Instantly convert Teradata ETL and EDW to Spark- Impetus webinar
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Teradata has been a popular choice for analytical data processing. As data complexity increases, more and more enterprises need the flexibility and scalability advantages of ‘modern data architecture’ technologies. The sophisticated algorithmic and statistical analytics, and interoperability, would also enable businesses to handle vast and complex data at high speeds. But the challenges and risks involved in an enterprise data warehouse transformation are well known. Impetus Workload Transformation solution is the only 100% cloud ready solution to assess, migrate, and validate your complex data transformation and analytical workloads from Teradata to modern open systems such as Spark, Hive or Scala. Find out how your enterprise can ensure a swift move to big data / cloud using an automation based EDW transformation solution. Join our upcoming webinar where our modern data architecture experts will talk about: Using automation to identify the candidate workloads to migrate Building a prioritized EDW conversion roadmap based on cost, performance, and business objectives Devising an effective offload strategy to a scalable data architecture Rapidly transforming complex business logic and code (with UDFs) using automation. To view the webinar, visit - https://bit.ly/347S19N
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Instantly convert Teradata ETL and EDW to Spark- Impetus webinar
- 1. ©2019 Impetus Technologies, Inc. You are prohibited from making a copy or modification of, or from redistributing, rebroadcasting, or re-encoding of this content without the prior written consent of Impetus. This presentation may include images from other products and services. These images are used for illustrative purposes only. Unless explicitly stated there is no implied endorsement or sponsorship of these products by Impetus. All copyrights and trademarks are property of their respective owners.
- 2. Instantly Convert Teradata ETL and EDW to Spark On Cloud or On Premise Hari Kodakalla Director Big Data Strategy Barry Tuthill VP Field Operations
- 3. Agenda The case for modernization Where to get started Our solution A case study Demo Q&A
- 4. Our Mission Enabling a unified, clear and present view of your business.
- 5. Poll
- 6. Our speakers Hari Kodakalla Director Big Data Strategy Barry Tuthill VP Field Operations
- 7. The case for modernization
- 8. The case for modernization Business readiness
- 9. The case for modernization Business readiness Technology readiness
- 10. The case for modernization Business readiness Technology readiness Strategic readiness
- 11. The call for a modern architecture that can provide a Unified Single Source of Truth.
- 12. Key features of a modern data architecture
- 13. Key features of a modern data architecture Flexible
- 14. Key features of a modern data architecture Cost efficient
- 15. Key features of a modern data architecture Versatile
- 16. Key features of a modern data architecture Secure
- 17. The journey from Teradata to a modern data infrastructure
- 18. Key action items
- 19. Key action items Select platform components
- 20. Key action items Select platform components Address cultural requirements
- 21. Key action items Select platform components Address cultural requirements Minimize risks
- 22. The first stumbling blocks
- 23. The first stumbling blocks Where do you start?
- 24. The first stumbling blocks Where do you start? Who will do all this transformation?
- 26. Potential sources of risk
- 27. Potential sources of risk Overwhelming
- 28. Potential sources of risk Overwhelming Too complex
- 29. Potential sources of risk Overwhelming Too complex Time consuming
- 30. Potential sources of risk Overwhelming Too complex Time consuming Limited expertise
- 31. These could all lead to strategy failures and serious disruptions.
- 32. Poll Results
- 34. Achieving the right balance Migrating as is Total re-engineeringTransfer and transform ? ??
- 35. How can we help?
- 36. We can help move your Teradata warehouse to where you want to be, on cloud, on premise, or hybrid. Automagically.
