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Modernize your Infrastructure and Mobilize Your Data

  1. Fern Halper Ph.D. VP and Senior Director, TDWI Advanced Analytics Modernize Your Infrastructure and Mobilize Your Data
  3. FERN HALPER VP, Senior Research Director for Advanced Analytics TDWI
  4. DATA TRENDS WE SEE AT TDWI Copyright © 2021 TDWI
  5. A Complex Data Environment Modernization is critical Volume and types of data are increasing Data Volume > 50% Already manage 10s of TB of data, Of this,10% manage PBs Modernize platforms > 45% State that they need to expand their data warehouse strategy Copyright © 2021 TDWI
  6. More often, organizations are collecting and analyzing this data (Copyright TDWI, 2021) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Video data Audio data Image still data Clickstream data Machine generated data (e.g., from sensors,… Real-time event streams External text data Geospatial data Semi-structured data Internal text data Time series data Demographic data Log data Transactional data Structured data What kind of data is your organization currently managing? Looking to manage in the next year? Manage now Manage in next year
  7. Data resides in numerous systems (% data by system type) 0% 5% 10% 15% 20% 25% 30% 35% 40% RDBMS (e.g., SQL server, Oracle,… SaaS applications (e.g., Salesforce,… NoSQL database Graph database Time series database Non-SaaS applications 1-25% 0% 5% 10% 15% 20% 25% 30% 35% 40% RDBMS (e.g., SQL server, Oracle, etc.) SaaS applications (e.g., Salesforce, Workday) NoSQL database Graph database Time series database Non-SaaS applications 26-50% 0% 5% 10% 15% 20% 25% 30% 35% 40% RDBMS (e.g., SQL server, Oracle,… SaaS applications (e.g., Salesforce,… NoSQL database Graph database Time series database Non-SaaS applications >50% Copyright © 2021 TDWI
  8. Organizations want to perform analytics using data from multiple sources • Enriched data for customer behavioral analysis • Sensor data and other internal data for proactive maintenance • Internal and external data for risk analysis. Median number of data sources: 11-25 Copyright © 2021 TDWI
  9. 0% 10% 20% 30% 40% 50% 60% 70% Tools for analytics on premises Data warehouse on premises Tools for data integration on premises Tools for analytics in the cloud Tools for data science on premises Data warehouse in the cloud Data lake in the cloud Tools for data integration in the cloud Tools for data science in the cloud Data lake on premises Other In your analytics data ecosystem today, which of the following are in production? Organizations are evolving their DM strategies to the cloud Converged platforms too Copyright © 2021 TDWI
  10. Why unify the DW and DL? • “[A unified DW/DL] provides more options for managing an increasingly diverse range of data structures, end user types, and business use cases.” Corporate IT professional, healthcare • “Modern data is both counting/reporting and using data as an input into predictive models. The structure and rigor necessary for full DW may not be the best format for a model needing real-world data in low latency; a data lake can meet that need. An architecture allowing both would be a good thing.” Corporate IT professional, software/internet • “We can tackle more use cases with a unified architecture that were either difficult or not possible on DW or DL individually.” Consulting/Professional service (Source: Q2 2021 TDWI Best Practices Report on the Unified DW/DL)
  11. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Other We don't need a data lake at this time Not enough information on how to get started Lack of data privacy compliance Our inadequate skills for big data Poor quality of big data Interoperability with existing systems or tools We already have a data lake complementing… Risk of exposing sensitive data (e.g.,… Immaturity of the data lake concept Lack of compelling business case Lack of data integration tools and skills Lack of business sponsorship Our inadequate skills for designing big data… Our inadequate skills for data lake design Lack of data governance In your organization, what are the most likely barriers to implementing a data lake that complements and integrates with an existing data warehouse? There are challenges, too Copyright © 2021 TDWI
  12. 0% 10% 20% 30% 40% 50% 60% 70% Other In-memory functions Event processing tools Microservices for data Data prep tools Orchestration and workflow management tools Interface and API management Self-service for data access and exploration tools Business glossary Data pipelining tools Data dictionary Data lineage tools Data quality tools ETL tools Data catalog Assuming the coexistence of a data warehouse and a data lake in an analytics ecosystem, which of the following tool types can help to unify the two by enabling integration, interoperability, and cross-platform processes? Organizations looking to various tool types to help to mobilize data Copyright © 2021 TDWI
  13. Summary • Organizations are collecting newer data types for analytics • As part of this, they need to evolve their architectures. As organizations evolve their architectures, they are moving to the cloud. Some are trying to better architect and unify their environments • Of course, organizational data resides in a variety of sources, including legacy systems and SaaS applications. • This can impact on processes like data integration, data governance, etc. • Mobilization involves access, understanding, trust, and movement/update • Organizations are looking for tools to help to mobilize their data
