2014 Astera Software. Convergence of Data and Application Integration

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1 Convergence of Data and Application Integration

2 Headquartered in Simi Valley, CA Vision: to deliver intuitive, fast, and seamless data integration across a broad spectrum of platforms Mission: to be a leader in complex data integration by delivering a unified, code-free platform

3 Over 150 customers in diverse vertical industries, including financial services, energy, government, healthcare, and more

4 Data integration that can be learned quickly and used effectively by business professionals Minimize dependence on IT department Empowers business users with input on how data is used One integration solution for multiple data integration needs Intuitive drag-and-drop, code-free, visual environment Best-in-class hierarchical data processing

5 Astera helps companies put data to work more quickly Puts data integration into the hands of data professionals Visibility into most complex data Eliminates hand coding; replaces with drag-and-drop user interface Automates manual and error-prone processes Example: Wells Fargo Bank Situation: losing loan partners due to 3-5 week onboarding process After Centerprise: Onboarding in days

6 Centerprise has served our company significant programming time and resources. With our former tool we would have had to build complicated SSIS packages to transform huge amounts of disparate incoming data. -Raymond James Financial Services Centerprise met all our criteria for high development productivity, excellent performance, workflow design, and process automation -Vanderbilt University Centerprise ETL suits our needs perfectly. It has all the functionality we need, it is easy to use, keeps the data together so it can be centrally managed, and it is the most cost effective. -City of Lodi, CA Overall, the project met and surpassed all of its goals, including major productivity increases, considerably shorter lead time to integrate new business partners, and improved data quality. -Wells Fargo Home Loans

7 A Unified Platform for Multiple Integration Needs

8 Batch data integration Real-time application integration Data services

9 Databases All major OLTP and DW databases Flat files Fixed, delimited, name-value, hybrid Hierarchical Data XML, JSON, EDI, COBOL, Custom Visual data extraction Text, PDF, Excel, PRN Cloud applications SFDC, MS Dynamics CRM, QuickBooks Services REST and SOAP services ERP coming soon

10 Standard ETL transformations Sort, Join, Normalize, Denormalize, Route, Filter, Lookup, Aggregate, Distinct, ApplyToAll, Union, Hierarchical transformations Tree Join, FLWOR, scoped transformations Parsers and serializers EDI, XML, delimited, fixed 200+ built-in conversion functions Quality and profiling Data quality rules, data profiler, record level log, error as data Name and address parsing, cleansing, deduplication

11 High-performance parallel processing engine Server cluster for scalability and failover Job monitoring and management Job orchestration

12 Event-driven integration Web service call Message queue File drop Database changes Timer and scheduler Custom Transaction management Distributed transaction management Detailed transaction monitoring and history Reprocessing error transactions

13 Code-free development of data services Publish dataflows as services Support for complex hierarchical structures Integrated transaction management RESTful interface XML, JSON, text, Raw bytes OAuth1, OAuth2, and Custom authentication

14 REST Input GET Command Database XML Builder REST Output

15 Distributed request-processing cluster Built-in parallel-request processing Built-in caching Server clustering for scalability and failover

16 Visual, code-free development, deployment, and management environment Intuitive drag-and-drop interface Single-click preview for in-place testing Unlimited undo/redo Cut, copy, paste Numerous shortcuts Version control and collaboration features

17 Support for multiple integration scenarios from a single platform Superior usability for business users and developers Complex hierarchical data transformations Structured, semi-structured, unstructured, and hybrid data