sijè rechèch,,en. Enables to utilize variety of databases like MySQL, MongoDB, Oracle and sets Hadoop cluster on auto-pilot. It provides a query editor for Hive, Pig and MapReduce.

It provides everything-as-a-service i.e.

• query editor for Hive, Pig and MapReduce
• an expression evaluator
• utilization dashboard
• ETL and data pipeline builders

Its features are listed below:

• Runs faster than Amazon EMR
• Easy to use GUI with built-in connectors and seamless elastic cloud infrastructure.
• Optimization of resource allocation and management is done by QDS hadoop engine by using daemons. It provides an advanced Hadoop engine for better performance.

Using techniques like advanced caching and query acceleration, Qubole te demontre vitès demann nan jiska 5x pi vit ke konpare ak nwaj ki baze sou hadoop,,en,Pou demann pi vit,,en,I / O optimisé pou depo S3,,en,S3 se sekirite ak serye,,en,Sèvis Done Qubole ofri ekzekisyon 5x pi vit kont done nan S3,,en,Pa bezwen peye pou karakteristik rès ak aplikasyon an,,en,Cloud Entegrasyon i.e,,en,Sèvis done Qubole pa egzije chanjman yo dwe fè nan enfrastrikti aktyèl ou i.e,,en,li bay fleksibilite pou travay ak nenpòt ki platfòm,,en,Konektè QDS sipòte enpòte ak ekspòtasyon nan baz done nwaj MongoDB,,en,PostgresSQL ak resous tankou Google Analytics,,en,Jesyon Sik Cluster ak Sèvis Done Qubole pou fè pasaj nan minit,,en,dekale l 'ak demann ak kouri li nan anviwònman pou jesyon fasil nan Big Done evalyasyon,,en,Elastik MapReduce,,en,Amazon elastik MapReduce,,en,EMR,,en.

• For faster queries, I/O is optimized for S3 storage. S3 is secure and reliable. Qubole Data Service offers 5x faster execution against data in S3.
• No need to pay for unused features and application.
• Cloud Integration i.e. Qubole data service doesn’t require changes to be done to your current infrastructure i.e. it gives flexibility to work with any platform. QDS connectors supports import and export of cloud databases MongoDB, Oracle, PostgresSQL and resources like Google Analytics.
• Cluster Life Cycle Management with Qubole Data Service for provisioning clusters in minutes, scaling it with demand and running it in environment for easy management of Big Data assessment.









Elastic MapReduce:

Amazon Elastic MapReduce (EMR) bay yon Hadoop fondasyon jere pou senplifye gwo done pwosesis,,en,Li fasil,,en,ak pri-efikas pou distribye ak trete gwo kantite done,,en,Lòt estrikti distribye tankou Spark,,en,Presto kapab tou kouri nan Amazon EMR yo kominike avèk done nan Amazon S3 ak DynamoDB,,en,EMR okipe ka sa yo itilize ak disponiblite,,en,Web indexing,,en,Simulation syantifik,,en,Done depo,,en,Log analiz,,en,Bioinformatics,,en,Kliyan li yo gen ladan yo Yelp,,en,Nokia,,en,imaj Geti,,en,reddit,,en,Kèk nan karakteristik li yo ki nan lis anba a,,en,Fleksib pou itilize ak rasin aksè a tout egzanp,,en,sipòte plizyè distribisyon Hadoop ak aplikasyon pou,,en,Li fasil pou Customize chak gwoup epi enstale plis aplikasyon,,en,Li fasil pou enstale gwoup EMR Amazon la,,en,Serye ase pou depanse mwens tan siveyans gwoup ou a,,en. It’s easy, and cost-effective for distributing and processing large amount of data

Other distributed frameworks such as Spark, Presto can also run in Amazon EMR to interact with data in Amazon S3 and DynamoDB. EMR handles these use cases with reliability:

Its clients include Yelp, Nokia, getty images, reddit, and others. Some of its features are listed below:

• Flexible to use with root access to every instance, supports multiple Hadoop distributions and applications. It’s easy to customize every cluster and install additional application.
• It’s easy to install Amazon EMR cluster.
• Reliable enough to spend less time monitoring your cluster; rekoumanse travay echwe ak otomatikman ranplase sikonstans mal fè,,en,Tache jan li otomatikman konfigirasyon anviwònman firewall Amazon EC2,,en,Sa a se pou kontwole aksè rezo a ka,,en,Avèk Amazon EMR,,en,ou ka travay sou done yo nan nenpòt ki echèl,,en,Kantite ka ka fasilman ogmante ak diminye,,en,Pri pri ki ba ki pa gen okenn depans kache,,en,peye èdtan pou chak egzanp itilize yo,,en,Pa egzanp,,en,lanse yon gwoup Hadoop 10-ne pou tankou ti jan,,en,pou chak èdtan,,en,Li itilize pou analize done kouran klike pou konprann preferans itilizatè,,en,Advertisers ka analize sous dlo klike ak anrejistreman piblisite,,en,Li kapab tou itilize nan pwosesis kantite lajan vas nan done jenomik ak gwo done ansanm seryezman,,en,Done jenomik anime sou AWS ka jwenn aksè nan chèchè yo pou gratis,,en.
• Secure as it automatically configures Amazon EC2 firewall settings. This is for controlling network access to instances.
• With Amazon EMR, you can process data at any scale. The number of instances can be easily increased and decreased.
• Low cost pricing with no hidden costs; pay hourly for every instance used. For Example, launch a 10-node Hadoop cluster for as little as $0.15 per hour.

