Hortonworks & IBM solutions

1 © Hortonworks Inc. 2011 – 2017. All Rights Reserved1 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
Thiago Santiago
Solutions Engineer Latam

2 © Hortonworks Inc. 2011 – 2017. All Rights Reserved2
Thiago Santiago
Engenheiro de Soluções Hadoop na Hortonworks
• 10 anos de experiência profissional de TI em desenvolvimento e arquitetura e aplicações.
• Experiência em Plataformas DataGrid, Soluções NoSQL e arquiteturas distribuídas de computação e GoF
Design Patterns
• Experiência em ALM (Application Lifecycle Management) e CI (Continuous integration)
Ultimos Projetos em BigData
• Vivo
• TIM
• Banco do Brasil
• B2W
linkedin.com/in/thiagosantiago/

3 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
The Buzzword…

BigData Implícito…

O que é BigData?

O Big Data procura responder a perguntas como: Por quê? E se? O que
acontecerá? Como otimizar? E fornecer novos insights
O intuito final é apenas um: dominar a informação!
Big Data é baseado em 3 pilares:
Veracidade e
Valor

Informação é poder!

?
Quantas vezes Homens e
mulheres no Tinder movem
seus dedos para a esquerda e
direita nas telas de seus
dispositivos por minuto?

Mudança de era…
Papa Bento
Papa Francisco

8ZB
DATAINTERNET
OF
ANYTHING
44ZB
DATA
2020
Byte
Kilobyte (KB)
Megabyte (MB)
Gigabyte (GB)
Terabyte (TB)
Petabyte (PB)
Exabyte (EB)
Zettabyte (ZB)

Como analisar essa quantidade de informação?

Hadoop
https://pt.wikipedia.org/wiki/Hadoop
Plataforma de software em Java de computação distribuída voltada para clusters e processamento de grandes
massas de dados.
Foi inspirada no MapReduce e no GoogleFS (GFS). Trata-se de um projeto da Apache de alto nível, que vai
sendo construído por uma comunidade de contribuidores Java.
O Yahoo! tem sido o maior contribuidor do projeto, utilizando essa plataforma intensivamente em seus
negócios.

Players

Hortonworks & IBM - Powering the Future of Data
IBM Spectrum Scale & HDP
Hortonworks Products
Use Cases

Expanded partnership combines the best for our clients

Innovation Pervasive in the Design
Power Systems S822LC for Big Data
Not Just Another Intel Server
NVIDIA:
Tesla K80 GPU Accelerator
Linux by Redhat:
Redhat 7.2 Linux OS
Mellanox: InfiniBand/Ethernet
Connectivity in and out of server
HGST: Optional NVMe Adapters
Alpha Data with Xilinx FPGA:
Optional CAPI Accelerator
Broadcom: Optional PCIe Adapters
QLogic: Optional Fiber Channel PCIe
Samsung: SSDs & NVMe
Hynix, Samsung, Micron: DDR4
IBM: POWER8 CPU

Feedback from Early Adopters of HDP on Power
“As a member of ODPi, AsiaInfo is selecting IBM OpenPOWER servers and Hortonworks Data Platform to create a modern
data platform for our clients. AsiaInfo recognizes the performance and flexibility advantages that IBM OpenPOWER
servers and open source Hortonworks Data Platform bring to our clients.”
Zhu Jun, Senior Product Line Executive, Orange Cloud, Product & Technology Center, AsiaInfo Data
“The Center for Innovation Management Studies at the NC State Poole College of Management supports an advanced
analytic platform to help businesses mine key insights from unstructured text by harnessing open technologies such as
IBM Power Systems, Hortonworks Data Platform and IBM Watson Content Analytics.
The combination of open technologies from IBM and Hortonworks enable advanced analytics to mine key insights from
unstructured data.”
Michael Kowolenko, Ph.D., Managing Director at Institute of Next Generation Computing at NC State University

4X
Threads per core
4X
Mem. Bandwidth1
6X
More cache2 @
Lower Latency
SMT=Simultaneous Multi-Threading
OLTP = On-Line Transaction Processing
These design decisions result in best performance for data centric workloads
like:
Spark, Hadoop, Database, NoSQL, Big Data Analytics, OLTP
POWER8: Designed for data to deliver breakthrough performance
POWER8
SMT8
x86
Hyperthread
Parallel Processing
POWER8
pipe
Data flow
x86 pipe POWER8
x86 POWER8 +
OpenPOWER
x86
1. Up to 4X depending on specific x86 and POWER8 servers being compared
2. Up to 6X more cache comparing Intel e7-8890 servers to 12 core POWER8 servers. See speaker notes for more details

