HYBRIS – CLOUD - BIGDATA
V1.0 19/11/2014
Yassine MEJRI
HYBRIS-CLOUD-BIGDATA
Agenda
 Cloud Windows Azure
 Deploying Hybris on Windows Azure
 Elasticsearch
 Kibana
 Use cases : Analytics, Machine learning.
CLOUD
Cloud Computing
A standardised IT capability (services, software or infrastructure) delivered via
internet technologies in a pay-per-use, self-service way
Cloud services are shared services, under virtualised management, accessible
over the internet
A style of computing where massively scalable IT-related capabilities are
provided “as a service” using internet technologies to multiple external customers
CLOUD
History
1960 : John McCarthy’s Concept
“Computation may someday be organized as
a public utility."
1999 : Salesforce.com
“Pioneered the concept of delivering enterprise
applications via a simple website”
2000 : Microsoft
2001 : IBM
“Expanded Sass Concept through web service”
2005 : Amazon
“Launch of Amazon web services”
2007 : Google and IBM
“Start researching Cloud Computing”
2008 : Gartner Research
“Start using Cloud Computing in many
organization”
CLOUD
Cloud computing providers
http://www.cloudscreener.com/
CLOUD
WINDOWS AZURE
WINDOWS AZURE
WINDOWS AZURE LAYERS
WINDOWS AZURE
Cloud service model
WINDOWS AZURE
Geo-location Datacenter
West US
East US
South Central US
WINDOWS AZURE
Building and running apps
WINDOWS AZURE
Building and running apps
Windows Azure Blob Storage
WINDOWS AZURE BLOB STORAGE
Architecture
Azure Blob storage is a service for storing large amounts of unstructured data, such as text or binary data, that can
be accessed from anywhere in the world via HTTP or HTTPS. Common uses of Blob storage include:
 Serving images or documents directly to a browser
 Storing files for distributed access
 Streaming video and audio
 Performing secure backup and disaster recovery
WINDOWS AZURE BLOB STORAGE
Java API
Connexion String :
public static final String storageConnectionString = "DefaultEndpointsProtocol=http;" +
"AccountName=your_storage_account;" + "AccountKey=your_storage_account_key";
Create container :
CloudStorageAccount storageAccount = CloudStorageAccount.parse(storageConnectionString);
CloudBlobClient blobClient = storageAccount.createCloudBlobClient();
CloudBlobContainer container = blobClient.getContainerReference("images");
container.createIfNotExists();
WINDOWS AZURE BLOB STORAGE
Java API
Change permissions :
BlobContainerPermissions containerPermissions = new BlobContainerPermissions();
containerPermissions.setPublicAccess(BlobContainerPublicAccessType.CONTAINER);
container.uploadPermissions(containerPermissions);
Upload blob :
final String filePath = "C:\\myimages\\myimage.jpg";
CloudBlockBlob blob = container.getBlockBlobReference("myimage.jpg");
File source = new File(filePath);
blob.upload(new FileInputStream(source), source.length());
Download blob :
for (ListBlobItem blobItem : container.listBlobs()) {
if (blobItem instanceof CloudBlob) {
CloudBlob blob = (CloudBlob) blobItem;
blob.download(new FileOutputStream("C:\\mydownloads\\" + blob.getName()));
}
}
WINDOWS AZURE BLOB STORAGE
Tables NoSQL
WINDOWS AZURE BLOB STORAGE
Queue
CLOUD
Windows Azure Management Console
CLOUD
Azure SDK : Powershell, Node.js, Java …
Windows azure SDK :
Import-AzurePublishSettingsFile -PublishSettingsFile "full path to downloaded file“
New-AzureAffinityGroup -Name pslab-group -Location "East US“
New-AzureQuickVM -ImageName $VMImage -Windows -Name $myVMName -ServiceName $myVMName AdminUsername $myAdminName -Password $myAdminPwd
-AffinityGroup pslab-grou
Stop-AzureVM -Name $myVMName -ServiceName $myVMName
Start-AzureVM -Name $myVMName -ServiceName $myVMName
Restart-AzureVM -Name $myVMName -ServiceName $myVMName
HYBRIS
Deploy Hybris
Use Case : Deploying Hybris on Windows Azure
DEPLOY HYBRIS
Architecture : auto-scalable horizontal and vertical
HTTP/HTTPS
N1
VIP : windows Azure Load Balancer (Failover,
Round Robin, Performance)
N2
Cloud Service F.O
Ni
N1
N2
CDN
Ni
Cloud Service B.O
Azure Blob Storage : Medias, Files, Attachements,
orders.pdf…
AZURE
SQL
SERVER
HYBRIS-CLOUD
Azure cloud Extension
Windows Azure Blob provides a simple web services interface that can be used to store and retrieve any
amount of data. You can configure a specific MediaFolder to store binary data of a Media item directly in Windows
Azure Blob.
