Hybrid Data Management

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1 Kelly Schlamb Executive IT Specialist, Worldwide Analytics Platform Enablement and Technical Hybrid Data Management IBM Analytics Summit 2017 November 8, 2017

2 5 Essential Elements of Data and Analytics Data Science & Machine Learning Embrace new ways to develop insights and streamline operations Unified Governance Enable better insight and compliance across all data Hybrid Data Management Unify the approach to data and content on the path to cloud Data Analytics & Visualization Empower all to make data-driven decisions quickly and easily Open Source: Hadoop, Spark & more Commit to openness for speed and innovation 2

3 Market Observations 1. There is increasing pressure to perform analytics where data gets created Point-of-decision HTAP promises to simplify the information infrastructure by removing unnecessary data marts and, potentially, data warehouses. Gartner 2. Event-driven applications will enable new analytic use cases Event-driven real-time digital business is poised to become a priority for mainstream business Gartner In-process HTAP could potentially redefine the way some business processes are executed Gartner 4. Hybrid cloud capabilities of software support economies of scope Public cloud adoption has stalled for the time being, signaling enterprises are moving to the hybridization phase of their IT transformations. TBRI 2H Private cloud needs cloud-scale convenience IDC by end of % of the IT Market spend will be private hosted or private on Prem Cloud with On-Demand Convenience future growth point within private cloud. Skills, timing or cost to effectively procure, assemble, run, manage disperse infrastructure resource require integrated versatile platform offerings with appliance-like simplicity A client 3. Business applications are leveraging both SQL and NoSQL data in structured repositories for analytics Top relational database solutions are now offering a wide range of new features to combine structured and unstructured data types. Database decision-makers need to look at investing in these database technologies. Forrester 6. Diverse data sources support an ecosystem of innovation Established vendors, have continued their cloud-focused innovation around hybrid cloud for both cost and workload optimization. Many have added open-source products to their portfolio usually by acquisition in an attempt to capture a new generation of buyers. 3

4 IBM s Data Management Strategy is Hybrid Its not about Cloud or On-Premises its about Cloud AND On-Premises Its not about Traditional Relational or Open Source its about Traditional Relational AND Open Source It s About Hybrid Its not about SQL or NoSQL its about SQL AND NoSQL Its not about Structured or Unstructured Data its about Structured AND Unstructured Data 4

5 Essential Elements of a Hybrid Data Management Strategy Access your data All sources and all types Flexibility Support all data types, all workloads, all consumption models Gain value from your data, without limits Democratize access Provide data-driven decisions to everyone Analytics for all data Easily query all of your data Intelligent analytics Make better decisions, provide smarter capabilities Cloud journey Support your data regardless of location 5

6 IBM Hybrid Data Management Embedded machine learning and data science Transactional and analytic processing all in once place Common SQL engine for built-in data virtualization Support for onpremises or cloud, NoSQL or SQL Drive more value from your data. Run analytics where the data lives using tools already preferred by professionals. Spark and Jupyter notebooks built-in Integration with model building, BI, and visualization tools Instant insight from realtime operational data for growing revenue, reducing cost and lowering risk. Simplify IT with transactions and reporting (HTAP) within the same system Easy, low-risk upgrade from Oracle Database Anchored by a common SQL engine to enable highly scalable, data management with portable analytics. Application and operational compatibility Provide transparent access to other data sources Offers flexibility in choosing the form factor that best suits your business, enabling a controlled journey to the cloud. A platform that fits your data strategy Bridge data stores for seamless data integration More intelligent analytics and insights Go at the speed of your business Write once, run anywhere, from any source Deploy your data where you need it 6

7 Data Professionals Evolving Roles As data maturity increases, so does the number of data professionals who are hungry to put data to work App Developers Easily plug into data and models to make apps more powerful Data Scientists Streamline algorithm development to deliver insight faster Business Analysts Easily discover and explore data to improve decisions IoT An IBM Business Data Engineers Tame, curate and secure data to make it relevant and accessible. DBaaS DB/DW Public Social 7

8 Market Requires Optimized Offerings for all Workloads Desire to move to hybrid cloud Applications leveraging new types of data Data scientists with new analytic needs Desire to move to hybrid warehouse Need for never down applications 8