Hortonworks HDP running on POWER8 Price-Performance Guarantee
• •
IBM Power Systems guarantees the Power S822LC for Big Data system built with POWER8 delivers at least a 3X price-performance
advantage vs. x86 based results when running a customer application/workload with Tez/Hive LLAP on Hortonworks HDP under the
conditions noted below. A Worker Node is a server carrying out the HDP query functions, with one Worker Node per server.
3X price-performance means that the customer's documented throughput performance on the cluster of S822LC for Big Data Worker Nodes divided by the
price of the cluster of Worker Nodes will be at least 3 times higher than the customer's documented throughput performance on the cluster of x86 based
Worker Nodes divided by the price of the cluster of x86 Worker Nodes.
EX: If queries per second on the cluster of S822LC Worker Nodes are 30,000 and 10,000 on the cluster of x86 based Worker Nodes, while the price of the S822LC Worker Node cluster is
$10,000, and the price of the x86 based Worker Node cluster is $10,000, then the Throughput Performance Per Price would be exactly 3 times higher and the guarantee would be met."
Notes:
1. Client’s Power S822LC for BD Worker Nodes and the x86 Worker Nodes must be running at similar utilization rates of at least 50% or higher, using the same software stack as described in Note #4, and which are configured similarly.
2. Client’s Power S822LC for BD performance cannot be constrained by I/O subsystem. Specifically, the I/O subsystem on the Power S822LC for BD Worker Node must achieve greater than or equal I/O bandwidth and operations per second than the x86 Worker Node.
3. Client’s Power S822LC for BD Worker Node’s physical memory must be the same or greater than the physical memory on the x86 Worker Node.
4. Applicable software stack is Tez/Hive LLAP on HDP 2.6 or later for both the Power S822LC and x86-based Worker Nodes.
5. Client is responsible for demonstrating comparable real-world representative workload between the Power S822LC for BD Worker Node and the x86 Worker Node through the use of the IBM provided tools and comparable tools on x86 systems.
6. 3X guarantee is based on a list price for x86 servers from Dell, Cisco, HP or Lenovo based on E5-2600 v4 or earlier processor technology and the IBM S822LC for Big Data.
The IBM Power S822LC for Big Data servers (22-core/2.89 GHz) used as Worker Nodes must be purchased from IBM or an authorized IBM Business Partner prior to September 30, 2017.
The guarantee period is valid for three (3) months from the date of purchase. The x86-based Worker Nodes must be comparably configured branded servers from Cisco, Dell, HP, or
Lenovo and the client is responsible for all Hortonworks licenses.
3X throughput performance per price means that the customer's documented throughput performance on the cluster of Power S822LC for BD Worker Nodes based on either queries,
operations or transactions per second divided by the price of the cluster of Worker Nodes will be at least 3 times higher than the customer's same documented throughput
performance on the cluster of x86 Worker Nodes divided by the price of said cluster of x86 Worker Nodes.
Remediation: IBM will provide additional performance optimization and tuning services consistent with IBM Best Practices, at no charge. If unable to reach the guaranteed level of
price-performance, IBM will provide additional equally configured Worker Nodes to those already purchased to reach the guaranteed level of price-performance.
NEW

