Simplifying Big Data Integration

Goals of the Modern Data Architecture

• Centralize all your data

• Turn raw data into insights

•Maintain governance, compliance and security standards

• Eliminate complexities within IT

Global Leader in Big Iron to Big Data Solutions

4Syncsort Confidential and Proprietary - do not copy or distribute

Deep engineering and go-to-market relationships with strategic partners in Big Iron and Big Data ecosystems

Marquee global customer base of leaders and emerging businesses across all major industries

PEARL RIVER, NY

SINGAPORE

JAPAN

Trusted Industry Leadership Providing world-class data management software to large enterprises for nearly 50 years with a focus on customer value and unparalleled support

Lower Cost, Higher PerformanceOur fast, efficient software helps power business and operational analytics while optimizing resource utilization to dramatically reduce the cost of managing mainframe and legacy systems

Connecting Big Iron To Big Data Our solutions address the complex set of requirements for delivering rich data from mainframe and legacy sources to Big Data environments

Our Strategy: Simplify Big Data Integration

• Deploy on premise or in the cloud

• Choose among multiple execution frameworks – Hadoop, Spark, Linux, Unix, Windows

• Integrate streaming and batch data with a single data pipeline for innovative applications, like IoT

• Future-proof applications to avoid re-writing jobs in order to take advantage of innovations in new execution frameworks

• Access and integrate ALL enterprise data sources – including mainframe – for advanced analytics

Simplify Big Data Integration with Syncsort

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Access Integrate Comply Simplify

Get best in class data ingestion capabilities for Hadoop. Mainframes, RDBMS, MPP, JSON, Parquet, Avro, ORC, NoSQL, Kafka and more.

Single interface for streaming and batch processes. Single data pipeline for all enterprise data, batch or streaming.

Secure data access, data governance and lineage. Seamless integration with Kerberos, Apache Ranger, Apache Ambari, Cloudera Manager, Cloudera Navigator and Sentry.

Design once, deploy anywhere & insulate your organization from rapidly changing eco-system. Future proof your applications for new compute frameworks, on premise or in the cloud.

Polling Question

What stage of Hadoop implementation is your organization in?

– Researching/Evaluating

– Proof of Concept/Pilot

– In production

– None of the above

About the Survey – Who Responded

270+ IT decision makers

36% with revenue of $1B+

Financial Services

22%

Insurance7%

Data Services5%

Government7%

Healthcare10%

Non-profit3%

Retail8%

Software/SaaS5%

Telco8%

Information Technology

16%

Travel/Hospitality3%

Other6%

INDUSTRY

BI/Data Analyst12%

CIO/CTO/COO7%

Data Architect18%

Data Scientist9%

Developer17%

Other IT Exec5%

IT Manager21%

Other 11%

ROLE

Compute Frameworks and Cloud Deployment

Multiple frameworks are the norm – 54% use more than one framework

Migration away from MapReduce, toward Spark

More organizations are deploying on premises – but almost a third have a hybrid deployment

62%

40%

15%

11%

30%

10%

2%

4%

38%

52%

24%

13% 4%

8%

22%

6%

Polling Question

Do you plan on changing execution frameworks in the next 12 months?

– Yes

– No

– I don’t know

Data Sources

Traditional data sources are most popular for filling the data lake

Newer sources like streaming data and NoSQL databases on the rise

Even more pronounced for Financial Services & Insurance:

– 2x more likely to ingest MF data

– 20% more likely to pull data from RDBMS

Mainframe & Hadoop

71% of the Fortune 500 relies on mainframe data to conduct 30 billion business transactions a day

– 96 of the world’s top 100 banks

– 9 out of the world’s 10 largest insurance companies

– 23 out of the top 25 U.S. retailers

New solutions make it easier to take advantage of this critical data with Hadoop

HOW VALUABLE IS BEING ABLE TO

ACCESS/INTEGRATE MAINFRAME DATA

INTO HADOOP?

The Role of Hadoop

Hadoop & the Data Warehouse

– 70% of all EDW’s are performance and capacity constrained

– Shifting ELT workloads to Hadoop is a popular remedy

– However, most don’t expect Hadoop to replace their existing EDWs

Nearly half of respondents see Hadoop as a:

– Way to power better, faster analytics

– Cost effective storage/data archive strategy

Top Use Cases for Hadoop

Polling Question

What are the top use cases for Hadoop in your organization?

– Active archive

– Advanced/predictive analytics

– Data discover/visualization

– ETL

– Clickstream/social media data analysis

– Internet of Things

– EDW/Mainframe offload

– Operational analytics

– Real-time analytics

Significant Business Benefits Expected

Polling Question

What is the #1 challenge/barrier for implementing Hadoop in your organization?

– Lack of skills/staffing

– Cost

– Connectivity to existing data sources/apps

– Rapid change in tools/technology

– Difficulty moving data in/out of Hadoop

– Uncertainty

Top 3 Challenges/Barriers to Hadoop Adoption

Skills/Staffing:– 58% of respondents ranked it as a

significant challenge

– Top challenge reported for the 3rd

year in a row

Rapid Change:– 53% of respondents ranked it as a

significant challenge

– Difficulty keeping up with evolving compute frameworks and Hadoop-related tools

Connectivity:– 48% of respondents ranked it as a

significant challenge

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What’s coming in 2017

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2

3

4

5

Success begets success

Big Data insights will take their seat in the boardroom

Data governance grows in importance

Tools will become more flexible to adapt to changing

and hybrid environments

Organizations will seek simplicity to sustain & scale early

success

Thank You