datameer6 for prospects - june 2016_v2

© 2016 Datameer, Inc. All rights reserved.

John Morrell, Senior Director of Product MarketingSean Anderson, Senior Product Marketing Manager

Datameer 6 - The Modern BI Platform for Your Big Data Journey



About the Speakers

John MorrellSenior DirectorProduct Marketing

Sean AndersonSenior Product Marketing Manager

© Cloudera, Inc. All rights reserved. 3

Spark will replace MapReduceTo become the standard execution engine for Hadoop


Data Processing

Data may require unique processing characteristics▪ Batch▪ Streaming▪ Real-time

Hadoop arose to address one and now the ecosystem is answering the rest.▪ “We’re doubling down on Spark. We invested earliest,

and we’ve invested most, in making Hadoop enterprise-grade” Doug Cutting

Data ProcessingLeverage the right processing for your job


Powerful Data Processing - The Most Apache Spark Experience

5

STRUCTUREDSqoop

UNSTRUCTUREDKafka, Flume

PROCESS, ANALYZE, SERVE

UNIFIED SERVICES

RESOURCE MANAGEMENTYARN

SECURITYSentry, RecordService

FILESYSTEMHDFS

RELATIONALKudu

NoSQLHBase

STORE

INTEGRATE

BATCHSpark, Hive,

Pig MapReduce

STREAMSpark

SQLImpala

SEARCHSolr

SDKKite

Spark: In-memory data processing for developers and data scientists• Easy development• Flexible, extensible API• Fast batch and stream processing

Cloudera: Most experience with Spark on Hadoop for instant success • First to ship and support• Most Spark users trained• Most customers running Spark• Most engineering resources (committers, contributors, support)• Only vendor focused on enterprise Spark


Apache SparkCloudera was the first Hadoop vendor to ship and support Spark

• Spark is a fully integrated part of Cloudera’s platform• Shared data, metadata, resource management, administration, security,

and governance• Complements specialized analytic tools for comprehensive big data platform

• Cloudera is the first Hadoop vendor to offer Spark training• Trained more customers than any other vendor• Most popular training course

• Cloudera has 5x the engineering resources of the next competitor• Most committers on staff and most changes contributed• Well-trained staff across the globe with expertise implementing a broad range

of Spark use cases


Cloudera’s Engineering Commitment to Spark

7


The Spark Ecosystem & Hadoop

8

STRUCTUREDSqoop

UNSTRUCTUREDKafka, Flume

UNIFIED SERVICES

RESOURCE MANAGEMENTYARN

SECURITYSentry, RecordService

FILESYSTEMHDFS

RELATIONALKudu

NoSQLHBase

STORE

INTEGRATE

SQLImpala

SEARCHSolr

SDKKite

BATCH & STREAMSpark

Spark Streaming Spark SQL DataFrames MLlib …


Uniting Spark and Hadoop - The One Platform Initiative Investment Areas

9

ManagementLeverage Hadoop-nativeresource management.

SecurityFull support for Hadoop security

and beyond.

ScaleEnable 10k-node clusters.

StreamingSupport for 80% of common stream

processing workloads.


Community Initiative: Spark Supersedes MapReduce

10


Key Cloudera Contributions

11

• Spark-on-YARN integration• Dynamic Resource

Allocation• Kafka Integration• HBase Integration• Fixed operational issues at

scale

Integration with Hadoop Ecosystem Production-Ready Features Ongoing Initiatives

• Security• Kerberos Integration• HDFS Sync (Sentry)

• Governance• Cloudera Navigator integration

(audit & lineage)• Monitoring/

Troubleshooting• Improved debugging

• Zero Data Loss• Spark Streaming Resilience

• Standard Execution Engine• Hive on Spark • Pig on Spark• Crunch on Spark• Solr indexing on Spark


Cloudera Customers

12

• More customers running Spark than all other vendors combined• Over 170 customers• Spark clusters as large as 800 nodes• Diverse range of use cases across multiple industries