HDP on POWER – Reference Configurations
Switches
1 GbE (1x or 2x):
• IBM 7120-48E (Lenovo G8052) Switch (48x 1GbE + 4x 10GbE ports)
10 GbE (2x typical, 1x allowed):
• IBM 7120-64C (Lenovo G8264) Switch (48x 10GbE + 4x 40GbE), or
• IBM 8831-S48 (Mellanox SX1410) Switch (48x 10GbE + 12x 40GbE)
Additional Config Options:
Network topologies: Flat, Dual Homed, Partial Homed, Full DMZ
Size: POC, min-production (12 node), full rack, multi rack
Balanced Performance Storage Dense
Server Type 1U S821LC (Stratton) 1U S821LC (Stratton) 1U S821LC (Stratton) 2U S822LC (Briggs) 2U S822LC (Briggs) 2U S822LC (Briggs)
Count (Min / Max) 1 / 1 3 / Any 1 / Any 8 / Any 8 / Any 8 / Any
Cores 8 20 20 22 22 11
Memory 32GB 256GB 256GB 256GB 512GB 128GB
Storage - HDD 2x 4TB HDD 4x 4TB HDD 4x 4TB HDD 12x 4TB HDD 8x 6TB HDD 12x 8TB HDD
Storage - SSD + 4x 3.8TB SSD
Storage Controller Marvell (internal)
LSI MegaRAID 9361-8i
(2GB cache)
(2GB cache)
(2GB cache)
(2GB cache)
(2GB cache)
Network - 1GbE 4 ports (internal) 4 ports (internal) 4 ports (internal) 4 ports (internal) 4 ports (internal) 4 ports (internal)
Network - 10GbE 2 ports 2 ports 2 ports 2 ports 2 ports 2 ports
System Mgmt Node Master Node Edge Node
Worker Node
Reference Architecture Document Link:
https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=POL03270USEN&

IBM Spectrum Scale is a flexible and scalable software defined file storage for analytics
workloads.
Enterprises around the globe have deployed IBM Spectrum Scale for
Compute clusters (HPC)
Big data and analytics
High performance backup and restores
Content repositories

Spectrum Scale + HDP
IBM Spectrum Scale
• Spectrum Scale becomes the storage layer in your HDP environment.
• Spectrum Scale supports accessing data using HDFS API and hence is transparent to the applications using HDP.
• With the federation support, Spectrum Scale can co-exist with HDFS.
• Enterprise class storage for your Hadoop/Spark environment (Encryption, Compression, Tiering, DR…)

Scalability, Performance
Scalability
HDFS can scale upto 350 Million files with a single name node due to scale-out architecture limitation. Name
node becomes a bottleneck. Users have to use federation functionality to overcome this limitation.
Spectrum Scale has parallel file system architecture different from scale-out architecture of HDFS. No single
metadata server in the architecture as a bottleneck. Metadata serving function is distributed across the cluster.
Test limit for number of files per filesystem is 9 Billion. We have Spectrum Scale customers running in
production beyond this test limit.
Performance
Performance depends on the underlying hardware configurations. But we claim comparable or better
performance than HDFS on all equivalent hardware configurations. Here is the report of tests performed by
NASA for evaluating Spectrum Scale vs HDFS - http://files.gpfsug.org/presentations/2016/SC16/06_-
_Carrie_Spear_-_Spectrum_Sclale_and_HDFS.pdf

Reduce data center footprint with Spectrum Scale
HDFS
Raw
data
ext4
ext4
write move copy
Traditional
applications
Copies in both HDFS and ext4
Spectrum
Scale
Application
writes direct
to Hadoop
Path with
NFS/SMB/
Object/POSIX
direct-read with
NFS/SMB/
Object/POSIX
Raw
data
Traditional
applications
Multiple copies with HDFS based workflow Spectrum Scale in-place analytics (No copies required)
Hadoop analysis Jobs Hadoop
analysis Jobs
Direct read, one version
Data Scientists waste days just copying data to HDFS No copies required with Spectrum Scale
Costly data protection - Default uses 3-way replication with
HDFS
IBM ESS Software RAID eliminates need for 3 way replication.
Just 30% extra storage requirement.
[Erasure coding in HDFS has limitations and is good only for cold data]
Traditional
applications Traditional
applications
HDFS APIs
HDFS APIs
Example: For 5PB of data, HDFS requires 15PB of storage Example: For 5PB of data, ESS requires 6.5PB of storage
Copy process can take hours/days & eventually results are based on stale data.

Spectrum Scale vs Competition
Supported Interfaces (In-place Analytics) HDFS MapR-FS NetApp EMC Isilon IBM Spectrum Scale
HDFS API ü ü ü ü ü
NFS ü ü ü ü ü
SMB ü ü
Object ü ü
POSIX ü ü
ü True SDS solution to overcome HDFS scalability and availability limitations. SW only option available that can be directly installed on
commodity hardware running Hadoop clusters.
ü Reduce data center footprint with industry’s best in-place analytics solution. No need to copy data to & from HDFS.
Supported Storage Architecture HDFS MapR-FS NetApp EMC Isilon IBM Spectrum Scale
Shared nothing (Storage SW that runs on commodity servers) ü ü ü
Shared storage (Separate storage HW) ü ü ü
- Spectrum Scale makes Hortonworks Data Platform stronger against MapR that bundles proprietary file system MapR-FS.
- Use formal certification for Spectrum Scale with Hortonworks as your Cloudera attack.

