1. Pentaho 7.1

2. Data Discovery / Analysis
Current State Today
Data Engineering
Data Prep
Analytics
Ingestion
Processing
Blending
Data Delivery
Data Discovery / Analysis
Analysis & Dashboards
Administration
Security
Lifecycle Management
Data Provenance
Dynamic Data Pipeline
Monitoring
Automation

3. Future Vision: A Single Flow
Data Engineering
Data Prep
Analytics
Ingestion
Processing
Blending
Data Delivery
Data Discovery / Analysis
Analysis & Dashboards
Administration
Security
Lifecycle Management
Data Provenance
Dynamic Data Pipeline
Monitoring
Automation
Looking to build out a single platform where data engineers, data analysts, business analysts and data scientists can enter anywhere and make data useful for the business

4. Industry Challenges: An Evolving Big Data Landscape
Rapidly evolving Big Data technologies and landscape
Disjointed Tools
Growth in volumes and varieties of data
Pentaho 7.1
Those working in data are working in a turbulent, rapidly changing data world. They are facing the challenges of:
Growth of volumes and variety of data
Rapidly evolving big data technologies and landscape
Disjointed tools
7.1 is built to ease these challenges

5. Analyze data anywhere in the data pipeline
Recap: Pentaho 7.0
Analyze data anywhere in the data pipeline
Data Prep
Data Engineering
Analytics
Ingestion
Processing
Blending
Data Delivery
Data Discovery / Analysis
Analysis & Dashboards
Administration
Security
Lifecycle Management
Data Provenance
Dynamic Data Pipeline
Monitoring
Automation
Bridging the gap between data preparation and analytics with a visual data experience from anywhere in the data pipeline:
Bringing analytics into data prep
Share analytics during data prep
And governance, security, and big data ecosystem support for a blended data world
New Spark capabilities
Enhanced metadata Injection
Hadoop security
Simplified configuration, deployment, and administration

6. Introducing Pentaho 7.1
Adaptive execution and improved visualizations make users more productive and improves big data job performance across the entire data pipeline.
Data Prep
Data Engineering
Analytics
Ingestion
Processing
Blending
Data Delivery
Data Discovery / Analysis
Analysis & Dashboards
Administration
Security
Lifecycle Management
Data Provenance
Dynamic Data Pipeline
Monitoring
Automation
Increased productivity and job performance for big data
Adaptive execution on any engine, starting with Spark
Increased cloud support with HDInsight
More enterprise-level security for Hortonworks
Kerberos impersonation support
Ranger support
Improved analytics at every stage of the data pipeline
Visual Data Exploration enhancements
Support for third party visualizations

7. CHALLENGE: Productivity and Job Performance for Big Data Jobs

8. Increased User Productivity and Job Performance for Big Data Jobs
Adaptive Execution on any engine, starting with Spark
Adaptive Execution for Spark
Increased cloud support with HDInsight

9. Adaptive Execution on Any Engine, Starting with Spark
Build Once, Execute on Any Engine
Challenge: With rapidly changing big data technology, coding on various engines can be time-consuming or impossible with existing resources
Solution: Future-proof data integration and analytics development in a drag-and-drop visual development environment, eliminating the need for specialized coding and API knowledge. Seamlessly switch between execution engines to fit data volume and transformation complexity
Challenge: With rapidly changing big data technology, coding on various engines can be time-consuming or impossible
Enables anyone, not just a Java developer, to work with processing engines such as Spark
Build once, Execute on Any Engine. Can easily switch between execution engines, without rewriting transformation logic, based on data complexity
Allows data teams to future-proof data integration and analytics development in a drag and drop visual development environment, eliminates the need for specialized coding and API knowledge and enables seamless switching of execution engines to fit data volume and transformation complexity.
While the competition, such as Talend, support multiple execution engines, the number of transformation steps that they support decreases when they transition from their native engine to MapReduce and then further reduces when they transition to Spark. Pentaho on the other hand provides coverage of virtually all available steps across execution engines.

10. Adaptive Execution for Spark
Process Big Data Faster on Spark Without Manual Coding
Challenge: Finding the talent and time to work with Spark and newer big data technologies
Solution: More easily develop Spark applications in PDI using adaptive execution to ingest, process and blend data from a range of big data sources and scale on Spark clusters
Challenge: Finding the talent and time to work with Spark and newer big data technologies
Develop Spark applications in PDI’s drag-and-drop visual development environment. Enables more people to be more productive Spark applications.
Pentaho is the only vendor to support Spark with all data integration steps in a visual drag-and-drop environment.

