Utrecht NL-HUG/Data Science-NL - Agile Data Slides

Agile Analytics ApplicationsRussell Jurney (@rjurney) - Hadoop Evangelist @HortonworksFormerly Viz, Data Science at Ning, LinkedInHBase Dashboards, Career Explorer, InMaps© Hortonworks Inc. 2012 1

Agile Data - The Book (March, 2013) Read it now on OFPS A philosophy, not the only way But still, its good! Really! © Hortonworks Inc. 2012 2

We go fast... but don’t worry!• Examples for EVERYTHING on the Hortonworks blog: http://hortonworks.com/blog/authors/russell_jurney• Download the slides - click the links - read examples!• If its not on the blog, its in the book!• Order now: http://shop.oreilly.com/product/0636920025054.do• Read the book NOW on OFPS:• http://ofps.oreilly.com/titles/9781449326265/chapter_2.html © Hortonworks Inc. 2012 3

Agile Application Development: Check• LAMP stack mature• Post-Rails frameworks to choose from• Enable rapid feedback and agility + NoSQL © Hortonworks Inc. 2012 4

Data Warehousing © Hortonworks Inc. 2012 5

Scientific Computing / HPC • ‘Smart kid’ only: MPI, Globus, etc. until HadoopTubes and Mercury (old school) Cores and Spindles (new school) UNIVAC and Deep Blue both fill a warehouse. We’re back... © Hortonworks Inc. 2012 6

Data Science? Application Data WarehousingDevelopment 33% 33% 33% Scientiﬁc Computing / HPC © Hortonworks Inc. 2012 7

Data Center as Computer • Warehouse Scale Computers and applications“A key challenge for architects of WSCs is to smooth out these discrepancies in a cost efﬁcient manner.”Click here for a paper on operating a ‘data center as computer.’ © Hortonworks Inc. 2012 8

Hadoop to the Rescue! Big data refinery / Modernize ETL Audio, Web, Mobile, CRM, Video, ERP, SCM, … Images New Data Business Transactions Docs, Sources Text, & Interactions XML HDFS Web Logs, Clicks Big Data Social, Refinery SQL NoSQL NewSQL Graph, Feeds ETL EDW MPP NewSQL Sensors, Devices, RFID Business Spatial, GPS Apache Hadoop Intelligence & Analytics Events, Other Dashboards, Reports, Visualization, … Page 7 I stole this slide from Eric. Update: He stole it from someone else. © Hortonworks Inc. 2012 9

Hadoop to the Rescue!• Easy to use! (Pig, Hive, Cascading)• CHEAP: 1% the cost of SAN/NAS• A department can afford its own Hadoop cluster!• Dump all your data in one place: Hadoop DFS• Silos come CRASHING DOWN!• JOIN like crazy!• ETL like whoah!• An army of mappers and reducers at your command• OMGWTFBBQ ITS SO GREAT! I FEEL AWESOME! © Hortonworks Inc. 2012 10

NOW WHAT? © Hortonworks Inc. 2012 ? 11

Analytics Apps: It takes a Team• Broad skill-set to make useful apps• Basically nobody has them all• Application development is inherently collaborative © Hortonworks Inc. 2012 12

Data Science Team• 3-4 team members with broad, diverse skill-sets that overlap• Transactional overhead dominates at 5+ people• Expert researchers: lend 25-50% of their time to teams• Pick relevant researchers. Leave them alone. They’ll spawn new products by accident. Not just CS/Math. Design. Art?• Creative workers. Run like a studio, not an assembly line• Total freedom... with goals and deliverables.• Work environment matters most: private, social & quiet space• Desks/cubes optional © Hortonworks Inc. 2012 13

How to get insight into product?• Back-end has gotten t-h-i-c-k-e-r• Generating $$$ insight can take 10-100x app dev• Timeline disjoint: analytics vs agile app-dev/design• How do you ship insights efficiently?• How do you collaborate on research vs developer timeline? © Hortonworks Inc. 2012 14

The Wrong Way - Part One“We made a great design. Your job is to predict the future for it.” © Hortonworks Inc. 2012 15

The Wrong Way - Part Two“Whats taking you so long to reliably predict the future?” © Hortonworks Inc. 2012 16

The Wrong Way - Part Three “The users don’t understand what 86% true means.” © Hortonworks Inc. 2012 17

The Wrong Way - Part Four GHJIAEHGIEhjagigehganbanbigaebjnain!!!!!RJ(@J?!! © Hortonworks Inc. 2012 18

