How to Avoid Pitfalls in Big Data Analytics Webinar


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Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.

Watch this webinar to learn how to navigate the most common obstacles of big data analytics.

Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.

In this webinar, we will show you how to:

• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively

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How to Avoid Pitfalls in Big Data Analytics Webinar

  1. 1. © 2014 Datameer, Inc. All rights reserved. How to Avoid Pitfalls in 
 Big Data Analytics"
  2. 2. View Recording "" You can view the recording of this webinar at: How-to-Avoid-Pitfalls-in-Big-Data- Analytics-OnDemand.html
  3. 3. © 2013 Datameer, Inc. All rights reserved. Matt Schumpert @datameer Senior Director, Solutions Engineering Matt has been working in the enterprise infrastructure software space for over 14 years in various capacities, including sales engineering, strategic alliances and consulting. Matt currently runs the pre-sales engineering team at Datameer, supporting all technical aspects of customer engagement from initial contact through roll-out of customers into production. Matt holds a BS in Computer Science from the University of Virginia.  #datameer @datameer About Our Speaker"
  4. 4. © 2013 Datameer, Inc. All rights reserved. Dale Kim @MapR Director, Product Marketing Dale Kim is the Director of Product Marketing at MapR.  His background includes a variety of technical and management roles at information technology companies. While his experience includes work with relational databases, much of his career pertains to non-relational data in the areas of search, content management, and NoSQL.   Dale holds an MBA from Santa Clara University, and a BA in Computer Science from the University of California, Berkeley. #mapr @mapr About Our Speaker"
  5. 5. Agenda" ▪ Quick introduction to Hadoop ▪ Overview of analytics on Hadoop ▪ Quick tips on big data analytics ▪ Our 5 big data pitfalls to avoid
  6. 6. Quick Introduction to Apache Hadoop" ▪ What is Apache Hadoop – Software framework for reliable, scalable, distributed computing – “Divide-and-conquer” approach to processing large data sets ▪ Hadoop does analytics – Hadoop is the platform of choice for big data – If you have big data, then you are analyzing big data
  7. 7. Types of Analytics for Hadoop" ▪ Descriptive – what happened, and why – The “why” is also known as “diagnostic” – Data mining, management reporting
  8. 8. Types of Analytics for Hadoop [2]" ▪ Predictive – what will happen – Cross-sell/up-sell (recommendations), fraud/ anomaly detection ▪ Prescriptive – what should I do – Preventative maintenance,
 smart meter analysis Better with more data
  9. 9. Common Data Types for Hadoop" ▪ Clickstream/user behavior history ▪ Sensor/machine/event logs ▪ Social media profiles & communication ▪ Data warehouse data (structured, SoR) ▪ Long-tail/archive data
  10. 10. The Foundation for an Analytics Platform" ▪ Performance – Make sure you get results in a timely manner ▪ Scalability – Let your platform grow as your data grows ▪ Reliability – Keep your users productive ▪ Ease-of-use – Give users an end-to-end, self-service platform that delivers fast time-to-insight
  11. 11. Quick Tips on Big Data Analytics" ▪  Minimize copying large data volumes across the wire ▪  Plan for production issues (system responsiveness,
 performance, high availability, disaster recovery, audits) ▪  Start by looking for ways Hadoop can supplement, not supplant your existing system ▪  Be wary of reusing a classic app. virtualization stack ▪  Choose "built-on”, not “connects-to" Hadoop vendors ▪  Be wary of lofty claims around machine learning (e.g., IBM Watson) ▪  As Hadoop in an emerging technology, pick innovative rather than legacy vendors
  12. 12. Common Pitfalls in Big Data Implementations" 1. Incomplete plan for scaling up 2. Not architecting for maximum uptime 3. Over-use of immature technologies 4. Excessive/insufficient data governance 5. Wasting data scientists’ time with data preparation
  13. 13. Incomplete Plan for Scaling Up" RDBMS VS. •  Monolithic, RDBMS-based system •  Vertical scaling •  Large upgrade expenditure •  Commodity server-based Hadoop system •  Horizontal scaling •  Incremental expenditure
  14. 14. Incomplete Plan for Scaling Up [2]" ▪ Relatively easy to extrapolate existing data load to future ▪ But, must also factor in: –  Larger time windows of data •  Expanding beyond 3-month time window broke system •  Now can store 18-months, results in more accurate analytics –  More data sources •  Typically, new sources that could not be added before –  More use cases and users •  More divisions want to join system
  15. 15. Not Architecting for Maximum Uptime" Separate user communities and data are isolated, but… greater infrastructure complexity and risk
  16. 16. Not Architecting for Maximum Uptime [2]" ▪ Separate physical clusters for separate “tenants” appears easy ▪ Multiple clusters lead to: – Infrastructural complexity, more risk of error – More points of failure ▪ Instead, leverage software components to help logically separate users/data
  17. 17. Not Architecting for Maximum Uptime [3]" ▪ Global Storage Solutions Company ▪ Deployed file-serving HBase application ▪ Introduce ad-hoc analytics in same cluster ▪ No resource fencing, poor workload mgmt. ▪ Result: Significant downtime
  18. 18. Over-Use of Hadoop Ecosystem Technologies" ▪ Research group at a Fortune 500 ▪ Anxious to deliver the first NoSQL project ▪ Built an overly complex data model ▪ Deployed HBase with no support/expertise ▪ Lack of integration/analytics = limited success
  19. 19. Excessive / Insufficient Data Governance" ▪ Under-Governed –  Users deleting “unused data” after a project –  Incorrectly interpreted as data loss by others –  Result: panic ▪ Over-Governed –  Fortune 500 deployed Hadoop as a shared IT service –  Needed chargebacks based on data volume –  Setup a “walled garden” for each project –  Result: no sharing, no collaboration, fewer insights
  20. 20. Wasting Data Scientists’ Time with Data Prep" ▪ DS groups are often the first tenants on Hadoop ▪ Traditional DS tools are weak in data prep ▪ Hadoop tools like Pig unfamiliar to DS users ▪ Result: 80% of time spent on data wrangling
  21. 21. Demo …"
  22. 22. Datameer: Purpose-Built for Hadoop"
  23. 23. The #1 Data Discovery Platform" Source: GigaOM, 03/14
  24. 24. MapR Distribution for Hadoop" BIG DATA BEST PRODUCT BUSINESS IMPACT Hadoop Top Ranked Production Success Look for our follow-up blog post at:
  25. 25. The Power of the Open Source Community"Management MapR Data Platform APACHE HADOOP AND OSS ECOSYSTEM Security YARN Pig Cascading Spark Batch Spark Streaming Storm* Streaming HBase Solr NoSQL & Search Juju Provisioning & coordination Savannah* Mahout MLLib ML, Graph GraphX MapReduce v1 & v2 EXECUTION ENGINES DATA GOVERNANCE AND OPERATIONS Workflow & Data Governance Tez* Accumulo* Hive Impala Shark Drill* SQL Sentry* Oozie ZooKeeperSqoop Knox* WhirrFalcon*Flume Data Integration & Access HttpFS Hue *  Cer&fica&on/support  planned  for  2014  
  26. 26. Projects to Follow" ▪ Apache Spark – fast, large-scale data processing engine – MapR is only distribution for Hadoop to support the entire Spark stack ▪ Apache Drill – fast query execution engine – MapR-initiated open source project – Supports instant
 querying and broad
 data format support
  27. 27. For more information" " " " @datameer " @MapR " " Learn more Contact #datameer @datameer