11. Increased Cloud Support with HDInsight
Store and Process Big Data in the Cloud with HDInsight
Challenge: Big Data storage and processing options for deploying in the cloud, on premise, or hybrid
Solution: Customers who already use Microsoft Azure HDInsight will be able to seamlessly use Pentaho's capabilities, allowing more options in cloud, on-premise, or hybrid deployments
Challenge: Big Data storage and processing options for deploying in the cloud, on premise, or hybrid
More options to store – and more importantly, process – big data in hybrid, on-premises, and public cloud environments.
Now, potential customers who already use Microsoft Azure HDInsight will be able to use Pentaho's capabilities with support for Microsoft Azure HDInsight, Azure SQL and Azure SQL Server

12. CHALLENGE: Enterprise-Level Security for Hadoop Deployments

13. Additional Enterprise-Level Security for Hortonworks
Kerberos Impersonation Support
Ranger Support

14. Increased Kerberos Impersonation Support
Increase Security with Hortonworks Deployments
Challenge: Authentication security vulnerabilities with Hortonworks deployments
Solution: Reduced risk, more secure multi-user Hadoop data integration, better big data governance, and cluster protection from intruders
Challenge: Authentication security vulnerabilities with Hortonworks deployments
With increased Kerberos impersonation support - reduce risk, provide more secure multi-user Hadoop data integration, better big data governance, and cluster protection from intruders

15. Manage Role Based Permissions on Hortonworks
Ranger Support
Manage Role Based Permissions on Hortonworks
Challenge: Governance and risk with authorization on Hortonworks deployments.
Solution: Enterprise-grade compatibility with Ranger for authorization and role-based access to specific data sets on Hortonworks, ensuring business access rules are enforced across Hadoop data and components
Challenge: Security and role based permissions on Hortonworks
In 7.1, enterprise-grade compatibility with Ranger for authorization and role based access to specific data sets on Hortonworks. Ensures business access rules are enforced across Hadoop data and components, promoting governance, protecting resources, and reducing risk.

16. CHALLENGE: Multiple Tools and Siloed Processes in Data Prep

17. Improved Analytics at Every Stage of the Data Pipeline
Visual Data Exploration Enhancements
Integration with Third Party Visualizations

18. Recap: Visual Data Exploration
Access visualizations during data prep for inspection or prototyping
Challenge: Inability to view visualizations without switching in and out of tools
Solution: Visual Data Exploration provides access to analytics during data preparation so users can easily spot check data issues on the spot, without switching in and out of tools or waiting until the very end to find data quality problems In addition, IT and the business can collaborate and iterate faster, shortening the cycle from raw data to meaningful analytics.
Challenge: Inability to view visualizations without switching in and out of tools
Visual Data Exploration provides access to analytics during data preparation so users can easily spot check data issues on the spot, without switching in and out of tools or waiting until the very end to find data quality problems In addition, IT and the business can collaborate and iterate faster, shortening the cycle from raw data to meaningful analytics.

19. Visual Data Exploration Enhancements
Drill-Down Exploration
Can click to drill into various hierarchies
Access visualizations during data prep for data inspection or prototyping
Visual Data Exploration Enhancement: Ability to further drill-down into visualizations within Visual Data Explorer x
Visual Data Exploration Enhancement: Ability to further drill-down into visualizations within Visual Data Explorer

20. Visual Data Exploration Enhancements
New Visualizations: Heat Grid, Geo Map, Sunburst
Access visualizations during data prep for data inspection or prototyping
Visual Data Exploration Enhancement: New visualizations for expanded prototyping – heat grid, geo map, sunburst
New visualizations for expanded prototyping – heat grid, geo map, sunburst
Heat Grid: shows 2 dimensions and 2 measures at once. Most useful for relative comparisons at the ‘intersection’ of 2 dimensions. Ex: See sales metrics by each combination of month and region (as shown)
Sunburst: useful for showing how a measure is distributed across several categories / attributes. Esp. useful for showing multiple levels in hierarchy at once. Ex: breakdown of sales by state (inner slice), and city (outer slice)
Geo map: measures represented by dot size/color. Pan, zoom actions. User can now explicitly define lat and long fields when creating a location attribute for a model in annotate stream, with results showing in DE and Analyzer

21. Integration with 3rd Party Visualizations
More Easily Integrate 3rd Party Visualizations
Challenge: Easily integrating visualizations that are not out of the box
Solution: Integrate visualizations from 3rd party libraries (D3, FusionCharts, Highcharts, etc) with an easier-to-use and more flexible API and documentation
Challenge: Easily integrating visualizations that are not out of the box
Integrate visualizations from 3rd party libraries (D3, FusionCharts, Highcharts, etc) with an easier-to-use and more flexible API and documentation. More robust framework for developers to use, including samples and documentation.
Easier to integrate new visualizations into Pentaho
Better developer APIs
Reusability of visualizations in Data Explorer and Pentaho Analyzer
Better documentation

22. Demonstration

23. Questions?

24. Thank You