To configure your folder to use Windows Azure Blob you need to have:
Windows Azure account
Properly created Access Keys
For more details read http://www.windowsazure.com/en-us/develop/net/how-to-guides/blob-storage/.
HYBRIS-CLOUD
Azure cloud Extension
HYBRIS-CLOUD
Azure cloud Extension
https://wiki.hybris.com/display/release5/Using+Windows+Azure+Blob+Media+Storage+Strategy
1. Import extension : azurecloud
2. Configure blob storage in local.properties:
Global settings :
media.globalSettings.accountKey=
media.globalSettings.accountName=
media.globalSettings.connection=UseDevelopmentStorage\=True
media.globalSettings.endPointProtocol=http
media.globalSettings.local.cache=true
media.globalSettings.public.base.url=http://127.0.0.1:10000/devstoreaccount1
media.globalSettings.secured=true
media.globalSettings.storage.strategy=windowsAzureBlobStorageStrategy
media.globalSettings.url.strategy=windowsAzureBlobURLStrategy
HYBRIS-CLOUD
Azure cloud Extension
3. How to create new blob storage folder :
……..
media.folder.invoices.accountKey=
media.folder.invoices.accountName=
media.folder.invoices.connection=UseDevelopmentStorage\=True
media.folder.invoices.endPointProtocol=http
media.folder.invoices.local.cache=true
media.folder.invoices.public.base.url=http://127.0.0.1:10000/devstoreaccount1
media.folder.invoices.secured=true
media.folder.invoices.storage.strategy=windowsAzureBlobStorageStrategy
media.folder.invoices.url.strategy=windowsAzureBlobURLStrategy
……..
HYBRIS-CLOUD
Azure cloud Extension
4. Storing Media Files :
final MediaModel media = modelService.create(MediaModel.class);
media.setCatalogVersion(catalogVersionService.getCatalogVersion("productCatalog"
, "Staged"));
final MediaFolderModel folder = mediaService.getFolder("invoices");
media.setFolder(folder);
mediaService.save(media);
HYBRIS-CLOUD
Secure media access
HYBRIS-CLOUD
Secure media access
You can enable secure media access for specific Media folder by putting in your local.properties file the following
property set to true: media.folder.<folderName>.secured=true
 It means that only secure URL will be rendered for each Media item stored in these folders. It also means that
access to these medias will be filtered only by the SecureMediaFilter.
Managing Permissions :
 Use the MediaPermissionService
 Using hMC
You can grant or deny access to a Media item for a give principal by opening specific Media item and going
to Security tab.
 Using ImpEx
Below you can find the example of an ImpEx import script for granting access to a Media item with
code 1017895.jpg for the editor principal:
INSERT_UPDATE media; code[unique=true]; catalogVersion(catalog(id),version)[unique=true];
permittedPrincipals(uid);;1017895.jpg; clothescatalog:Staged;editor;
HYBRIS-CLOUD
Azure cloud Extension
http://hybrisazure.blob.core.windows.net/hybris/sys_master/root/h3e/hd7/8796157378590.jpg
Initialze or Update Hybris :
Keep in mind that even if name of custom container is myContainer, then prefix with tenantId is added
automatically, so finally container name is sys-master-myContainer. The pattern is sys-<tenantID>-<containerName>.
To control cleaning Windows Azure storage on fresh initialization use following global property:
media.globalSettings.windowsAzureBlobStorageStrategy.cleanOnInit={true or false}
DEPLOY HYBRIS
Azure Cloud Service ?
