Does Big Data Spell Big Costs- Impetus Webinar

8/2/2019 Does Big Data Spell Big Costs- Impetus Webinar

1/27

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Does Big Data Spell Big Costs ?

March 3010:00 am PT/ 1:00 pm ET

@ impetuscalling

Recorded version available at

http://www.impetus.com/webinar_registration?event=archived&eid=56
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2/27

Impetus Proprietary

Outline

Big Data Current Scenario Cost components in a Big Data Warehouse

Best Practices - Reducing the cost of Big Data solutions Cost of storage

Technologies- What and Where?

Big Data strategies Our recommendations to reduce TCO

Impetus Proprietary 2Recorded version available at

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3/27

Impetus Proprietary

Big Data - Current Scenario

2.5 Quintillion Bytes produced every day

$6 Trillion Big data cost IDC/EMC

$650 BillionCost of wasted productivity because of information

overload

1ZB Estimated Internet Traffic by 2015

1800EB Size of the digital universe in 2011

90%90% of the data in the world today has been

created in the last two years alone

18 Months Estimated time for the digital universe to double

3Impetus ProprietaryRecorded version available at

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4/27

Impetus Proprietary

Age of Data

Age of Software Age ofData


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5/27

Impetus Proprietary

Existing State of Big Data Solutions


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6/27

Impetus Proprietary

Using Commodity H/w for Big Data

Commodity Hardware Pros

Build your own

The promise of innovation

Cons

Building reliable storage $1 per GB Add the cost of managing / monitoring / hosting


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7/27

Impetus Proprietary

Using Open Source & Cloud Computing

Open Source Pros

Software is free !! Glory to the Elephant Cons

Cost of Training thinking parallel is not intuitive Cost of Support support is not free

Cloud Computing

Pros

Rent what you need Cons

$14,000 a month for 100 TB data storage only


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8/27

Impetus Proprietary

Big Data Warehouse Cost Components

Initial entry costs- Cost of experimentation Cost of integration and moving data - Cost of ETL

Query and analytics capability

Manageability

On-going maintenance - Monitoring and tuning

Changing capacity - Additional hardware

Cost of compliance




9/27

Impetus Proprietary

Lowering TCO of Big Data

Hardware Lower cost of storage

Lower cost of computation

Software Make things faster

Do more with less


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10/27

Impetus Proprietary

How to reduce the cost of storage?

Compress RainStor and similar solutions Just make sure your Read Throughput is high

Retain all v/s load & process

Setup data pipelines or use ILM Principles

Creation and Receipt Distribution

Use

Maintenance

Disposition

Focus on Big Data but dont forget the Small Data




11/27

Technologies: What and Where?

What? Open Source vs. Commercial software?

Specialized hardware/appliances vs. commodity hardware?

Vendor lock-in vs. vendor independence?

Cost of latencies?

Cloud?

Where?

OLTP - NoSQL v/s OLAP - DW (MapReduce & MPP)


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12/27

OLAP: Big Data Scenarios


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13/27

Data Tapping Point, Cost & Latency


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14/27

Impetus Proprietary

Indirect Analytics over Hadoop




15/27

Impetus Proprietary

Direct Analytics over Hadoop




16/27

Impetus Proprietary

Analytics over Hadoop with MPP DW




17/27

Selecting the Right Technology

Key considerations $ per TB

Business Continuity/ Cost/ Vendor Lock-in

Latency Needs


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18/27

Choosing MPP

$ per TB Driven EMC Greenplum

Teradata, Aster

HP Vertica

Oracle Exadata

Netezza

ParAccel

Others




19/27

Faster Map Reduce & Hadoop

Business Continuity/ Cost/ Vendor Lock-in MapR

HPCC

Hadapt

Pervasive DataRush, HStreaming

Cloud Map Reduce

DataStax Platform Computing

MARS, GPMR

ParStream


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20/27

OLTP: NoSQL Solutions

Latency Needs Column stores

HBase, Cassandra

Documents stores MongoDB, CouchDB

Key stores Redis, Riak etc.; Kyoto Cabinet/Tokyo Tyrant, Berkley

GraphDB Neo4j

Cloud stores

SimpleDB




21/27

OLTP: New Era RDBMS Versions

Postgres, InfiniDB, Infobright MySQL Cluster

GridSQL, EnterpriseDB

MS SQL

Sybase IQ

Specialized stores VoltDB, MarkLogic, Clustrix

Xeround

ParStream

Oracle NoSQL


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22/27

Impetus Proprietary

Recommendations Cost Components of a Big Data Warehouse

Initial Entry Costs - Cost of ExperimentationWe recommend Follow Best Practices , Learn or Hire

Cost of Integration and Moving Data- Cost of ETL

We recommend - Remove costly licensed tools, switch to MapReduce for ETL or ELT

Manageability - Provisioning, management tools

We recommend Opt for multi-vendor management toolsets,e.g. Impetus Ankush

On-Going Maintenance- Monitoring and Tuning

We recommend Automate! Automate! Automate!

Changing Capacity - Additional Hardware

Do you know the GPU?

2222Impetus Proprietary


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23/27

Impetus Proprietary

Recommendations Hardware & Software

Cost of Storage- Compress DataWe recommend Opting for RainStor/ similar solutions

Do More with Less - Faster MR

We recommend MapR/ similar solutions Acunu and related solutions for NoSQL



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24/27

About Us

Strategic partners for software product engineering and R&D Thought leaders in cutting-edge technologies

Mature processes and practices that are methodical, yet flexible

Diverse domain expertise

Our services in Big Data and Analytics

Expert consulting Proof-of-concept & Implementation Support services




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Big Data Quick Start Program

Three Modules Gear up (1 day session)

Base Camp (4 day session)

Summit (5 day session)


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Questions

Please send in your questions

using the chat panel





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Thank you

For more information,write to us at [email protected]

@ impetuscalling


http://www.impetus.com/webinar registration?event=archived&eid=56
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Date post:	05-Apr-2018
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