  14. THANK YOU!
  15. TARIK DWIEK Head of Technology Alliances Snowflake
  16. © 2021 Snowflake Inc. All Rights Reserved MODERNIZE YOUR INFRASTRUCTURE AND MOBILIZE YOUR DATA - SNOWFLAKE OVERVIEW Tarik Dwiek - Head of Technology Alliances
  17. © 2021 Snowflake Inc. All Rights Reserved DATA SILOS PREVENT VALUE REALIZATION Finance Web/ Log Data Product Usage Agencies OLTP Databases IoT Enterprise Applications Sales Suppliers Third-Party 87% of firms are expanding their ability to source external data1 1 “The Insights Professional's Guide To External Data Sourcing” Forrester, 2020
  18. © 2021 Snowflake Inc. All Rights Reserved THE DATA CLOUD IS A GLOBAL NETWORK 18 One global, unified system connecting companies and data providers to the most relevant data for their business
  20. © 2021 Snowflake Inc. All Rights Reserved 20 PROVEN BY THOUSANDS OF CUSTOMERS
  21. © 2021 Snowflake Inc. All Rights Reserved THE DATA CLOUD TODAY A software company shares terabytes of data with hundreds of customers COVID-19 data is available live on Snowflake Data Marketplace from a US State, and other organizations Today’s financial data is accessible immediately without data pipelines Thousands of companies share data with suppliers, partners, or other business units * Visualization based on actual Data Cloud sharing activity as of July 31 2021 THOUSANDS OF ORGANIZATIONS ARE SHARING DATA WITH THEIR ECOSYSTEM
  22. © 2021 Snowflake Inc. All Rights Reserved PLATFORM ELEMENTS OF THE DATA CLOUD + CONTENT Applications Customer Data 3rd Party Data Data Services SaaS Data Partner Data
  23. © 2021 Snowflake Inc. All Rights Reserved PLATFORM REQUIREMENTS 23 FAST FOR ANY WORKLOAD IT JUST WORKS CONNECTED TO WHAT MATTERS Run any number or type of job across all users and data volumes quickly and reliably. Replace manual with automated to operate at scale, optimize costs, and minimize downtime. Extend access and collaboration across teams, workloads, clouds, and data, seamlessly and securely.
  24. © 2021 Snowflake Inc. All Rights Reserved SNOWFLAKE PLATFORM Under the hood
  25. © 2021 Snowflake Inc. All Rights Reserved ELASTIC PERFORMANCE ENGINE Data science ETL BI/Visualization Dev/QA One engine for every workload Simplify your architecture. Power complex pipelines, analytics, data science, interactive applications, and more. Leading performance and concurrency Fast, reliable performance every time with no tuning or contention. Instantly and cost- efficiently scale to any amount of users, jobs, or data. Support any user or skillset Get the accessibility of SQL, with the flexibility to support Java, Scala, Python, and more. Run external tools directly for extended capabilities.
  26. © 2021 Snowflake Inc. All Rights Reserved MAINTENANCE & TUNING Automated and fully managed for you Focus on what matters. Fully managed with automations that encrypt data, control access, and eliminate manual maintenance and troubleshooting. High availability, high reliability Automate complex replication and failover cross-clouds and cross-regions. Stay up-and- running no matter what happens. Optimized costs for all data Usage-based model paired with patented compression and fine-grained controls to right- size costs. Continual improvements for new efficiencies. INTELLIGENT INFRASTRUCTURE Snowflake Managed MULTI-CLUSTER COMPUTE RESOURCES ADMINISTRATION NETWORKING & ENCRYPTION DATA MANAGEMENT CENTRALIZED STORAGE
  27. © 2021 Snowflake Inc. All Rights Reserved SNOWGRID AWS GCP Azure Snowflake Regions Maintain global business continuity Eliminate disruptions, deliver better experiences, and comply with changing regulations through unique cross- cloud, cross-region connectivity. Share data with no ETL or silos Remove the barriers to data, regardless of cloud, region, workload, or organizational domains. Get instant access and distribution through a single copy of data. Cross-cloud governance controls Simplify governance at scale with flexible policies that follow the data for consistent enforcement across users and workloads. Tap into the extended ecosystem Enrich insights with a network of third-party data. Discover and run new functions for extended workflows.