HTTP/HTTPS
N1
VIP : windows Azure Load Balancer (Failover,
Round Robin, Performance)
N2
Cloud Service F.O
Ni
N1
N2
CDN
Ni
Cloud Service B.O
Azure Blob Storage : Medias, Files, Attachements,
orders.pdf…
AZURE
SQL
SERVER
DEPLOY HYBRIS
AzureRunMe
DEPLOY HYBRIS
Packaging and Deploy Hybris

Service Definition (*.csdef)

Service Configuration (*.cscfg)

Encrypted(Zipped(Code + *.csdef)) == *.cspkg
DEPLOY HYBRIS
Devops : Azure PowerShell cmdlets
# import Azure dll
$env:PSModulePath=$env:PSModulePath+";"+"C:\Program Files (x86)\Microsoft SDKs\Windows Azure\PowerShell
Import-Module Azure
# Connexion
Import-AzurePublishSettingsFile $pubsettings
Select-AzureSubscription -SubscriptionName $selectedsubscription
Set-AzureSubscription -CurrentStorageAccount $storageAccountName -SubscriptionName $selectedsubscription
# Create New deployement
$opstat = New-AzureDeployment -Slot $slot -Package $packageLocation -Configuration $cloudConfigLocation -label
$deploymentLabel -ServiceName $serviceName
# Upgrade deployement
$setdeployment = Set-AzureDeployment -Upgrade -Slot $slot -Package $packageLocation -Configuration
$cloudConfigLocation -label $deploymentLabel -ServiceName $serviceName -Force
# swap deployment, staging  production
Move-AzureDeployment -ServiceName $serviceName
HYBRIS-CLOUD
AzureRunMe
Demo : AzureRunMe and Windows Azure Emulator
ELASTICSEARCH
Elasticsearch
ELASTICSEARCH
Elasticsearch
https://github.com/elasticsearch
1. Java
2. Apache Lucene
3. Plug and play
4. Document Oriented
5. Scalable
6. Clustering
7. Lucene
8. Sharding and replication
9. REST/ JSON Client
10. Apache2 license
ELASICSEARCH
SQL VS ES
ELASTICSEARCH
Architecture
Cluster
Document à indexer
Node 1 (Master)
a0
b2
Route
b1
Node 2
a1
Instance ElasticSearch
b3
b0
b2
Instance ElasticSearch
Réplication
Node 3
b0
Node 4
b4
a0
b3
Instance ElasticSearch
Shard primaire
b1
a1
b4
Instance ElasticSearch
Shard répliqué
a et b = index
ELASTICSEARCH
Mapping fields types
Core types : String, Integer, Long , Double, Boolean, Date, Binary ….
IP type : "address" : { "type" : "ip", "store" : "yes" }
{
"name" : "Tom PC",
"address" : "192.168.2.123"
}
Geo point type :
"location" : { "type" : "geo_point"}
Attachement type : "my_attachment" : { "type" : "attachment" }
Token count type : The token_count field type allows us to store index information about how many words the given
field has instead of storing and indexing the text provided to the field.
"address_count" : { "type" : "token_count", "store" : "yes" }
ELASTICSEARCH
Mapping fields types
Object types : JSON documents are hierarchical in nature, allowing them to define inner "objects" within the actual
JSON.
"tweet" : {
"properties" : {
"person" : {
"type" : "object",
"properties" : {
"name" : {
"type" : "object",
"properties" : {
"first_name" : {"type" : "string"},
"last_name" : {"type" : "string"}
}
},
"sid" : {"type" : "string", "index" : "not_analyzed"}
}
},
"message" : {"type" : "string"}
}
}
ELASTICSEARCH
Mapping fields types
Nested Types : The nested type works like the object type except that an array of objects is flattened, while an array
of nested objects allows each object to be queried independently. To explain, consider this document:
Mapping :
{
"type1" : {
"properties" : {
"users" : {
"type" : "nested",
"properties": {
"first" : {"type": "string" },
"last" : {"type": "string" }
}
}
}
}
}
ELASTICSEARCH
Mapping fields types
Array types : JSON documents allow to define an array (list) of fields or objects.
