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Information Builders May 11, 2012 Information Builders (Canada) Inc. WebFOCUS Hyperstage Analyze/Report from large Volumes of Data.

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Presentation on theme: "Information Builders May 11, 2012 Information Builders (Canada) Inc. WebFOCUS Hyperstage Analyze/Report from large Volumes of Data."— Presentation transcript:

1 Information Builders May 11, 2012 Information Builders (Canada) Inc. WebFOCUS Hyperstage Analyze/Report from large Volumes of Data

2 Reporting Query & AnalysisDashboards InformationDelivery PerformanceManagement EnterpriseSearch Visualization & Mapping Data Updating PredictiveAnalytics MS Office & e-Publishing Extended BI Core BI Extensions to the WebFOCUS platform allow you to build more application types at a lower cost Business to Business Data Warehouse & ETL Master Data Management Data Profiling & Data Quality Business Activity Monitoring High Performance Data Store MobileApplications WebFOCUS Higher Adoption & Reuse with Lower TCO

3 High Performance Data Store Reporting Query & AnalysisDashboards InformationDelivery PerformanceManagement EnterpriseSearch Visualization & Mapping Data Updating PredictiveAnalytics MS Office & e-Publishing Extended BI Core BI Extensions to the WebFOCUS platform allow you to build more application types at a lower cost Business to Business Data Warehouse & ETL Master Data Management Data Profiling & Data Quality Business Activity Monitoring MobileApplications WebFOCUS High Performance Data Store

4 The Business Challenge Big Data Copyright 2007, Information Builders. Slide 4

5 Data Storage Time Machine- Generated Data Human-Generated Data Todays Top Data-Management Challenge Big Data and Machine Generated Data

6 Source: KEEPING UP WITH EVER-EXPANDING ENTERPRISE DATA ( Joseph McKendrick Unisphere Research October 2010) How Performance Issues are Typically Addressed – by Pace of Data Growth IT Managers try to mitigate these response times ….. When organizations have long running queries that limit the business, the response is often to spend much more time and money to resolve the problem

7 Classic Approaches and Challenges Data Warehousing Traditional Data Warehousing Labour intensive, heavy indexing, aggregations and partitioning Hardware intensive: massive storage; big servers Expensive and complex More Data, More Data Sources More Kinds of Output Needed by More Users, More Quickly Limited Resources and Budget Real time data Multiple databases External Sources

8 New Demands: Larger transaction volumes driven by the internet Impact of Cloud Computing More -> Faster -> Cheaper Data Warehousing Matures: Near real time updates Integration with master data management Data mining using discrete business transactions Provision of data for business critical applications Early Data Warehouse Characteristics: Integration of internal systems Monthly and weekly loads Heavy use of aggregates Classic Approaches and Challenges Data Warehousing – Growing Demands

9 Classic Approaches and Challenges Dealing with Large Data INDEXES CUBES/OLAP

10 Classic Approaches and Challenges Limitations of Indexes Increased Space requirements Sum of Index Space requirements can exceed the source DB Index Management Increases Load times Building the index Predefines a fixed access path

11 Classic Approaches and Challenges Limitations of OLAP Cube technology has limited scalability Number of dimensions is limited Amount of data is limited Cube technology is difficult to update (add Dimension) Usually requires a complete rebuild Cube builds are typically slow New design results in a new cube

12 Limitations of Rows These Solutions Contribute to Operational Limitations 1.Impediments to business agility wait for DBAs to create indexes or other tuning structures, thereby delaying access to data. Indexes significantly slow data-loading operations and increase the size of the database, sometimes by a factor of 2x. 2.Loss of data and time fidelity: ETL operations typically performed in batch during non-business hours. Delay access to data, often result in mismatches between operational and analytic databases. 3.Limited ad hoc capability: Response times for ad hoc queries increase as the volume of data grows. Unanticipated queries (where DBAs have not tuned the database in advance) can result in unacceptable response times. 4.Unnecessary expenditures: Attempts to improve performance using hardware acceleration and database tuning schemes raise the capital costs of equipment and the operational costs of database administration. Added complexity of managing a large database diverts operational budgets away from more urgent IT projects.

