Your Path to Big Data Sucess

•

4 j'aime•1,614 vues

Cloudera, Inc.

Technologie

3
The Typical Business Intelligence Data Stack
3
BI / Reporting
EDW
Transformation (ETL)
Staging / Storage
Collection

4
Step 1: EDH for Storage/Staging/Active Archive
4
BI / Reporting
EDW
Transformation (ETL)
EDH for Storage Active Archive
Collection

5
EDH for Collection & Storage.
Step 1: EDH for Storage/Staging/Active Archive
5
BI / Reporting
EDW
Transformation (ETL)

6
Step 3: EDH for Transformation Acceleration
6
EDW
EDH for Collection,
Storage
& Transformation Acceleration.
ETL / Data
Integration
Tools
BI / Reporting

7
EDH for Collection, Storage,
Transformation Acceleration
& historical EDW data/queries.
Step 4: EDH for EDW Optimization (Impala)
7
BI / Reporting
EDW Rarely Used Data

8
Step 4: EDH for EDW Optimization (Impala)
8
EDW
BI / Reporting Agile Exploration
EDH for Collection, Storage,
Transformation Acceleration
& historical EDW data/queries.

9
Step 6: EDH for Data Science (Oryx/Spark)
9
EDH for Collection, Storage,
Transformation Acceleration
& historical EDW data/queries.
EDW
BI / Reporting Agile Exploration Data Science

10
Step 7: Full Consolidation - Apps Come to Data
10
EDW
BI Explore
Data
Science
SAS, R,
Spark
Informatica
SyncSort,
Pentaho
Hunk
...
EDH for
Collection, Storage, Transformation
Acceleration
& historical EDW data/queries.

11
Data ScienceExploration
ETL
Acceleration
Operational Efficiency Information Advantage
Cheap
Storage
BusinessIT
Journey to Achieve Full Potential
©2014 Cloudera, Inc. All Rights Reserved.
EDW
Optimization
Consolidation
360° View

12
WEB/MOBILE APPLICATION
ONLINE SERVING
SYSTEM
ENTERPRISE DATA
WAREHOUSE
ENTERPRISE
REPORTINGBI / ANALYTICSDATA MODELINGDEVELOPER
TOOLS
CLOUDERA
MANAGER
META DATA /
ETL TOOLS
ENTERPRISE DATA HUB
The Modern Information Architecture
©2014 Cloudera, Inc. All Rights Reserved.12
Data Architects System Operators Engineers Data Scientists Analysts Business Users
Customers & End Users
SYS LOGS WEB LOGS FILES RDBMS

13
Data Warehouse vs. Data Hub
©2014 Cloudera, Inc. All Rights Reserved.
Enterprise Data Warehouse Enterprise Data Hub

14
BI and Analytics
Partners
Enabling The App Store of Big Data
SI, Cloud, MSP
Partners
Database
Partners
Resellers
Data Integration
Partners
Hardware
Partners

15
Customer Success Across Industries
Financial &
Business Services
Telecom
Technology
Healthcare
Life Sciences
Media
Retail
Consumer
Energy
Public Sector

16
Conclusion: An Enterprise Data Hub Allows You To
• Active Archive
• Retain “Option Value” of Data
• Accelerate ETL Transformations
• Enable Exploration/Agility
• Consolidate Silos
• Achieve True 360 View of
Customers and Products.
©2014 Cloudera, Inc. All Rights Reserved.

Contenu connexe

En vedette

Bigdata antipatternsAnurag S

SITNL 2015 - Big Data Small PocketsJan van Ansem

Extending the EDW with Hadoop - Chicago Data Summit 2011Jonathan Seidman

BotPrize 2014 Results. Human-Like Bots Competition at IEEE CIGAccenture Analytics

Trivadis TechEvent 2016 DWH Modernization – in the Age of Big Data by Gregor ...Trivadis

Integrating BI - Data Warehouse and Big DataAccenture Analytics

Edw Data ArcAlex CK

EDW Data Model Storming for Integration of NoSQL and RDBMS by Daniel UptonDaniel Upton

Introduction to Apache ApexApache Apex

The EDW EcosystemDataWorks Summit/Hadoop Summit

Big Data vs Data WarehousingThomas Kejser

Big Data 2.0: ETL & Analytics: Implementing a next generation platformCaserta

Intro to Apache Apex @ Women in Big DataApache Apex

Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer

Modernise your EDW - Data LakeDataWorks Summit/Hadoop Summit

"Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about...Kai Wähner

Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.

Edw Optimization Solution Hortonworks

Hadoop and Enterprise Data WarehouseDataWorks Summit

MPP vs HadoopAlexey Grishchenko

En vedette (20)

Bigdata antipatterns

SITNL 2015 - Big Data Small Pockets

Extending the EDW with Hadoop - Chicago Data Summit 2011

BotPrize 2014 Results. Human-Like Bots Competition at IEEE CIG

Trivadis TechEvent 2016 DWH Modernization – in the Age of Big Data by Gregor ...