  28. © 2021 Snowflake Inc. All Rights Reserved 28 Traditional Methods Copy and move data Data is delayed Costly to manage and maintain Unsecure, once data is moved Error prone; pipelines break SNOWGRID UNLOCKS DATA SHARING Snowflake FTP | APIs | ETL | Cloud buckets Secure Data Sharing Single copy of live data, no delays No costs of moving, copying, ingestion No more data lake silos Privacy compliant Governed, revocable access
  29. © 2021 Snowflake Inc. All Rights Reserved SHARE AND COLLABORATE IN THE DATA CLOUD DISCOVER AND BE DISCOVERED IN THE DATA CLOUD SHARE ACROSS YOUR BUSINESS ECOSYSTEM Access data and services from 150+ providers SNOWFLAKE DATA MARKETPLACE Market and deliver your products to customers DIRECT SHARE Share with other Snowflake customers YOUR EXCHANGE DATA EXCHANGE Administer group sharing and data discovery across business units READER ACCOUNTS Share with companies not yet on Snowflake YOUR ACCOUNT
  30. © 2021 Snowflake Inc. All Rights Reserved CONNECT TO THE MOST RELEVANT CONTENT Discover and be discovered with data and services from 150+ providers across 16+ categories. Thousands of companies share data with suppliers, partners, or other business units. Hundreds of applications that businesses rely on run in the Data Cloud. SNOWFLAKE DATA MARKETPLACE SNOWFLAKE CUSTOMERS POWERED BY SNOWFLAKE APPLICATIONS Partner Data Applications Data Services Customer Data 3rd Party Data SaaS Data
  31. DATA CLOUD GROWTH Oct 2021 April 2020
  32. © 2021 Snowflake Inc. All Rights Reserved SNOWFLAKE CUSTOMERS SEE SIGNIFICANT BENEFITS 32 84% 96% 95% of customers surveyed decreased administration effort through use of Snowflake of customers surveyed able to better manage organizational risk and decrease cost of service of customers surveyed achieved more of a competitive advantage RISK COST GROWTH
  33. © 2021 Snowflake Inc. All Rights Reserved BUSINESS IMPACT OF SNOWFLAKE 33 The Total Economic Impact of Snowflake’s Cloud Data Platform, a commissioned study conducted by Forrester Consulting on behalf of Snowflake Return on Investment over 3 years 612% 50% 75% Faster Time to Roll Out the Business Product Reduction in Effort for the IT Support Team
  34. © 2021 Snowflake Inc. All Rights Reserved THANK YOU
  35. ASHWIN RAMACHANDRAN Senior Director of Product Management, Data Integration Precisely
  36. 36 +
  37. Legacy sources cannot be left behind of executives say their customer- facing applications are completely or very reliant on mainframe and IBM i processing. Forrester Consulting, 2019 55% Your traditional systems – including mainframes, IBM i servers & data warehouses – adapt and deliver increasing value with each new technology wave 72% increase in transaction volume on mainframe environments in 2019 BMC 2019 $1.65trillio n invested by enterprise IT to support data warehouse & analytics workloads over the past decade Wikibon “10-Year Worldwide Enterprise IT Spending 2008-2017”
  38. What happens when legacy data is unlocked? Enhanced BI and analytics Improved data discovery Data democratization with governance Critical data available for next- gen projects – AI and ML
  39. Connecting mainframe and IBM i to Snowflake Bring rich transaction data to the cloud Improve cloud analytics and insights Speed delivery of information Scale with next-generation initiatives
  40. Connect and Snowflake IBM i Traditional ETL sources, files, RDMBS, etc. Convert mainframe, IBM i and data from other sources to be shared anywhere on Snowflake BI and Analytics Tools Deploy Connect capabilities on-prem, in cloud or hybrid environments Mainframe
  41. Customer Story • Connect leverages IBM i journals to identify inserts, updates, and deletes across over 1000 tables, replicating those to Snowflake in near-real-time. • Installation and proof of concept configuration was complete in 2 weeks, with IT able to demonstrate value to the business quickly. • Sales now has greater visibility into the operations of subscribers, seeing data that is fresher than the old ETL processes could provide. • Core business operations continue to run on the IBM i while strategic modernization initiatives can push forward on Snowflake. About New Zealand broadcasting company that offers satellite pay TV with 70+ channels, sports and entertainment streaming services, and broadband internet service. Sky NZ has more than 990,000 customers and 990 employees, and was the first to bring an all-digital and high-definition experience to New Zealanders Problem Ability to derive business insights was hampered by data silos. Billing, subscriber management, financial management, and chart of accounts all run on core IBM i platforms. Existing bespoke ETL processes were slow to run and painful to maintain. Sky needed to move faster, requiring data be delivered in Snowflake in a near-real-time fashion. Solution Precisely Connect Snowflake
  43. CONTACT INFORMATION If you have further questions or comments: Fern Halper, TDWI Tarik Dwiek @fhalper Ashwin Ramachandran
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