13 Pivoting Your Perspective: Columnar Technology …. Copyright 2007, Information Builders. Slide 13

14 Row-based databases are ubiquitous because so many of our most important business systems are transactional. Row-oriented databases are well suited for transactional environments, such as a call center where a customers entire record is required when their profile is retrieved and/or when fields are frequently updated. The Ubiquity of Rows But - Disk I/O becomes a substantial limiting factor since a row-oriented design forces the database to retrieve all column data for any query. 30 columns 50 millions Rows The Limitation of Rows

15 Row Oriented ( 1, Smith, New York, 50000; 2, Jones, New York, 65000; 3, Fraser, Boston, 40000; 4, Fraser, Boston, ) Works well if all the columns are needed for every query. Efficient for transactional processing if all the data for the row is available Works well with aggregate results (sum, count, avg. ) Only columns that are relevant need to be touched Consistent performance with any database design Allows for very efficient compression Column Oriented ( 1, 2, 3, 4; Smith, Jones, Fraser, Fraser; New York, New York, Boston, Boston, 50000, 65000, 40000, ) Employee Id Name Smith Jones Fraser Location New York Boston Sales 50,000 65,000 40,000 4FraserBoston70,000 Pivoting Your Perspective Columnar Technology

16 WebFOCUS Hyperstage Copyright 2007, Information Builders. Slide 16

17 Introducing WebFOCUS Hyperstage Mission Improve database performance for WebFOCUS applications with less hardware, no database tuning, and easy migration What is WebFOCUS Hyperstage High performance analytic data store Designed to handle business-driven queries on large volumes of data without IT intervention. Easy to implement and manage, Hyperstage provides answers to your business users need at a price you can afford Advantages Dramatically increase performance of WebFOCUS applications Disk footprint reduced with powerful compression algorithm = faster response time Embedded ETL for seamless migration of existing analytical databases No change in query or application required Includes optimized Hyperstage Adapter WebFOCUS metadata can be used to define hierarchies and drill paths to navigate the star schema 17

18 Hyperstage Engine Knowledge Grid Compressor Bulk Loader Unmatched Administrative Simplicity No Indexes No data partitioning No Manual tuning Introducing WebFOCUS Hyperstage How it is architected Combines a columnar database with intelligence we call the Knowledge Grid to deliver fast query responses. Improve database performance for WebFOCUS applications with less hardware, no database tuning, and easy migration

19 Introducing WebFOCUS Hyperstage What it means for Customers Self-managing: 90% less administrative effort Low-cost: More than 50% less than alternative solutions Scalable, high-performance: Up to 50 TB using a single industry standard server Fast queries: Ad hoc queries are as fast as anticipated queries, so users have total flexibility Compression: Data compression of 10:1 to 40:1 means a lot less storage is needed, it might mean you can get the entire database in memory!

20 Create Information (Metadata) about the data, and, upon Load, automatically … Create Information (Metadata) about the data, and, upon Load, automatically … Uses the metadata when Processing a query to Eliminate / reduce need to access data Uses the metadata when Processing a query to Eliminate / reduce need to access data Architecture Benefits o Stores it in the Knowledge Grid (KG) o KG Is loaded into Memory o Less than 1% of compressed data Size o The less data that needs to be accessed, the faster the response o Sub-second responses when answered by KG o No Need to partition data, create/maintain indexes projections, or tune for performance o Ad hoc queries are as fast as static queries, so users have total flexibility Introducing WebFOCUS Hyperstage How it works

21 Smarter Architecture No maintenance No query planning No partition schemes No DBA Data Packs – data stored in manageably sized, highly compressed data packs Knowledge Grid – statistics and metadata describing the super-compressed data Column Orientation Data compressed using algorithms tailored to data type WebFOCUS Hyperstage Engine How it works

22 Summary Copyright 2007, Information Builders. Slide 22

23 Business Intelligence – Meeting Requirements

24 No indexes No partitions No views No materialized aggregates Value proposition Low IT overhead Allows for autonomy from IT Ease of implementation Fast time to market Less Hardware Lower TCO No DBA Required! WebFOCUS Hyperstage The Big Deal

25 WebFOCUS Hyperstage Adapter What it looks like

26

27 Example – Focus to Hyperstage Compression Rows

28 Q&A Co pyr igh t 20 07, Inf or ma tio n Bui lde rs. Slid e 28


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