- 37. We bring agility with automation 1 2 3 4 Assess Transform Validate Execute
- 38. Automated vs. manual Impetus Automated Workload Transformation Manual Migration Week Assess Assess 0 4 8 12 16 20 24 28 32 36 … 48 … 75 Transform Validate Execute Migrate Validate Debug & Revalidate Execute
- 39. A transformation engine that learns SQL, PLSQL Spark SQL Stored Procedures, BTEQ Teradata ETL Hive QL Spark Scala, Java, Shell, Python, NiFi/Sqoop,… Workload Transformation Engine
- 40. The case of a US-based Fortune Global 20 Telco…
- 41. What were their challenges? Data volumes Increasing costs Complex code
- 42. What did they accomplish? Transformed 20,000 production queries automatically to Spark SQL Migrated historical and incremental data with SCD Optimized query, script, and architecture for performance gains
- 43. What did they accomplish? Teradata dependency diminished significantly Process ETL rapidly on top of the data lake Handle data growth of 100–200% per year Enable massive new analytics capabilities
- 44. Demo
- 45. Q&A
- 46. Thank you Visit us at www.impetus.com/transform
Editor's Notes
- The case for modernization is driven from 3 distinct perspectives of readiness and the state that each organization sees itself in Business Readiness What are the key business needs from an Analytics perspective, are they being met . Are the goals well defined Does the current state adequately addressing the needs both current and future Are you at risk of not being able to support emerging near term and long term needs Technology Readiness Is there a aging technology risk for the prevailing tech stack of choice Are there any points of integrations difficult to maintain for a well integrated ecosystem / platform Are performance needs being met for current users Does the current platform capable of satisfying anticipated needs of analytical demand Strategic Readiness How is the balance between the platform strategy , tech adoption and day-2 day operations risk Can the current strategy whether anticipated demands without disruption
- The case for modernization is driven from 3 distinct perspectives of readiness and the state that each organization sees itself in Business Readiness What are the key business needs from an Analytics perspective, are they being met . Are the goals well defined Does the current state adequately addressing the needs both current and future Are you at risk of not being able to support emerging near term and long term needs Technology Readiness Is there a aging technology risk for the prevailing tech stack of choice Are there any points of integrations difficult to maintain for a well integrated ecosystem / platform Are performance needs being met for current users Does the current platform capable of satisfying anticipated needs of analytical demand Strategic Readiness How is the balance between the platform strategy , tech adoption and day-2 day operations risk Can the current strategy whether anticipated demands without disruption
- The case for modernization is driven from 3 distinct perspectives of readiness and the state that each organization sees itself in Business Readiness What are the key business needs from an Analytics perspective, are they being met . Are the goals well defined Does the current state adequately addressing the needs both current and future Are you at risk of not being able to support emerging near term and long term needs Technology Readiness Is there a aging technology risk for the prevailing tech stack of choice Are there any points of integrations difficult to maintain for a well integrated ecosystem / platform Are performance needs being met for current users Does the current platform capable of satisfying anticipated needs of analytical demand Strategic Readiness How is the balance between the platform strategy , tech adoption and day-2 day operations risk Can the current strategy whether anticipated demands without disruption
- The case for modernization is driven from 3 distinct perspectives of readiness and the state that each organization sees itself in Business Readiness What are the key business needs from an Analytics perspective, are they being met . Are the goals well defined Does the current state adequately addressing the needs both current and future Are you at risk of not being able to support emerging near term and long term needs Technology Readiness Is there a aging technology risk for the prevailing tech stack of choice Are there any points of integrations difficult to maintain for a well integrated ecosystem / platform Are performance needs being met for current users Does the current platform capable of satisfying anticipated needs of analytical demand Strategic Readiness How is the balance between the platform strategy , tech adoption and day-2 day operations risk Can the current strategy whether anticipated demands without disruption
- As one examines the answers to some of the key questions raised from the 3 dimensions of readiness enterprises start quickly realizing the imminent risks and expose the need for a modern architecture to mitigate the challenges . Which in turn triggers a flurry of activities across the organizations that potentially could span over a couple years to engage in this transformational journey that we are here to talk about This journey of transformation and innovation that demands the availability of a single source of truth and a modern data platform capable of breaking down barriers and impediments in the organizations way to realize the sophistication they demand and excel in their sphere of business We can see that in the industry more today then ever before , Where Gartner has quoted that more than 80% of enterprises having launched modernization initiatives. All of these initiatives have a single goal in mind---to create the data driven enterprise of the future based on a single source of truth. What are “must haves” for such an architecture? The key characteristics and drivers of such an architecture are centered around: There are a number of fundamental capabilities that need to be present. (Could mention a few without elaborating much on any one). {PICK A FEW: Flexibility – In accommodating new forms of use cases with the changing business use cases, elastic architecture that adapts to changing data processing requirements Efficiency –Responding in real-time is critical to the customer experience Customer -centricity Automated Resilient Cost - Cost becomes an important yard stick in choosing the Advanced Analytics – This is needed to derive the insights on which business can take decisions Strategic value – This new architecture should be in line with the enterprise data strategy to be ‘cloud ready’ Technological value – It should have the relevant technology proposition to take the enterprise to the next level} Independence from vendor lock-in for agile innovation – Not getting locked in vendor’s platform is very important. Secure But one thing is clear. We MUST have access to all of the enterprise data. And for Teradata users, much of that is held inside the Teradata warehouse. How can we swiftly re-commission this data onto the modern data architecture? Impetus has been constantly working with customers and partners alike to help them identify the various components that enable this modern architecture, in fact Dhiren can you provide some insights into what you are also observing as a Product Manager as you bring these capabilities to our specific offerings.
- Consider your potential sources of risk: the project can be…
- Consider your potential sources of risk: the project can be…
- Consider your potential sources of risk: the project can be…
- Consider your potential sources of risk: the project can be…
- Consider your potential sources of risk: the project can be…
- This is what was accomplished…
- …facilitating this future. Teradata dependency diminished significantly -> Saving millions of dollars on recurring licensing cost