9 Common SQL Engine Business Value NEW NEW Event Store Managed Public Cloud Service Hosted Public Cloud Service On-premises Private Cloud On-premises Appliance On-premises Custom Software Hadoop / Spark Environment IBM Db2 Db2 Event Store Db2 [Warehouse] On Cloud Db2 Hosted Db2 Warehouse IBM Integrated Analytics System Db2 Big SQL A Common SQL Engine enabling true HYBRID data store solutions for all workload types Investment Protection Application compatibility: Write once, run anywhere Operational compatibility: Re-use operational and administrative procedures Leverage existing skills: Leverage a common set of development, management and administrative skills Flexible Licensing: Leverage the Data Management Bundle for flexible deployment allocations and shifts 9 Integration: Ability to access data from all data sources, both IBM data stores and non-ibm data stores Ecosystem: One ISV product certification for all platforms test once, certify all Standardized API: Common programming model for systems of engagement, systems of record and systems of insight

10 Common SQL Engine API & Application Support API APPLICATIONS Applications, Tools, Languages NEW NEW Event Store Managed Public Cloud Service Hosted Public Cloud Service On-premises Private Cloud On-premises Appliance On-premises Custom Software Hadoop / Spark Environment IBM Db2 Db2 Event Store Db2 [Warehouse] On Cloud Db2 Hosted Db2 Warehouse IBM Integrated Analytics System Db2 Big SQL 10

11 Common SQL Engine High Value Capabilities NEW NEW Event Store Managed Public Cloud Service Hosted Public Cloud Service On-premises Private Cloud On-premises Appliance On-premises Custom Software Hadoop / Spark Environment IBM Db2 Db2 Event Store Db2 [Warehouse] On Cloud Db2 Hosted Db2 Warehouse IBM Analytics Integrated System Db2 Big SQL A Common SQL Engine enabling true HYBRID data store solutions for all workload types Data Virtualization Full MPP scalability Oracle Compatibility Load and Go deployment Application portability Native Encryption Built-in analytics Hybrid by design Spark Integration SQL and NOSQL Capabilities In-memory & columnar High Concurrency R Language/Studio Integration Advanced Security Workload Flexibility Consistent Management Advanced WLM HA, DR & Replication 11

12 Event-based Use Cases Evolving in the Market Event Applications are starting to Transform and Differentiate Businesses Across Many different Industries Cyber Security Retail and Logistics Banking and Finance Internet of Things Telecommunications Industrial & Manufacturing Operational Efficiency Risk Management Problem Detection Fraud Customer Service Energy Savings Smarter Cities 12

13 Characteristics of Event Driven Applications Velocity of data Up to millions of events per second Supporting IoT or finance / retail workloads Volume of data 100 s GBs to petabytes of events Performance & flexibility of data access High performance Look up access Stream based analytics High performance real time deep insight queries Data sharing with ease Easily share event data with other tools without duplication / replication 13

14 IBM and Hortonworks Partnership IBM and Hortonworks Announce Partnership to Bring Data Science to the World on an Open Platform The partnership, announced June 13 th 2017, showcases Hortonworks recognition of IBM as the leader in data science, and provides Hadoop clients a clear choice for open data science vs. the limited and proprietary offerings from vendors such as Cloudera and MapR. Specifically Announced: Hortonworks adopts IBM Data Science Experience (DSX) as its Data Science Platform. Hortonworks adopts IBM Big SQL as its SQL engine for complex analytics on Hadoop. IBM will leverage Hortonworks HDP as the core Hadoop distribution for Big SQL and DSX. 14

15 IBM and Hortonworks Deliver Data Science at Scale Focus on extending data science and machine learning to analyze the data in Apache Hadoop systems Consumers get the best in class open technology + #1 Rank by Gartner 2017 Data Science Magic Quadrant Leader in SQL technology for Hadoop ( Leader in data and analytics solutions for hybrid cloud Provides Data Science & Machine Learning Leader in Hadoop Open Source Distribution customers and ecosystem partners Hadoop original architects, developers employed by Hortonworks Provides Open Hadoop Data Platform Commitment to progressing advanced analytics through open source 15