Architecture choices
Shared Nothing model
Good for:
• High data locality requirements
• Starting small
Shared Storage model
Good for:
• Large capacity deployments
• Requirement to grow storage independent of compute
• In-place analytics, mix workload (Hadoop & traditional) on same storage
Support for combination of both these models in a single Hadoop cluster is coming soon.
10 GigE / 40 GigE / InfiniBand (Spectrum Scale I/0)
HDP HDP HDP HDP
Storage-Rich Servers
• IBM Power
• Commercial X86
S S S S
E E
InfiniBand (RDMA) / 40 GigE / 10 GigE
Compute Nodes
• IBM Power or X86
• Only Hadoop services and
HDFS client
ESS
HDP HDP HDP HDP HDP
S Spectrum Scale
ESS Elastic Storage Server(Powered by Spectrum Scale)

The Datalake
HDP
Data Science
IT Systems & Ops
HDF

HORTONWORKS DATA FLOW
NIFI
STREAMING & INTEGRATION OPERATIONS SECURITY
1.2.0
HDF 3.0
1H2017
1.0.0
HDF 2.0
Mar 2016
* HDF 3.0 – Shows current Apache branches being used. Final component version subject to change based on Apache release process.
1.1.0
SuperSet
TP
Ranger
0.7.0
0.5.0
0.6.0
Ambari
2.5.1
2.4.0
2.4.2
Kafka
0.10.1.0
0.9.0
0.10.0
Zookeeper
3.4.6
3.4.6
3.4.6
Storm
1.1.0
1.0.1
1.0.2
SAM
0.5.0
Schema Registry
0.3.0
HDF 2.1
Aug 2016
Ongoing Innovation in Apache
Hortonworks Data Flow 3.0
HDP 2.2
Dec 2014
HDF 1.0
Dec 2014
0.3.0
0.6.1
HDF 1.2
Oct 2015
MiNiFi
0.2.0
1.0.0
0.0.1
0.10.0

HORTONWORKS DATA PLATFORM
Hadoop
& YARN
DATA MGMT DATA ACCESS GOVERNANCE & INTEGRATION OPERATIONS SECURITY
HDP 2.2
Dec 2014
HDP 2.2
Dec 2014
2.2.0
2.4.0
2.6.0
2.7.1
HDP 2.3
Oct 2015
2.7.3
HDP 2.6*
1H2017
2.7.1
HDP 2.4
Mar 2016
* HDP 2.6 – Shows current Apache branches being used. Final component version subject to change based on Apache release process.
** Spark 1.6.3+ Spark 2.1 – HDP 2.6 supports both Spark 1.6.3 and Spark 2.1 as GA.
*** Hive 2.1 is GA within HDP 2.6.
**** Apache Solr is available as an add-on product HDP Search.
2.7.3
Sqoop
1.4.4
1.4.5
1.4.4
1.4.6
1.4.6
1.4.6
1.4.6
Druid
0.9.2
Knox
0.4.0
0.5.0
0.6.0
0.11.0
0.6.0
0.9.0
Ranger
0.4.0
0.5.0
0.7.0
0.5.0
0.6.0
Ambari
1.4.4
2.0.0
1.5.1
2.1.0
2.5.0
2.2.1
2.4.0
Kafka
0.8.2
0.8.1
0.10.1.0
0.9.0
0.10.0
Zookeeper
3.4.5
3.4.6
3.4.5
3.4.6
3.4.6
3.4.6
3.4.6
Flume
1.5.2
1.4.0
1.3.1
1.5.2
1.5.2
1.5.2
1.5.2
Solr
4.10.2
4.7.2
5.2.1
5.5.1
****
5.2.1
5.5.1
Slider
0.60.0
0.80.0
0.91.0
0.80.0
0.91.0
Atlas
0.5.0
0.8.0
0.5.0
0.7.0
Accumulo
1.6.1
1.5.1
1.7.0
1.7.0
1.7.0
1.7.0
Phoenix
4.0.0
4.2.0
4.4.0
4.7.0
4.4.0
4.7.0
Storm
0.9.3
0.10.0
0.9.1
1.1.0
0.10.0
1.0.1
Falcon
0.5.0
0.6.0
0.6.1
0.10.0
0.6.1
0.10.0
Tez
0.4.0
0.5.2
0.7.0
0.7.0
0.7.0
0.7.0
Hive
0.12.0
0.13.0
0.14.0
1.2.1
1.2.1+
2.1***
1.2.1
1.2.1+
2.1***
Pig
0.12.0
0.12.1
0.14.0
0.15.0
0.16.0
0.15.0
0.16.0
HDP 2.5
Aug 2016
Oozie
3.3.2
4.1.0
4.0.0
4.2.0
4.2.0
4.2.0
4.2.0
Spark
1.2.1
1.4.1
1.6.3+
2.1**
1.6.0
1.6.2+
2.0**
HBase
0.98.4
0.96.1
0.98.0
1.1.2
1.1.2
1.1.2
1.1.2
Zeppelin
0.7.0
0.6.0
HDP 2.1
April 2014
HDP 2.0
Oct 2013
Ongoing Innovation in Apache
Hortonworks Data Platform 2.6