"Product" : [
{
"id" : 12
"title" : "iphone",
"categories" : [1,3,5,7],
"tag" : ["iphone4", "iphone5","iphone6"],
"author" : [
{
"firstname" : "Francois",
"lastname": "francoisg",
"id" : 18
},
{
"firstname" : "Gregory",
"lastname" : "gregquat"
"id" : "2"
}
]}}
ELASTICSEARCH
Relationnel vs denormalize
ELASTICSEARCH
Relationnel vs denormalize
"translation" : {
"any_empty" : { "type" : "boolean", "index" : "not_analyzed" },
"_routing" : {
"all_empty" : { "type" : "boolean", "index" : "not_analyzed" },
"required" : true,
"status" : { "type" : "string", "index" : "not_analyzed" },
"path" : "project_id"
"phrases" : {
},
"_id" : {
"_id" : {
"path" : "id"
"path" : "id"
},
},
"type" : "nested",
"_all" : {
"properties" : {
"enabled" : "false"
"id" : { "type" : "string", "index" : "not_analyzed" },
},
"iso2_lang" : { "type" : "string", "index" : "not_analyzed" },
"dynamic" : "strict",
"content" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_standard" },
"properties" : {
"content_cs" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_cs" },
"id" : { "type" : "string", "index" : "not_analyzed" },
"created_at" : { "type" : "long", "index" : "not_analyzed" },
"public_id" : { "type" : "integer", "index" : "not_analyzed" },
"updated_at" : { "type" : "long", "index" : "not_analyzed" },
"project_id" : { "type" : "string", "index" : "not_analyzed" },
"status" : { "type" : "string", "index" : "not_analyzed" }
"title_na" : { "type" : "string", "index" : "not_analyzed" },
}
"title" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_standard" },
}
"title_cs" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_cs" },
}
"description" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_standard" },
}
"description_cs" : { "type" : "string", "index" : "analyzed", "analyzer" : "trans_cs" },
"resource_file_id" : { "type" : "integer", "index" : "not_analyzed" },
"created_at" : { "type" : "long", "index" : "not_analyzed" },
"updated_at" : { "type" : "long", "index" : "not_analyzed" },
ELASTICSEARCH
Elasticsearch : CRUD
Insert Data:
$ cat data.json
{ "index" : { "_index" : "requests" , "_type" : "request" , "_id" : 33 } }
{ "client" : "client1" , "country" : "FR" , "id" : 1, "ip" : "100.1.1.3", "password" : "test" , "sensor" : "test" , "session" :
"EFRFR34344" , "success" : "OK" ,"timestamp" : "1414183085848", "username" : "test" }
$ curl -XPOST http://localhost:9200/requests -d @data.json
Update :
$curl -XPOST 'localhost:9200/test/type1/1/_update' -d '{
"doc" : {
"name" : "new_name"
}
}'}
}‘
Delete :
$ curl -XDELETE 'http://localhost:9200/twitter/tweet/1‘
ELASTICSEARCH
Query DSL
$ curl -XPOST http://localhost:9200/_search?<YOUR_QUERY>
Type
Exemple
Terms
Apple iphone
Phrases
« apple iphone»
Proximity
« apple iphone»~5
Fuzzy
Apple~5
Wilcards
App*
Boosting
Apple^10 safari
Range
[2014/05/01 To 2014/05/30]
Boolean
apple AND NOT iphone
Fields
Title:iphone^5
ELASTICSEARCH
Query DSL
'http://localhost:9200/requests/_search?pretty' -d '{
}
]
"query": {
}
"filtered": {
},
"query": {
"filter": {
"bool": {
"bool": {
"should": [
"must": [
{
{
"query_string": {
"match_all": {}
"query": "marketing.cars >100"
},
{
}
"exists": {
},
"field": "location"
{
}
"query_string": {
}
"query": "marketing.music > 100"
]
}
}
},
}
{
}
"query_string": {
"query": "marketing.electronics > 00"
},
"fields": [
"location",
}
"remoteAddr"
},
],
{
"size": 1000
"query_string": {
"query": "marketing.fashion > 100"
}
}'
ELASTICSEARCH
MULTI SEARCH API
SearchRequestBuilder requestOne = node.client()
.prepareSearch().setQuery(QueryBuilders.matchQuery("name", "test1")).setSize(1);
SearchRequestBuilder requestTwo = node.client()
.prepareSearch().setQuery(QueryBuilders.matchQuery("name", "test2")).setSize(1);
MultiSearchResponse response = node.client().prepareMultiSearch()
.add(requestOne )
.add(requestTwo )
.execute().actionGet();
// You will get all individual responses from MultiSearchResponse#getResponses()
long nbHits = 0;
for (MultiSearchResponse.Item item : sr.getResponses()) {
SearchResponse response = item.getResponse();
nbHits += response.getHits().getTotalHits();
}
ELASTICSEARCH
Bulk API
The bulk API makes it possible to perform many index/delete operations in a single API call. This can greatly
increase the indexing speed.