It is used to analyze click stream data for understanding user preferences. Advertisers can analyze click streams and advertising impression logs.

It can also be used to process vast amounts of genomic data and large data sets efficiently. Genomic data hosted on AWS could be accessed by researchers for free.

Amazon EMR ta ka itilize pou pwosesis boutèy demi lit ak ede yo nan vire petabytes nan done ki pa structurés ak semi-estriktire nan Sur itil.,,en,Mòtye,,en,Li se yon platfòm pou segondè-echèl done syans ak bati sou Amazon Web Services nwaj la,,en,Li se bati sou elastik MapReduce,,en,lanse grap Hadoop yo,,en,M Young te fonde mòtye,,en,Jeremy Kam,,pl,ak Doug Daniels nan,,en,ak yon motif elimine tan konsome travay yo difisil,,en,Sa a te fè pou syantis yo te ka pase tan travay yo ap fè lòt travay kritik,,en,Li kouri sou Java,,en,Jythoo,,hmn,Hadoop elatriye,,en,pou minimize tan envesti pa itilizatè yo ak kite yo konsantre sou syans done,,en,Li libere ekipman fatigan ak enstalasyon konsomasyon ak tan ou,,en,Ekonomize tan ak mòtèl pa jwenn solisyon nan operasyon nan yon span tan kout,,en.








Mortar:

It is a platform for high-scale data science and built on the Amazon Web Services cloud. It is built on Elastic MapReduce (EMR) to launch Hadoop clusters. Mortar was founded by K Young, Jeremy Kam, and Doug Daniels in 2011 with a motive to eliminate the time consuming difficult tasks. This was done so that the scientists could spend their work time doing other critical works.

It runs on Java, Jythoo, Hadoop etc. for minimizing time invested by users and to let them focus on data science.

It comes with the following features:

• It frees your team tedious and time-consuming installation and maintenance.
• Save time with Mortal by getting solutions into operations in a short span of time.
• Otomatikman alèt itilizatè yo pou nenpòt ki piki nan teknoloji ak aplikasyon asire w ke yo ap jwenn enfòmasyon egzat ak an tan reyèl,,en,Chanjman machann pa afekte itilizatè anpil paske li te kouri sou teknoloji louvri,,en,Aplikasyon pou platfòm Mortor,,en,Pou deplwaye yon pwisan,,en,motè rekòmandasyon évolutive,,en,platfòm ki pi rapid la se Mortor,,en,Mortor konplètman otomatize jan li kouri motè a rekòmandasyon soti nan fen nan fen ak sèlman yon sèl lòd,,en,Li itilize endistri estanda vèsyon kontwòl ki ede nan adaptasyon fasil ak personnalisation,,en,Pou analize,,en,fasil konekte sous done miltip depo done yo,,en,Li ekonomize tan travay nan ekip ou a pa manyen enfrastrikti,,en,deplwaman,,en,ak lòt operasyon,,en,Prevwa analiz lè l sèvi avèk done yo ou deja ap gen,,en.
• Vendor changes don’t affect users much because it’s been running on open technologies.

Applications of Mortor platform:

• For deploying a powerful, scalable recommendation engine, the fastest platform is Mortor.
• Mortor is fully automated as it runs the recommendation engine from end to end with only one command.
• It uses industry standard version control which helps in easy adaptation and customization.
• For analyzing, easily connect multiple data sources to data warehouses.
• It saves work time of your team by handling infrastructure, deployment, and other operations.
• Predict analysis by using the data you’re already having. Mòtye sipòte apwòch tankou regresyon lineyè,,en,klasifikasyon pou analiz,,en,Sipòte dirijan teknoloji aprantisaj machin tankou R,,en,Piton pou fournir paralèl efor pou travay konplèks,,en,monte tan ak estratejik alèt asire konfyans nan itilizatè yo ak livrezon nan analytics tiyo ankò e ankò,,en,Algorithik prediksyon yo te itilize pou grandi biznis la tankou predi ke demann,,en,idantifye gwo valè kliyan yo,,en,Analize nan gwo volim nan tèks fasil fè,,en,si wi ou non tokenization li yo,,en,imigran oswa LDA,,en,n-gram,,en,Gen yon anpil nan aplikasyon pou Big Done ki disponib jodi a ak nan lavni ta gen pi vit ak pi bon mache solisyon ki disponib pou itilizatè yo,,en,founisè sèvis ta vini ak pi bon solisyon fè enstalasyon an ak antretyen nan yon pousantaj pi bon mache,,en,techalpine.com/big-data-hadoop-as-a-service-how-does-it-work,,en, classification for analysis.
• Support leading machine learning technologies like R, Pig, Python for delivering effortless parallelization for complex jobs.
• With 99.9% up-time and strategic alerting ensures the trust of users and delivering of analytics pipeline again and again.
• Predictive algorithms are used for growing the business like predicting demand, identifying high-value customers.
• Analyzing of large volume of text is easily done, whether its tokenization, stemming or LDA, n-grams.









Summary:

There are a lot of Big Data applications available today and in future there would be faster and cheaper solutions available for users. Moreover, service providers would come with better solutions making the installation and maintenance at a cheaper rate.

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