Integrating BI - Data Warehouse and Big Data

Edw Data Arc

EDW Data Model Storming for Integration of NoSQL and RDBMS by Daniel Upton

Introduction to Apache Apex

The EDW Ecosystem

Big Data vs Data Warehousing

Big Data 2.0: ETL & Analytics: Implementing a next generation platform

Intro to Apache Apex @ Women in Big Data

Hadoop Integration into Data Warehousing Architectures

Modernise your EDW - Data Lake

"Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about...

Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals

Edw Optimization Solution

Hadoop and Enterprise Data Warehouse

MPP vs Hadoop

Similaire à Your Path to Big Data Sucess

BI Data Warehouse SolutionHong Bui Van

Smarter Management for Your Data GrowthRainStor

Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA

Integrating Hadoop in Your Existing DW and BI EnvironmentCloudera, Inc.

What is a Data Warehouse and How Do I Test It?RTTS

Database Camp 2016 @ United Nations, NYC - Amir Orad, CEO, Sisense✔ Eric David Benari, PMP

Optimizing your Modern Data Architecture - with Attunity, RCG Global Services...Hortonworks

Date warehousing conceptspcherukumalla

Exadata Smart Scan - What is so smart about it?Uwe Hesse

Not Your Father's Database: How to Use Apache Spark Properly in Your Big Data...Databricks

Not Your Father's Database by Vida HaSpark Summit

Splice machine-bloor-webinar-data-lakesEdgar Alejandro Villegas

20IT501_DWDM_PPT_Unit_I.pptPalaniKumarR2

Group Presentation on Bussiness IntelligenceGaurav Paliwal

Data Mining Concept & Technique-ch04.pptMutiaSari53

DataTalks #4: Построение хранилища данных на основе платформы hadoop / Игорь ...WG_ Events

Lecture 01.pptHFLEX

Tuning ETL's for Better BIDatavail

Hadoop DeveloperEdureka!

IT Category Purchasing Managers Opportunity for Savings with Non Relational S...Bill Kohnen

Similaire à Your Path to Big Data Sucess (20)

BI Data Warehouse Solution

Smarter Management for Your Data Growth

Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...

Integrating Hadoop in Your Existing DW and BI Environment

What is a Data Warehouse and How Do I Test It?

Database Camp 2016 @ United Nations, NYC - Amir Orad, CEO, Sisense

Optimizing your Modern Data Architecture - with Attunity, RCG Global Services...

Date warehousing concepts

Exadata Smart Scan - What is so smart about it?

Not Your Father's Database: How to Use Apache Spark Properly in Your Big Data...

Not Your Father's Database by Vida Ha

Splice machine-bloor-webinar-data-lakes

20IT501_DWDM_PPT_Unit_I.ppt

Group Presentation on Bussiness Intelligence

Data Mining Concept & Technique-ch04.ppt

DataTalks #4: Построение хранилища данных на основе платформы hadoop / Игорь ...

Lecture 01.ppt

Tuning ETL's for Better BI

Hadoop Developer

IT Category Purchasing Managers Opportunity for Savings with Non Relational S...

Plus de Cloudera, Inc.

Partner Briefing_January 25 (FINAL).pptxCloudera, Inc.

Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.

2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.

Edc event vienna presentation 1 oct 2019Cloudera, Inc.

Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.

Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.

Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.

Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.

Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.

Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.

Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.

Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.

Modern Data Warehouse Fundamentals Part 3Cloudera, Inc.

Modern Data Warehouse Fundamentals Part 2Cloudera, Inc.

Modern Data Warehouse Fundamentals Part 1Cloudera, Inc.

Extending Cloudera SDX beyond the PlatformCloudera, Inc.

Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.

Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.

Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.

Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.

Plus de Cloudera, Inc. (20)

Partner Briefing_January 25 (FINAL).pptx

Cloudera Data Impact Awards 2021 - Finalists

2020 Cloudera Data Impact Awards Finalists

Edc event vienna presentation 1 oct 2019

Machine Learning with Limited Labeled Data 4/3/19

Data Driven With the Cloudera Modern Data Warehouse 3.19.19

Introducing Cloudera DataFlow (CDF) 2.13.19

Introducing Cloudera Data Science Workbench for HDP 2.12.19

Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19

Leveraging the cloud for analytics and machine learning 1.29.19

Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19

Leveraging the Cloud for Big Data Analytics 12.11.18

Modern Data Warehouse Fundamentals Part 3

Modern Data Warehouse Fundamentals Part 2

Modern Data Warehouse Fundamentals Part 1

Extending Cloudera SDX beyond the Platform

Federated Learning: ML with Privacy on the Edge 11.15.18

Analyst Webinar: Doing a 180 on Customer 360

Build a modern platform for anti-money laundering 9.19.18

Introducing the data science sandbox as a service 8.30.18

Dernier

DMCC Future of Trade Web3 - Special EditionDubai Multi Commodity Centre

DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy

Commit 2024 - Secret Management made easyAlfredo García Lavilla

The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz

Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge

Story boards and shot lists for my a level piececharlottematthew16

Take control of your SAP testing with UiPath Test SuiteDianaGray10

Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB

Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm

How to write a Business Continuity PlanDatabarracks

From Family Reminiscence to Scholarly Archive .Alan Dix

"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays

Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed

Search Engine Optimization SEO PDF for 2024.pdfRankYa

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays

"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays

CloudStudio User manual (basic edition):comworks

TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc

Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro

Dernier (20)