Hortonworks Connection: Services and Solutions for Your Success
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Hortonworks
Connection

Open Source Optimizes Variety and Efficiency
Leadership from the Committers
Reduced Risk of Lock-In
Seamless Integration
Unmatched Economics
COST
EFFICIENCY
DATA
VARIETY
EDW
PROPRIETARY
HADOOP
HORTONWORKS
OPEN SOURCE
RDBMS

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Seasoned Professional Services
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Helping you to maximize connected data platforms investments
Hortonworks Professional Services
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Advice
• Define data platform strategy
• Plan for successful roll out
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Managed Services
• Optimize platforms on site
• Extend your team
• Offload cluster administration

Training By Hortonworks University
Comprehensive Role-Based Learning Paths
Flexible Delivery
Skills Certifications

Actionable Intelligence Personalizes Digital Advertising
Market
Research
Studies
CRM
Records
Online
Transactions
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Streams
Impressions
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Consumption Logs
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SEGMENTATION
ONLINE AD
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RECOMMENDATIONS
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Data
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Surveys
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Actionable Intelligence Drives the New Automotive Industry
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Tracking
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Metadata
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Systems
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Streams
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MAINTENANCE
SUPPLY CHAIN
OPTIMIZATION
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YIELDS MAXIMIZATION
QUALITY
CONTROL
NEW PRODUCT
PLANNING
ERP
Systems
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Testing Data
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Data Data
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Design Docs
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Records

Actionable Intelligence Transforms Energy & Utilities
Asset
Data
Customer
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Streams
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SINGLE VIEW
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PREDICTIVE EQUIPMENT
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TRADING
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Actionable Intelligence Powers Today’s Financial Services
OFAC
Lists
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Streams Transactions &
Wires
Stock
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CUSTOMER 360
RISK DATA
AGGREGATION
ANTI-MONEY
LAUNDERING
FRAUD
DETECTION
TRADE
SURVEILLANCE
Mobile App
Data
Trade
Data
Web
Logs
Banker
Notes
Demographic
Data
Customer
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Data

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Patient
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Lab Data
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Data
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Locations
Wearables
Intra-Network
Data
Sensor
Data
Claims
Data
Social
Media Physician
Notes
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Satisfaction Data
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(EMR) Data
SINGLE VIEW OF
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REAL-TIME VITAL
SIGN MONITORING
BILLING &
REIMBURSEMENTS
EMR
OPTIMIZATION
SUPPLY CHAIN
OPTIMIZATION

Actionable Intelligence Is Shaping the Modern Insurance Industry
Catastrophic
Event Data
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Onboarding Data
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Data
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Metadata
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Drones &
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Claims Docs,
Notes & Diaries
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Underwriting
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Photos

Actionable Intelligence Powers Modern Manufacturing
Defect Testing
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Designs
MES
Systems
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Streams
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Systems
Shop Floor
Sensors
PREVENTATIVE
MAINTENANCE
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MAXIMIZATION
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Systems
Supplier
Receipts
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Data
Assembly
Line Sensors
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Historians
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Orders