 Example
$ cat requests
{ "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } }
{ "field1" : "value1" }
$ curl -s -XPOST localhost:9200/_bulk --data-binary @requests; echo
{"took":7,"items":[{"create":{"_index":"test","_type":"type1","_id":"1","_version":1}}]}
ELASTICSEARCH
Aggregations
The following snippet captures the basic structure of aggregations:
"aggregations" : {
"<aggregation_name>" : {
"<aggregation_type>" : {
<aggregation_body>
}
[,"aggregations" : { [<sub_aggregation>]+ } ]?
}
[,"<aggregation_name_2>" : { ... } ]*
}
ELASTICSEARCH
Aggregations : min, max, avg
Metric aggregations : Min, max, sum, and avg
{
{
"aggs": {
"aggs": {
"min_year": {
"min_year": {
"min": {
"min": {
"field": "year",
"field": "year"
"script": "_value - mod",
}
"params": {
}
"mod" : 1000
}
}
}
Example :
"aggs" : { "avg_price" : { "avg" : { "field" : "price" } } }
"aggs" : { "min_price" : { "min" : { "field" : "price" } } }
"aggs" : { "max_price" : { "max" : { "field" : "price" } } }
"aggs" : { "sum_price" : { "sum" : { "field" : "price" } } }
}
ELASTICSEARCH
Aggregations : The terms aggregation
Request :
Response :
"availability": {
{
"buckets": [
"aggs": {
{
"availability": {
"key": 0,
"terms": {
"doc_count": 2
"field": "copies"
},
}
{
}
"key": 1,
}
"doc_count": 1
}
},
{
"key": 6,
"doc_count": 1
}
ELASTICSEARCH
Aggregations : The range aggregation
Request :
Response :
{
"years": {
"aggs": {
"buckets": {
"years": {
"*-1850.0": {
"range": {
"to": 1850,
"field": "year",
"doc_count": 0
"keyed": true,
},
"ranges": [
"1851.0-1900.0": {
{ "to" : 1850 },
"from": 1851,
{ "from": 1851, "to": 1900 },
"to": 1900,
{ "from": 1901, "to": 1950 },
"doc_count": 1
{ "from": 1951, "to": 2000 },
},
{ "from": 2001 }
"1901.0-1950.0": {
]
"from": 1901,
"to": 1950,
ELASTICSEARCH
Aggregations : Histogram aggregation
Request :
Response :
{
{ "aggregations": {
"aggs" : {
"prices" : {
"histogram" : {
"buckets": {
"0": {
"field" : "price",
"key": 0,
"interval" : 50,
"doc_count": 2
"keyed" : true
}
"prices": {
},
"50": {
}
"key": 50,
}
}
"doc_count": 4
},
"150": {
"key": 150,
"doc_count": 3
}……
ELASTICSEARCH
Facet
Request :
}
}
{
"facets":{
"department":{
"terms":{
"field":"department_name"
}
}
},
"query":{
"constant_score":{
"boost":1.5,
"filter":{
"term":{
"department_name":"Books"
ELASTICSEARCH
Use case : Faceting using Elasticsearch Aggregations
ELASTICSEARCH
Use case : Faceting using Elasticsearch Aggregations
 Facets Component - Facets help users to narrow down / or filter a search result, facet is built based on the search
context.
 Sort Order - Sort order impacts the search results components, it defines in what order the results should be listed
on the page, for instance a user may sort by lowest to highest price or by product ratings.
 Pagination of Results - Pagination component allows an user to navigate back and forth through a search results,
this also guides the number of records that should be returned in ES query.
 Search Result - Restricted to number of records that should be displayed on the landing page, perhaps this will be
configurable based on your application needs.