The Wrong Way - Inevitable Conclusion Plane Mountain © Hortonworks Inc. 2012 19

Reminds me of... the waterfall model © Hortonworks Inc. 2012 :( 20

Chief ProblemYou can’t design insight in analytics applications. You discover it. You discover by exploring. © Hortonworks Inc. 2012 21

-> Strategy So make an app for exploring your data. Iterate and publish intermediate results. Which becomes a palette for what you ship. © Hortonworks Inc. 2012 22

Data Design• Not the 1st query that = insight, its the 15th, or the 150th• Capturing “Ah ha!” moments• Slow to do those in batch...• Faster, better context in an interactive web application.• Pre-designed charts wind up terrible. So bad.• Easy to invest man-years in the wrong statistical models• Semantics of presenting predictions are complex, delicate• Opportunity lies at intersection of data & design © Hortonworks Inc. 2012 23

How do we get back to Agile? © Hortonworks Inc. 2012 24

Statement of Principles (then tricks, with code) © Hortonworks Inc. 2012 25

Setup an environment where...• Insights repeatedly produced• Iterative work shared with entire team• Interactive from day 0• Data model is consistent end-to-end• Minimal impedance between layers• Scope and depth of insights grow• Insights form the palette for what you ship• Until the application pays for itself and more © Hortonworks Inc. 2012 26

Value document > relationMost data is dirty. Most data is semi-structured or un-structured. Rejoice! © Hortonworks Inc. 2012 27

Value document > relationNote: Hive/ArrayQL/NewSQL’s support of documents/array types blur this distinction. © Hortonworks Inc. 2012 28

Relational Data = Legacy?• Why JOIN? Storage is fundamentally cheap!• Duplicate that JOIN data in one big record type!• ETL once to document format on import, NOT every job• Not zero JOINs, but far fewer JOINs• Semi-structured documents preserve data’s actual structure• Column compressed document formats beat JOINs! (paper coming) © Hortonworks Inc. 2012 29

Value imperative > declarative• We don’t know what we want to SELECT.• Data is dirty - check each step, clean iteratively.• 85% of data scientist’s time spent munging. See: ETL.• Imperative is optimized for our process.• Process = iterative, snowballing insight• Efficiency matters, self optimize © Hortonworks Inc. 2012 30

Value dataflow > SELECT © Hortonworks Inc. 2012 31

Ex. dataflow: ETL + email sent count © Hortonworks Inc. 2012 (I can’t read this either. Get a big version here.) 32

Value Pig > Hive (for app-dev)• Pigs eat ANYTHING• Pig is optimized for refining data, as opposed to consuming it• Pig is imperative, iterative• Pig is dataflows, and SQLish (but not SQL)• Code modularization/re-use: Pig Macros• ILLUSTRATE speeds dev time (even UDFs)• Easy UDFs in Java, JRuby, Jython, Javascript• Pig Streaming = use any tool, period.• Easily prepare our data as it will appear in our app.• If you prefer Hive, use Hive.But actually, I wish Pig and Hive were one tool. Pig, then Hive, then Pig, then Hive... See: HCatalog for Pig/Hive integration, and this post. © Hortonworks Inc. 2012 33

Localhost vs Petabyte scale: same tools• Simplicity essential to scalability: highest level tools we can• Prepare a good sample - tricky with joins, easy with documents• Local mode: pig -l /tmp -x local -v -w• Frequent use of ILLUSTRATE• 1st: Iterate, debug & publish locally• 2nd: Run on cluster, publish to team/customer• Consider skipping Object-Relational-Mapping (ORM)• We do not trust ‘databases,’ only HDFS @ n=3.• Everything we serve in our app is re-creatable via Hadoop. © Hortonworks Inc. 2012 34

Data-Value Pyramid Climb it. Do not skip steps. See here. © Hortonworks Inc. 2012 35

0/1) Display atomic records on the web © Hortonworks Inc. 2012 36

0.0) Document-serialize events• Protobuf• Thrift• JSON• Avro - I use Avro because the schema is onboard. © Hortonworks Inc. 2012 37