• Search personalization• Genomics research• Insurance modeling• Advertising optimization• Predictive modeling of disease conditions


Cloudera Enterprise, A New Way Forward

13


Key Cloudera Contributions

14

Download or Deploy in the Cloud

Signup for Training Contact us or a Partner to Start a POC

Getting Started is Easy


Datameer 6:The Modern BI Platform for Your Big Data Journey


Big Data Journey Challenges

Meeting Demand

(Productivity)

Putting Your

Insights to Work

Answering New

Questions

Using More of Your Data

Skill Gaps


Deep New-Age Questions

• What journey do customers take to purchase products?• What actions do customers take before they churn?• What attributes do customers with similar buying behavior have in

common?• Why do certain assets have a large impact on our overall risk? • What series of events occur before equipment fails?• Where are my network bottlenecks and how does this impact service?


More Data Sources

Business Data

Digital Interactions

Machine Data

Marketing Call Center WebSocial Media

Devices IT Sensors Security Network

CRM Sales Financial

…

…

…


Datameer: Fastest Time to Insight

Time

Com

plex

ity

4 weeks 8 weeks 12 weeks 18 months

$$$

$$

$

Enterprise Data WarehouseETL Data Warehouse BI

UseCase#2

UseCase#3

UseCase#4

UseCase#5

Datameer Use Case #1

Use Case #1

UseCase#6

UseCase#7

UseCase#8

UseCase#9

6 weeks 10 weeks

Integrate Analyze Visualize

Use Case #2 Use Case #3

Custom Big DataFlume Hive Pig Sqoop Raw Data Hadoop

NO CODIN

G


End-to-end Modern BI Platform

Integrate

• 70+ Connectors• Wizard-led• Unstructured data• High Performance

Prepare/Analyze

• 270+ Functions• Instant Profiling• Familiar Spreadsheet

UI• Advanced analytics• Smart Data

Discovery

Visualize

• 30+ Widgets• Infographics• HTML 5

Operationalize

• Security• Governance• Process integration


Agile Self-Service Analytics without Chaos

Spreadsheet Collaborative Governance & ControlDrag-n-Drop


Advanced Analytics & Smart Data Discovery

Clustering Decision Trees Dependencies Recommendations

Time Series Analytics Graph & Path Analytics Text Analytics


Augment Existing Analytics

Visualization & Exploration

Traditional BI


Enterprise Ready

Integrate, Don’t Replace

Intelligent Execution Framework

Flexible Deployment Options


What’s New in Datameer 6?

Make Big Data Simple for Everyone

Speed-to-InsightEase-of-UseRe-imagined User Experience & Workflow Spark

Enable Citizen Data Scientists Abstract Complexity


Smart Execution with Spark


Differences in Spark Implementations

BI on Spark Spark Cluster

Embedded Spark

Smart Execution

Spark• Limited to

structured view of data

• Like using Hive or Impala in Hadoop

• Fully programmatic approach

• Need specialized skills $$

• May force you to code at points

• No future-proof story

• Limited execution frameworks

• Eliminates technical complexities

• Don’t need specialized skills

• Optimizes execution/performance


New Backend Benefits

Future-proofFastest

processing, every time

Concentrate on analytics, not

backend

Abstract Complexity


Why does UI and Workflow Matter?“That is the way to learn the most, that when you are doing something with such enjoyment that you don’t notice the time passes.”


The Evolution of Analytic Workflow

Integrate Transform Analyze Visualize

1st Generation:Multiple Steps

2nd Generation:Self-Service

3rd Generation:Iterative/Fluid


Datameer 6 User Experience

Parallel WorkflowImmediate insight into downstream

effects

Fresh, ModernUI/UX

Significant Time Savings Increased Productivity


Demonstration


Learn Morehttp://www.datameer.com/product/datameer-6/

http://www.datameer.com/product/datameer-6/

http://www.datameer.com/product/datameer-6/


Thank You!

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