ELASTICSEARCH
Use case : Faceting using Elasticsearch Aggregations
 # Get search results and facets for men's category, filter by facet selection Brand = "diesel"
$curl -XGET 'http://localhost:9200/products/_search?pretty=true' -d '{
"from" : 0, "size" : 5,
"query": {"filtered": {"filter": {"term": {"Brand": "diesel"}}, "query": { "term" : { "categories" : "men" }}}},
"aggs" : { "offerprice" : { "range" : { "field" : "offerprice", "keyed" : true, "ranges" : [ { "to" : 5 }, { "from" : 5, "to" : 10}, {
"from" : 10, "to" : 20}, { "from" : 20, "to" : 30} ] }
},
"size" : {"terms" : {"field" : "size","order": { "_count" : "asc" }}},
"Deals" : {"terms" : {"field" : "offers","order": { "_count" : "asc" }}},
"Brand" : {"terms" : {"field" : "Brand","order": { "_count" : "desc" }}}
},
"sort" : [{"offerprice" : {"order" : "asc", "mode" : "avg", "ignore_unmapped":true, "missing":"_last"}},"_score"]
}'
ELASTICSEARCH
Use case : Faceting using Elasticsearch Aggregations
# Get search results and facets for men's category, filter by facet selection Brand = "diesel" and Size "small"
curl -XGET 'http://localhost:9200/products/_search?pretty=true' -d '{
"from" : 0, "size" : 5,
"query": {"filtered": {"filter": {
"and": [{"term": {"Brand":"diesel" }},{"term": {"size":"small"}}]}, "query": { "term" : { "categories" : "men" }}}},
"aggs" : {
"offerprice" : {
"range" : {"field" : "offerprice","keyed" : true,"ranges" : [{ "to" : 5 },{ "from" : 5, "to" : 10},{ "from" : 10, "to" : 20},{
"from" : 20, "to" : 30}]}
},
"size" : {"terms" : {"field" : "size","order": { "_count" : "asc" }}},
"Deals" : {"terms" : {"field" : "offers","order": { "_count" : "asc" }}},
"Brand" : {"terms" : {"field" : "Brand","order": { "_count" : "desc" }}}
},
"sort" : [{"offerprice" : {"order" : "asc", "mode" : "avg", "ignore_unmapped":true, "missing":"_last"}},"_score"]
}'
ELASTICSEARCH
ES Client
Python :
https://github.com/elasticsearch/elasticsearch-dsl-py
Ruby :
https://github.com/printercu/elastics-rb
Javascript :
https://github.com/fullscale/elastic.js
Scala :
https://github.com/sksamuel/elastic4s
Clojure :
https://github.com/clojurewerkz/elastisch
Nodejs :
https://github.com/phillro/node-elasticsearch-client
Spring-data-elasticsearch : https://github.com/spring-projects/spring-data-elasticsearch
KIBANA
Introduction
https://github.com/elasticsearch/kibana
 Angular JS
 Responsive Design with Bootstrap
 Nodejs Platform
 Open-source
KIBANA
Dashboard KIBANA
KIBANA
Queries and filters KIBANA:
Query :
Filtering :
KIBANA
Panels : bettermap
Bettermap panel : The field that contains the coordinates, in geojson format. GeoJSON is [longitude,latitude]in an
array. This is different from most implementations, which use latitude, longitude.
KIBANA
Term panel
A table, bar chart or pie chart based on the results of an Elasticsearch terms facet.
KIBANA
Histogram panel
The histogram panel allow for the display of time charts. It includes several modes and tranformations to display
event counts, mean, min, max and total of numeric fields, and derivatives of counter fields.
KIBANA
Map
The map panel translates 2 letter country or state codes into shaded regions on a map. Currently available maps are
world, usa and europe.
KIBANA
Table
The table panel contains a sortable, pagable view of documents that. It can be arranged into defined columns and
offers several interactions, such as performing adhoc terms aggregations.
KIBANA
Text
The text panel is used for displaying static text formated as markdown, sanitized html or as plain text.
KIBANA
Trends
A stock-ticker style representation of how queries are moving over time. For example, if the time is 1:10pm, your time
picker was set to "Last 10m", and the "Time Ago" parameter was set to "1h", the panel would show how much the
query results have changed since 12:00-12:10pm
USE CASE 1 : MARKETING
Analytics Dashboard
DEMO
MAKE SENSE OF YOUR DATA
Example : Recommander Engine
IN:{HTTP Requests}
OUT:{Recommendations}
Hybris Cluster
Apache Mahout , Apache Spark
mllib or H2O…
Node 0
…
Node N
{Data analysis}
ElasticSearch, Mongodb
or Apache Solr
{insert Data}
Requests Collector
MAKE SENSE OF YOUR DATA
Example : Amazon
MAKE SENSE OF YOUR DATA
Example : Zalando
MAKE SENSE OF YOUR DATA
Example : NetFlix
Q&A