Actionable Intelligence Fuels Oil & Gas Industry Renovation
ERP Data
Engineering
Notes
IoT Gateway
Data Video
WITSML
Data
Weather &
Environment
REAL-TIME
MONITORING
SINGLE VIEW OF
OPERATIONS
PREDICTIVE
MAINTENANCE
LAS ARCHIVE
& ANALYTICS
UNSTRUCTURED DATA
CLASSIFICATION
Vehicle GPS
Data
GIS Data
SCADA
Systems Field
Comments
Production
Histories
G&G
Data

Actionable Intelligence Makes Pharmaceuticals Safe & Effective
Research
Cohort Data
Molecular
Data
RFID Data
Social Media
Biometrics
Sensor
Data
DRUG TRIAL
COHORT SELECTION
YIELD
OPTIMIZATION
RAW MATERIAL
WASTE REDUCTION
SEARCHABLE
RESEARCH REPOS
NEXT-GEN
SEQUENCING (NGS)
Supply Chain
Geo-location Data
Scientific
Studies
Manufacturing
Machine Data
Clinical
Records
Sales
Reports
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Data

Actionable Intelligence Enhances Public Sector Efficiency
Historical
Archives
Cyber Threat
Metadata
Vehicle
Telemetry Data
Disease
Outbreaks
Natural
Disasters
PUBLIC
TRANSPORTATION
INFRASTUCTURE
MAINTENANCE
PUBLIC
HEALTH
NATIONAL
DEFENSE
HOMELAND
SECURITY
Social
MediaWork
Orders
Meeting
Notes
Voter Rolls
Public Benefits
Claims
Financial
Audits
Extreme
Weather Alerts

Actionable Intelligence Drives Retail Sales Growth
Product
Catalogs
Sales
Forecasts
Beacons &
RFID Server
Logs
In-Store
WiFi Logs
Store
Communications
SINGLE VIEW OF
THE CUSTOMER
PRODUCT
RECOMMENDATIONS
INVENTORY &
SUPPLY CHAIN
PRICING
OPTIMIZATION
TARGETED
PROMOTIONS
Clickstream
ERP
Data
Social
Media
Staffing
Plans
Store
Reporting
CRM
Records

Actionable Intelligence Transforms the Software Industry
Cyber Security
Metadata
Sales
Forecasts
Mobile Device
Geo-Location Server
Logs
User Activity
Events
Network
Logs
NEW PRODUCT
DEVELOPMENT
QUALITY
ASSURANCE
CUSTOMIZATION &
PERSONALIZATION
CYBER
SECURITY
REAL-TIME USAGE
MONITORING
Clickstreams
CRM
Records
Social Media
Streams
Sprints &
Backlogs
User
Testing
Historical
Audit Trails

Connected Data Drives Success in Telecommunications
Call Detail
Records
Product
Catalogs
Cyber Threat
Metadata
Sensor
Data
Server
Logs
Voice-to-Text
SINGLE VIEW OF
THE CUSTOMER
CHURN
REDUCTION
CDR
ANALYSIS
NETWORK
OPTIMIZATION
DYNAMIC BANDWIDTH
ALLOCATION
Clickstream
ERP System
Data
Social
Media Billing
Data
Subscriber
Profiles
CRM
Records

Client
Use CasesLegacy Cluster
Telecom Architecture standard
Tooling Data Science, Machine
Learning
Model Pré-
processing
Analytics, BI, Ad-hoc
Exploration
Data
Exploration
Complex
Event
Processing
Kafka SAM
Analytics, BI, Ad-hoc
Exploration
Visualization
& Reporting
All Data
HDFS
Tooling
Hive
Bach Views
Tooling
SuperSet
Real Time Views
Custom Applications
Dashboards
Batch LayerSpeed LayerServing Layer
Ingest
Atlas/Ranger
Model
Building
R
Spark
Druid
Marketing
Others
Customer Sentiment & Churn
Use Case
Zeppelin
Network Optimization Journey
Real-time Marketing &
Advertising
Relational Bases
Social Networks
WebSites
Mobile Apps
CDR - Network
OOT
Adwords/adserver
Beacon
TWW/Smart Focus
…
CRM

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