that is written like an advertisement . please help improve it by removing promotional content and inappropriate external links and by adding encyclopedic content written from a neutral point of view . may 2016 learn how and when to remove this template message for other uses see aster disambiguation . teradata aster analytics type public part of teradata corp industry advanced analytics fate acquired founded 2005 headquarters san carlos california usa products teradata aster analytics parent teradata website www.teradata.com teradata aster overview aster data systems is a data management and analysis software company headquartered in san carlos california . it was founded in 2005 and acquired by teradata in 2011 . contents 1 products 2 history 3 clients and partners 4 recognition 5 references 6 external links products edit teradata aster analytics is a multi genre scalable advanced analytics solution that empowers business users to uncover and operationalize non intuitive insights . aster analytics combines different data stores analytic processing and analytic algorithms that are typically found across multiple silo d platforms within a single analytic solution to solve complex business problems . aster analytics leverages sql mapreduce graph and r functions on data across row column and file stores . the ability to combine analytics from multiple genres such as machine learning statistics path pattern graph or text within a single work stream is a unique differentiator for aster analytics . aster analytics is based on a massively parallel processing mpp architecture where tasks are run simultaneously across multiple nodes for more efficient processing . the aster database includes three analytic engines sql mapreduce and graph designed to provide optimal processing of the analytic task across massive volumes of data . in addition the analytics are performed directly in the database to eliminate data movement and to leverage the mpp . while most big data platforms require specialized data science skills to design build and maintain parallel mapreduce programs aster analytics provides interfaces to enable a broad range of users to discover insights from their data . highly technical users such as programmers and data scientist can code directly to the apis for the engines to develop their own analytics . business analysts with sql skills can leverage over 100 prebuilt functions to easily run sophisticated analytics against their data . it s simple to deploy these analytic programs using teradata aster appcenter to enable self service big data discovery by enterprise users . the complete aster analytics software solution is made up of several components the database and client the analytics portfolio and appcenter . aster database and client the aster database is a parallel database that enables users to store and process multi structured data . this section describes the components and provides a compare contrast analysis with a traditional relational database . data stores relational databases require a data model with structure such as third normal form or a star schema . while the aster database can load multi structured data in raw form . machine data web logs text or internet of things data can be loaded as a row column and file store and transformed into structured data for further analytic processing . analytic engines aster analytics offers three different analytic engines sql sql mapreduce and sql gr that can be used together within a single query delivering optimal performance . additionally users can install open source r as an add on engine for r processing . each is described in more detail below sql aster analytics offers a true relational sql engine for efficient set based processing . many of the statistical techniques sorting merging and aggregations are best done in sql . sql mapreduce mapreduce is a programming model not a language and implementation for processing large multi structured data sets . programs written in this functional style are automatically parallelized and executed on a large cluster of servers . mapreduce is better suited than sql for row over row processing use cases such as pattern detection affinity analysis and collaborative filtering . the sql implementation of such use cases translates into expensive full table scans and multiple table joins whereas mapreduce uses a procedural framework to process such use cases more efficiently . iterative queries such as affinity analysis of more than two products text analytics and many machine learning techniques are typically processed more efficiently using mapreduce vs. sql . sql gr aster analytics sql gr is a native graph processing engine that makes it easy to perform powerful graph analysis especially complex graph analysis such as social network influencer analysis fraud detection supply chain management network analysis and threat detection and money laundering across big data sets . sql gr is based on the bulk synchronous processing bsp model and uses massively iterative distributed and parallel processing to solve complex graph problems . aster sql gr is focused on large scale graph analytics with processing architectures and apis designed specifically for parallel execution on commodity class clusters . r open source r and r packages can be embedded directly in the aster analytics database where r scripts can be executed on one or many v workers for concurrent processing across multiple v workers . with aster 6.20 the aster r client package allows users to run r scripts through the r apply family of functions . this eliminates the need to know sql . in addition aster r provides a library of prebuilt parallel functions that leverages the sql mapreduce and sql engines . aster r also includes combiners to implement the split apply combine construct to parallelize r functions . seamless network analytics processing snap the snap framework works transparently to deliver an integrated end user experience with a single user interface and a single high level language sql across multiple processing engines in the top layer and data stores in the bottom layer . using the unified sql interface analysts and data scientists can invoke and combine various types of analytics graph path pattern text sql and statistics in a single sql statement . integrated optimizer takes the query with multiple analytics and parses it into multiple sub queries