16 IBM and Hortonworks - Open Source Commitment and our combined commitment to Open Standards is Unmatched Apache Committers by Top 20 Companies Jan 2, 2017 Based upon company affiliation of 2,200 ASF Committers 16

17 IBM Big Data High Value with Hortonworks 17

18 Apache Spark Spark's core libraries enable analytic processing of data from many sources Powerful DBaaS Apache Spark is an open source, in-memory processing framework Distributed data processing & iterative analysis on massive data volumes Spark's standalone framework goes beyond Hadoop and HDFS Interactive query via SparkSQL: Accelerated predictive analytics processing via SparkR Micro-batched event processing via Spark Streaming Machine learning libraries via MLib Graph processing via GraphX 18

19 IBM Analytics Engine Built on Spark + Hadoop Developing and deploying advanced analytics applications in a more user friendly way! IBM Analytics Engine 19

20 IBM Analytics Engine IBM Analytics Engine is built on open source Apache Hadoop and Apache Spark. It provides users flexibility of open source and an opportunity to expand on their existing open source investments. IBM Analytics Engine helps Data scientists, Data engineers, and Developers to focus on building data models and business solutions while simplifying cluster administration through easy to use interfaces for management and integration. IBM Analytics Engine deploys clusters in minutes with enterpriselevel security, reliability, and powerful integration capabilities for data management, monitoring, and dashboards. 20

21 IBM ECM / Content Services Portfolio Content Navigator Unified experience on web, mobile and desktop FileNet P8 Datacap Datacap Insight Edition Case Foundation Case Manager Watson Explorer Content Manager OnDemand Information Lifecycle Governance On-premise, On Cloud, or Hybrid 21

22 IBM ECM Managed Cloud Offerings FileNet / Content Foundation on Cloud CM OnDemand on Cloud CM8 on Cloud Case Manager on Cloud Datacap on Cloud Enterprise Records Provisioning on IBM Cloud Installation, configuration, admin Back-ups HA/DR Data encryption Replicated storage 24 x 7 support Dev/Test, Production 22

23 Watson Data Platform Intelligent by Design with intelligence services ranging from cognitive APIs to automatic entity analytics infused in every aspect of the making/creation process Lead the way in collaboration so data scientists, developers and data engineers are natively supported in their tasks and are working together to deliver an intelligent application Self-service trusted access to data giving data professionals the freedom of access to the data they need with the trust that the business expects Streaming with real-time analytics to support modern application demands with first class streaming analytics that augments batch Open and Extensible achieving scale and the network effect by attracting partners to build on and extend through APIs and toolkits Be the premier content hub of open, rich 3 rd party content and curated IBM assets that enhances the types of insights users are able to derive 23

24 Watson Data Platform DATA TYPES / SOURCES INGEST PERSIST ANALYZE UI Data Engineer LOGS Streams Elasticsearch etcd RethinkDB Streams Architects how data is organized & ensures operability IBM Bluemix Data Connect Sensors Data PDA Data Scientist Application Data Data Connect Elasticsearch PostGreSQL MySQL Db2 for Cloud Spark Gets deep into the data to draw hidden insights for the business Data Science Experience Batch Data Streaming Data Social Data Weather Data Lift RabbitMQ Redis ScyllaDB Object Storage GraphDB Cloudant Hadoop Db2 Warehouse Watson Machine Learning Data Science Experience Business Analyst Works with data to apply insights to the business strategy Watson Analytics App Developer and more.. IOTP Watson Analytics Plugs into data and models & writes code to build apps Bluemix 24

25 Watson Data Platform Data at your fingertips Store your data in the IBM Cloud Connect to your on-premises and cloud data 25

26 Introducing the IBM DataFirst Method The more you put data to work in your organization, the better the outcome The IBM DataFirst Method is our expertise to ensure your success to get from where your are now, to where they need to be Start Anywhere Focus on your biggest business opportunity Fill the Gaps Strategy. Expertise. Skills. No more and no less Build Value at Every Step Achieve a data-driven culture, one initiative at a time Ensures client success through repeatable and actionable use cases & engagements Provides an entry point and a roadmap to the future for your client s journey 26

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