0.1) Documents via Relation ETLenron_messages = load /enron/enron_messages.tsv as ( message_id:chararray, sql_date:chararray, from_address:chararray, from_name:chararray, subject:chararray, body:chararray); enron_recipients = load /enron/enron_recipients.tsv as ( message_id:chararray, reciptype:chararray, address:chararray, name:chararray); split enron_recipients into tos IF reciptype==to, ccs IF reciptype==cc, bccs IF reciptype==bcc; headers = cogroup tos by message_id, ccs by message_id, bccs by message_id parallel 10;with_headers = join headers by group, enron_messages by message_id parallel 10;emails = foreach with_headers generate enron_messages::message_id as message_id, CustomFormatToISO(enron_messages::sql_date, yyyy-MM-dd HH:mm:ss) as date, TOTUPLE(enron_messages::from_address, enron_messages::from_name) as from:tuple(address:chararray, name:chararray), enron_messages::subject as subject, enron_messages::body as body, headers::tos.(address, name) as tos, headers::ccs.(address, name) as ccs, headers::bccs.(address, name) as bccs;store emails into /enron/emails.avro using AvroStorage( Example here. © Hortonworks Inc. 2012 38

0.2) Serialize events from streamsclass GmailSlurper(object): ... def init_imap(self, username, password): self.username = username self.password = password try: imap.shutdown() except: pass self.imap = imaplib.IMAP4_SSL(imap.gmail.com, 993) self.imap.login(username, password) self.imap.is_readonly = True ... def write(self, record): self.avro_writer.append(record) ... def slurp(self): if(self.imap and self.imap_folder): for email_id in self.id_list: (status, email_hash, charset) = self.fetch_email(email_id) if(status == OK and charset and thread_id in email_hash and froms in email_hash): print email_id, charset, email_hash[thread_id] self.write(email_hash) © Hortonworks Inc. 2012 Scrape your own gmail in Python and Ruby. 39

0.3) ETL Logslog_data = LOAD access_log USING org.apache.pig.piggybank.storage.apachelog.CommongLogLoader AS (remoteAddr, remoteLogname, user, time, method, uri, proto, bytes); © Hortonworks Inc. 2012 40

1) Plumb atomic events -> browser (Example stack that enables high productivity) © Hortonworks Inc. 2012 41

Lots of Stack Options with Examples• Pig with Voldemort, Ruby, Sinatra: example• Pig with ElasticSearch: example• Pig with MongoDB, Node.js: example• Pig with Cassandra, Python Streaming, Flask: example• Pig with HBase, JRuby, Sinatra: example• Pig with Hive via HCatalog: example (trivial on HDP)• Up next: Accumulo, Redis, MySQL, etc. © Hortonworks Inc. 2012 42

1.1) cat our Avro serialized eventsme$ cat_avro ~/Data/enron.avro{ ubccs: [], ubody: uscamming people, blah blah, uccs: [], udate: u2000-08-28T01:50:00.000Z, ufrom: {uaddress: ubob.dobbs@enron.com, uname: None}, umessage_id: u<1731.10095812390082.JavaMail.evans@thyme>, usubject: uRe: Enron trade for frop futures, utos: [ {uaddress: uconnie@enron.com, uname: None} ]} © Hortonworks Inc. 2012 Get cat_avro in python, ruby 43

1.2) Load our events in Pigme$ pig -l /tmp -x local -v -wgrunt> enron_emails = LOAD /enron/emails.avro USING AvroStorage();grunt> describe enron_emailsemails: { message_id: chararray, datetime: chararray, from:tuple(address:chararray,name:chararray) subject: chararray, body: chararray, tos: {to: (address: chararray,name: chararray)}, ccs: {cc: (address: chararray,name: chararray)}, bccs: {bcc: (address: chararray,name: chararray)}} © Hortonworks Inc. 2012 44

1.3) ILLUSTRATE our events in Piggrunt> illustrate enron_emails ---------------------------------------------------------------------------| emails || message_id:chararray || datetime:chararray || from:tuple(address:chararray,name:chararray) || subject:chararray || body:chararray || tos:bag{to:tuple(address:chararray,name:chararray)} || ccs:bag{cc:tuple(address:chararray,name:chararray)} || bccs:bag{bcc:tuple(address:chararray,name:chararray)} |---------------------------------------------------------------------------| || <1731.10095812390082.JavaMail.evans@thyme> || 2001-01-09T06:38:00.000Z || (bob.dobbs@enron.com, J.R. Bob Dobbs) || Re: Enron trade for frop futures || scamming people, blah blah || {(connie@enron.com,)} || {} || {} | Upgrade to Pig 0.10+ © Hortonworks Inc. 2012 45