to be executed by workload specific execution engines like sql gr and sql mapreduce . the integrated optimizer provides powerful advanced optimizations like global collaborative planning and adaptive optimization and execution by eliminating redundant operations and reordering of operators in a query . integrated executor orchestrates the execution of query and iterative phases across multiple engines . it passes parameters and results and performs common workload management and monitoring across engines . common storage system and services provide a unified data storage architecture that eliminates wasteful and redundant effort . it is a plug and play system designed for typed data stores and provides common services such as fault tolerance replication security and snapshots across the data stores . aster client aster r aster client includes client tools such as aster database cluster terminal odbc jdbc and . net interfaces loaders and exporter and most notably aster r. aster r package that allows an r user to interact with aster through an r interface . the package provides wrappers for r functions that run sql or sql mr functions in aster analytics to parallelize them . aster r package also provides runners that allow users to run r scripts across single or multiple v workers to run tasks concurrently . these runners also provide a combiner that allows programmers to write custom sql parallel programs using the mapreduce constructs . this is a powerful capability that allows r programmers to exceed the data processing and memory limitations of r. the aster analytics portfolio the integrated discovery portfolio provides over 100 ready to use sql based functions that address the big data analytics and discovery process . these analytics are defined in detail in the aster analytics portfolio white paper . statistical analysis approximate distinct count approximate percentile average confusion matrix correlation coxph coxpredict coxsurvfit distribution matching exponential moving average enhanced histogram f measure generalized linear model glm glm predict histogram k nearest neighbor classification knn lars functions linear regression logistic predict deprecated logistic regression deprecated lrtest percentile principal component analysis sample simple moving average support vector machines vector distance volume weighted average price weighted moving average decision trees forest drive predict & analyze single tree drive & predict na ve bayes na ve bayes map reduce & predict data transformation antiselect apache log parser fellegisunter trainer & predict e mail parser identity match ipgeo json parser multicase murmurhash outlier filter pst parser afs pack pivot scale functions string similarity sessionization unpack unpivot uri pack & unpack xml parser & relation cluster analysis canopy gaussian mixture model kmeans kmeansplot minhash association analysis basket generator collaboration filter wsrecommender graph analysis all pairs shortest path betweenness closeness eigenvector centrality gtree hidden markov model local clustering coefficient loopy belief propagation modularity ntree pagerank personalized salsa random walk sample shapley value text analysis chinese text segmentation latent dirichlet allocation lda levenshtein distance na ve bayes text classifier named entity recognition ner ngram pos tagger sentenizer sentiment extraction term frequency inverse document frequency tf idf text classifier text chunker text morph text parser text tagging text tokenizer time series path & pattern matching arima & predictor attribution burst cumulative moving average dynamic time warping dtw discrete wavelet transforms discrete wavelet transforms on 2 dimensional input frequent paths inverse discrete wavelet transforms inverse wavelet transforms on multiple sequences interpolator npath path generator starter & summarizer symbolic aggregation approximation sax sessionization shapelets location analysis load geometries point in polygon geometry overlay visualization cfilterviz npathviz aster database system function nc skew nc relationstats figure 1 the aster analytics portfolio teradata aster appcenter teradata aster appcenter provides a framework that allows data scientists and business analysts to embed their sql or java analytic code into apps which can be shared or deployed within appcenter as shown in the diagram below . the appcenter framework provides a web based portal to manage and view apps services to build apps and restful api and other methods for third party tool integration . these apps are stored managed and invoked through a web based interface allowing self service access to users of all skill levels . history edit aster data was co founded in 2005 by stanford university graduate students george candea mayank bawa and tasso argyros . 1 2 it received funding from first round capital sequoia capital institutional venture partners cambrian ventures jafco ventures as well as angel investors including rajeev motwani ron conway and david cheriton . 3 it received first round of funding of 5 million in 2005 then a second round of 17 million in february 2009 and third round of 30 million in september 2010 . 4 teradata had acquired an 11 percent ownership interest in aster data systems in september 2010 . on march 3 2011 teradata agreed to pay an additional 263 million for the remaining ownership interest net of debt and other expenses . 5 6 the acquisition completed in april 2011 . 2 in september 2011 a computer appliance version of the product was announced with pre configured software bundled with hardware . 7 in october 2012 aster announced a second version of its appliance . in addition to the aster database software the new appliance was available with nodes running the hortonworks distribution of apache hadoop . 8 9 in october 2013 version 6 of aster database software was announced . it supported graph database technology and a file system that the company said was compatible with the hadoop distributed file system . 10 11 in june 2014 teradata introduced aster r which extends the power of open source r analytics by lifting the memory and processing limitations . aster r offers the r analyst an enterprise ready business analytics solution that is massively scalable reliable and easy to use . aster r leverages a high performance computer platform with all the benefits of security data management and an ensemble