DMCC Future of Trade Web3 - Special Edition

DevoxxFR 2024 Reproducible Builds with Apache Maven

Commit 2024 - Secret Management made easy

The Ultimate Guide to Choosing WordPress Pros and Cons

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost

Designing IA for AI - Information Architecture Conference 2024

Story boards and shot lists for my a level piece

Take control of your SAP testing with UiPath Test Suite

Developer Data Modeling Mistakes: From Postgres to NoSQL

Streamlining Python Development: A Guide to a Modern Project Setup

How to write a Business Continuity Plan

From Family Reminiscence to Scholarly Archive .

"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...

Scanning the Internet for External Cloud Exposures via SSL Certs

Search Engine Optimization SEO PDF for 2024.pdf

"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack

"Debugging python applications inside k8s environment", Andrii Soldatenko

CloudStudio User manual (basic edition):

TrustArc Webinar - How to Build Consumer Trust Through Data Privacy

Unraveling Multimodality with Large Language Models.pdf

Your Path to Big Data Sucess

2. Your Path to Success with Big Data

3. 3 The Typical Business Intelligence Data Stack 3 BI / Reporting EDW Transformation (ETL) Staging / Storage Collection

4. 4 Step 1: EDH for Storage/Staging/Active Archive 4 BI / Reporting EDW Transformation (ETL) EDH for Storage Active Archive Collection

5. 5 EDH for Collection & Storage. Step 1: EDH for Storage/Staging/Active Archive 5 BI / Reporting EDW Transformation (ETL)

6. 6 Step 3: EDH for Transformation Acceleration 6 EDW EDH for Collection, Storage & Transformation Acceleration. ETL / Data Integration Tools BI / Reporting

7. 7 EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries. Step 4: EDH for EDW Optimization (Impala) 7 BI / Reporting EDW Rarely Used Data

8. 8 Step 4: EDH for EDW Optimization (Impala) 8 EDW BI / Reporting Agile Exploration EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries.

9. 9 Step 6: EDH for Data Science (Oryx/Spark) 9 EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries. EDW BI / Reporting Agile Exploration Data Science

10. 10 Step 7: Full Consolidation - Apps Come to Data 10 EDW BI Explore Data Science SAS, R, Spark Informatica SyncSort, Pentaho Hunk ... EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries.

11. 11 Data ScienceExploration ETL Acceleration Operational Efficiency Information Advantage Cheap Storage BusinessIT Journey to Achieve Full Potential ©2014 Cloudera, Inc. All Rights Reserved. EDW Optimization Consolidation 360° View

12. 12 WEB/MOBILE APPLICATION ONLINE SERVING SYSTEM ENTERPRISE DATA WAREHOUSE ENTERPRISE REPORTINGBI / ANALYTICSDATA MODELINGDEVELOPER TOOLS CLOUDERA MANAGER META DATA / ETL TOOLS ENTERPRISE DATA HUB The Modern Information Architecture ©2014 Cloudera, Inc. All Rights Reserved.12 Data Architects System Operators Engineers Data Scientists Analysts Business Users Customers & End Users SYS LOGS WEB LOGS FILES RDBMS

14. 14 BI and Analytics Partners Enabling The App Store of Big Data SI, Cloud, MSP Partners Database Partners Resellers Data Integration Partners Hardware Partners

15. 15 Customer Success Across Industries Financial & Business Services Telecom Technology Healthcare Life Sciences Media Retail Consumer Energy Public Sector

16. 16 Conclusion: An Enterprise Data Hub Allows You To • Active Archive • Retain “Option Value” of Data • Accelerate ETL Transformations • Enable Exploration/Agility • Consolidate Silos • Achieve True 360 View of Customers and Products. ©2014 Cloudera, Inc. All Rights Reserved.

17. Thank You! 17

Notes de l'éditeur

IN THIS SESSION, WE WILL EXPLORE USING HADOOP TO ADDRESS QUESTIONS AND ISSUES SURROUNDING * Cost of storage * Value of accessibility * Getting maximum return on your IT investments and all of your data

Your Path to Big Data Sucess

Recommandé

Recommandé

Contenu connexe

En vedette

En vedette (20)

Similaire à Your Path to Big Data Sucess

Similaire à Your Path to Big Data Sucess (20)

Plus de Cloudera, Inc.

Plus de Cloudera, Inc. (20)

Dernier

Dernier (20)

Your Path to Big Data Sucess

Notes de l'éditeur