1.4) Publish our events to a ‘database’From Avro to MongoDB in one command:pig -l /tmp -x local -v -w -param avros=enron.avro -param mongourl=mongodb://localhost/enron.emails avro_to_mongo.pigWhich does this:/* MongoDB libraries and configuration */register /me/mongo-hadoop/mongo-2.7.3.jarregister /me/mongo-hadoop/core/target/mongo-hadoop-core-1.1.0-SNAPSHOT.jarregister /me/mongo-hadoop/pig/target/mongo-hadoop-pig-1.1.0-SNAPSHOT.jar/* Set speculative execution off to avoid chance of duplicate records in Mongo */set mapred.map.tasks.speculative.execution falseset mapred.reduce.tasks.speculative.execution falsedefine MongoStorage com.mongodb.hadoop.pig.MongoStorage(); /* Shortcut *//* By default, lets have 5 reducers */set default_parallel 5avros = load $avros using AvroStorage();store avros into $mongourl using MongoStorage(); © Hortonworks Inc. 2012 Full instructions here. 46

1.5) Check events in our ‘database’$ mongo enronMongoDB shell version: 2.0.2connecting to: enron> show collectionsemailssystem.indexes> db.emails.findOne({message_id: "<1731.10095812390082.JavaMail.evans@thyme>"}){" "_id" : ObjectId("502b4ae703643a6a49c8d180")," "message_id" : "<1731.10095812390082.JavaMail.evans@thyme>"," "date" : "2001-01-09T06:38:00.000Z"," "from" : { "address" : "bob.dobbs@enron.com", "name" : "J.R. Bob Dobbs" }," "subject" : Re: Enron trade for frop futures," "body" : "Scamming more people..."," "tos" : [ { "address" : "connie@enron", "name" : null } ]," "ccs" : [ ]," "bccs" : [ ]} © Hortonworks Inc. 2012 47

1.6) Publish events on the webrequire rubygemsrequire sinatrarequire mongorequire jsonconnection = Mongo::Connection.newdatabase = connection[agile_data]collection = database[emails]get /email/:message_id do |message_id| data = collection.find_one({:message_id => message_id}) JSON.generate(data)end © Hortonworks Inc. 2012 48

1.6) Publish events on the web © Hortonworks Inc. 2012 49

Whats the point?• A designer can work against real data.• An application developer can work against real data.• A product manager can think in terms of real data.• Entire team is grounded in reality!• You’ll see how ugly your data really is.• You’ll see how much work you have yet to do.• Ship early and often!• Feels agile, don’t it? Keep it up! © Hortonworks Inc. 2012 50

1.7) Wrap events with Bootstrap<link href="/static/bootstrap/docs/assets/css/bootstrap.css" rel="stylesheet"></head><body><div class="container" style="margin-top: 100px;"> <table class="table table-striped table-bordered table-condensed"> <thead> {% for key in data[keys] %} <th>{{ key }}</th> {% endfor %} </thead> <tbody> <tr> {% for value in data[values] %} <td>{{ value }}</td> {% endfor %} </tr> </tbody> </table></div></body> Complete example here with code here. © Hortonworks Inc. 2012 51

1.7) Wrap events with Bootstrap © Hortonworks Inc. 2012 52

Refine. Add links between documents. Not the Mona Lisa, but coming along... See: here © Hortonworks Inc. 2012 53

1.8) List links to sorted eventsUse Pig, serve/cache a bag/array of email documents:pig -l /tmp -x local -v -wemails_per_user = foreach (group emails by from.address) { sorted = order emails by date; last_1000 = limit sorted 1000; generate group as from_address, emails as emails; };store emails_per_user into $mongourl using MongoStorage();Use your ‘database’, if it can sort.mongo enron> db.emails.ensureIndex({message_id: 1})> db.emails.find().sort({date:0}).limit(10).pretty() { { " "_id" : ObjectId("4f7a5da2414e4dd0645d1176"), " "message_id" : "<CA+bvURyn-rLcH_JXeuzhyq8T9RNq+YJ_Hkvhnrpk8zfYshL-wA@mail.gmail.com>", " "from" : [ ... © Hortonworks Inc. 2012 54

1.8) List links to sorted documents © Hortonworks Inc. 2012 55

1.9) Make it searchable...If you have list, search is easy with ElasticSearch and Wonderdog.../* Load ElasticSearch integration */register /me/wonderdog/target/wonderdog-1.0-SNAPSHOT.jar;register /me/elasticsearch-0.18.6/lib/*;define ElasticSearch com.infochimps.elasticsearch.pig.ElasticSearchStorage();emails = load /me/tmp/emails using AvroStorage();store emails into es://email/email?json=false&size=1000 using ElasticSearch(/me/elasticsearch-0.18.6/config/elasticsearch.yml, /me/elasticsearch-0.18.6/plugins);Test it with curl: curl -XGET http://localhost:9200/email/email/_search?q=hadoop&pretty=true&size=1ElasticSearch has no security features. Take note. Isolate. © Hortonworks Inc. 2012 56

From now on we speed up... Don’t worry, its in the book and on the blog. http://hortonworks.com/blog/ © Hortonworks Inc. 2012 57

2) Create Simple Charts © Hortonworks Inc. 2012 58

2) Create Simple Tables and Charts © Hortonworks Inc. 2012 59

2) Create Simple Charts• Start with an HTML table on general principle.• Then use nvd3.js - reusable charts for d3.js• Aggregate by properties & displaying is first step in entity resolution• Start extracting entities. Ex: people, places, topics, time series• Group documents by entities, rank and count.• Publish top N, time series, etc.• Fill a page with charts.• Add a chart to your event page. © Hortonworks Inc. 2012 60

2.1) Top N (of anything) in Pigpig -l /tmp -x local -v -wtop_things = foreach (group things by key) { sorted = order things by arbitrary_rank desc; top_10_things = limit sorted 10; generate group as key, top_10_things as top_10_things; };store top_n into $mongourl using MongoStorage();Remember, this is the same structure the browser gets as json. This would make a good Pig Macro. © Hortonworks Inc. 2012 61

2.2) Time Series (of anything) in Pigpig -l /tmp -x local -v -w/* Group by our key and date rounded to the month, get a total */things_by_month = foreach (group things by (key, ISOToMonth(datetime)) generate flatten(group) as (key, month), COUNT_STAR(things) as total;/* Sort our totals per key by month to get a time series */things_timeseries = foreach (group things_by_month by key) { timeseries = order things by month; generate group as key, timeseries as timeseries; };store things_timeseries into $mongourl using MongoStorage(); Yet another good Pig Macro. © Hortonworks Inc. 2012 62

Data processing in our stackA new feature in our application might begin at any layer... great! omghi2u! I’m creative! I’m creative too! where r my legs? I know Pig! I <3 Javascript! send halp Any team member can add new features, no problemo! © Hortonworks Inc. 2012 63

Data processing in our stack... but we shift the data-processing towards batch, as we are able. See real example here. Ex: Overall total emails calculated in each layer © Hortonworks Inc. 2012 64

3) Exploring with Reports © Hortonworks Inc. 2012 65

3) Exploring with Reports © Hortonworks Inc. 2012 66

3.0) From charts to reports...• Extract entities from properties we aggregated by in charts (Step 2)• Each entity gets its own type of web page• Each unique entity gets its own web page• Link to entities as they appear in atomic event documents (Step 1)• Link most related entities together, same and between types.• More visualizations!• Parametize results via forms. © Hortonworks Inc. 2012 67

3.1) Looks like this... © Hortonworks Inc. 2012 68

3.2) Cultivate common keyspaces © Hortonworks Inc. 2012 69

3.3) Get people clicking. Learn.• Explore this web of generated pages, charts and links!• Everyone on the team gets to know your data.• Keep trying out different charts, metrics, entities, links.• See whats interesting.• Figure out what data needs cleaning and clean it.• Start thinking about predictions & recommendations. ‘People’ could be just your team, if data is sensitive. © Hortonworks Inc. 2012 70

4.0) Preparation• We’ve already extracted entities, their properties and relationships• Our charts show where our signal is rich• We’ve cleaned our data to make it presentable• The entire team has an intuitive understanding of the data• They got that understanding by exploring the data• We are all on the same page! © Hortonworks Inc. 2012 72

4.2) Think in different perspectives• Networks• Time Series / Distributions• Natural Language Processing• Conditional Probabilities / Bayesian Inference• Check out Chapter 2 of the book... © Hortonworks Inc. 2012 See here. 73

4.3.1) Weighted Email Networks in PigDEFINE header_pairs(email, col1, col2) RETURNS pairs { filtered = FILTER $email BY ($col1 IS NOT NULL) AND ($col2 IS NOT NULL); flat = FOREACH filtered GENERATE FLATTEN($col1) AS $col1, FLATTEN($col2) AS $col2; $pairs = FOREACH flat GENERATE LOWER($col1) AS ego1, LOWER($col2) AS ego2;}/* Get email address pairs for each type of connection, and union them together */emails = LOAD /me/Data/enron.avro USING AvroStorage();from_to = header_pairs(emails, from, to);from_cc = header_pairs(emails, from, cc);from_bcc = header_pairs(emails, from, bcc);pairs = UNION from_to, from_cc, from_bcc;/* Get a count of emails over these edges. */pair_groups = GROUP pairs BY (ego1, ego2);sent_counts = FOREACH pair_groups GENERATE FLATTEN(group) AS (ego1, ego2), COUNT_STAR(pairs) AS total; © Hortonworks Inc. 2012 75

4.4) Time Series & Distributionspig -l /tmp -x local -v -w/* Count things per day */things_per_day = foreach (group things by (key, ISOToDay(datetime)) generate flatten(group) as (key, day), COUNT_STAR(things) as total;/* Sort our totals per key by day to get a sorted time series */things_timeseries = foreach (group things_by_day by key) { timeseries = order things by day; generate group as key, timeseries as timeseries; };store things_timeseries into $mongourl using MongoStorage(); © Hortonworks Inc. 2012 79

4.4.2) Regress to find TrendsJRuby Linear Regression UDF Pig to use the UDF Trend Line in your Application © Hortonworks Inc. 2012 81

4.5.1) Natural Language Processing import tfidf.macro; my_tf_idf_scores = tf_idf(id_body, message_id, body); /* Get the top 10 Tf*Idf scores per message */ per_message_cassandra = foreach (group tfidf_all by message_id) { sorted = order tfidf_all by value desc; top_10_topics = limit sorted 10; generate group, top_10_topics.(score, value); } © Hortonworks Inc. 2012 Example with code here and macro here. 82

4.5.3) NLP for All: Extract Topics!• TF-IDF in Pig - 2 lines of code with Pig Macros:• http://hortonworks.com/blog/pig-macro-for-tf-idf-makes- topic-summarization-2-lines-of-pig/• LDA with Pig and the Lucene Tokenizer:• http://thedatachef.blogspot.be/2012/03/topic-discovery- with-apache-pig-and.html © Hortonworks Inc. 2012 84

4.6.2) Step 2: COUNT(From, To, Cc)/Total P(cc | to) = Probability of cc’ing someone, given that you’ve to’d someone © Hortonworks Inc. 2012 89

Example: Packetpig and PacketLoopsnort_alerts = LOAD $pcap USING com.packetloop.packetpig.loaders.pcap.detection.SnortLoader($snortconﬁg);countries = FOREACH snort_alerts GENERATE com.packetloop.packetpig.udf.geoip.Country(src) as country, priority;countries = GROUP countries BY country;countries = FOREACH countries GENERATE group, AVG(countries.priority) as average_severity;STORE countries into output/choropleth_countries using PigStorage(,); Code here. © Hortonworks Inc. 2012 93

• Amsterdam, March 20, 21st• Call for papers now open!• Submit a lightning talk!• http://hadoopsummit.org/amsterdam/• Discount coupons - 10% off! © Hortonworks Inc. 2012 95

Hortonworks Data Platform • Simplify deployment to get started quickly and easily • Monitor, manage any size cluster with familiar console and tools • Only platform to include data 1 integration services to interact with any data • Metadata services opens the platform for integration with existing applications • Dependable high availability architecture Reduce risks and cost of adoption • Tested at scale to future proof Lower the total cost to administer and provision your cluster growth Integrate with your existing ecosystem © Hortonworks Inc. 2012 96

Hortonworks Training The expert source for Apache Hadoop training & certificationRole-based Developer and Administration training – Coursework built and maintained by the core Apache Hadoop development team. – The “right” course, with the most extensive and realistic hands-on materials – Provide an immersive experience into real-world Hadoop scenarios – Public and Private courses availableComprehensive Apache Hadoop Certification – Become a trusted and valuable Apache Hadoop expert © Hortonworks Inc. 2012 97

Next Steps?1 Download Hortonworks Data Platform hortonworks.com/download2 Use the getting started guide hortonworks.com/get-started3 Learn more… get support Hortonworks Support • Expert role based training • Full lifecycle technical support • Course for admins, developers across four service levels and operators • Delivered by Apache Hadoop • Certification program Experts/Committers • Custom onsite options • Forward-compatible hortonworks.com/training hortonworks.com/support © Hortonworks Inc. 2012 98

Thank You!Questions & AnswersSlides: http://slidesha.re/O8kjaFFollow: @hortonworks and @rjurneyRead: hortonworks.com/blog © Hortonworks Inc. 2012 99

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Utrecht NL-HUG/Data Science-NL - Agile Data Slides

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