Ibi Open Visualizations Data Sheet

Total Page:16

File Type:pdf, Size:1020Kb

Ibi Open Visualizations Data Sheet Secure. Curated. Real-time. Trusted. ibi Open Visualizations Data sheet Connect your BI platform to any enterprise data source Are you asking your BI platform to do too much? Install once, Glossy visuals can’t make up for missing data. Or dirty data. Or all of the other configure at scale workarounds you’ve patched together trying to squeeze more out of your BI tools. Establish secure At some point, the complexities of your organization demand a cohesive platform data management and that opens up your ability to leverage your enterprise data. ibi Open Visualizations architecture standards connects your existing business intelligence (BI) platform to any database, file format, your application, or web service in a curated, secure, real-time, and trusted manner. Power up dashboards with Let your BI platform do what it does best – and let us do the rest. scalable, integrated data Empower your BI platform to get the results your Make it easy for end users business deserves ThoughtSpot, Power BI, Tableau, and others are visualization tools meant for self-service insights. But in a complex data environment, these tools struggle to leverage many of the most essential enterprise data sources, without adding more complexity by building new data marts or warehouses. “Only 22% of organizations say they have gained good competitive advantage from » their BI investments.”1 1 “Understanding the Vendor Perspective in BI Projects,” Business Application Research Center, 2020. In addition to the limitations of the tools themselves, they quickly erode faith in the entire BI function they’re meant to elevate, resulting in: › “Islands of data” › Lack of data security › Multiple versions of the truth › Significant enterprise data management challenges Metadata Query Clustering and Figure 1. Deliver Management Optimization Load Balancing richer analytics without switching your visualization Administration, Performance, Data Prep product. Simply Scale, Security, Resource power up with ibi Mgmt. Open Visualizations. All enterprise data sources ibi Open Visualizations Your BI platform Databases One central repository Native and single connection to all data Mainframes Data security Real-time access Files Single metadata layer Trusted data for dashboards APIs User governance Cloud infrastructure Clustering and load balancing Applications ibi Open Visualizations empowers any visualization tool with all of the benefits of data virtualization – real- time, direct access to data without the need for an expensive data warehouse. It supplies connectivity to any enterprise data source. Make better decisions with better data, all while using the tool your business users are already familiar with. › Install once and configure at scale. Install and configure multiple data connections on one server with little data preparation effort › Easily establish secure data management and architecture standards. Take control of your data without undertaking a large data mastering project › Power up your dashboards with scalable, integrated data. Cross-database joins let you query and treat multiple data sources as one › Make it easy for end users. Provide a native and single connection to trusted data with real-time access Databases JBoss SQLBase Legacy OLAP RStat Compiled 1010data JDBC SnappyData Adabas ESSBASE Models Amazon Athena Jethro Snowflake Datacom/DB SAP BW ESRI ArcGIS Amazon Redshift MS Access Stratio Crossdata FOCUS SQL Server Analysis Social Media ODBC Services MS Azure Synapse Sybase IDMS/DB ALCHEMY Apache Drill Analytics (formerly SSAS Tabular Data Teradata IMS Facebook Apache Hive SQL DW) Model Transoft LDAP Google Analytics Apache Impala MS SQL Server ODBC/AzureDB UniData Lotus Notes Procedures Google Sheets Apache Phoenix MS SQL Server OLE UniVerse Millennium Adabas/NAT LinkedIn Apache Spark SQL DB/AzureDB Vertica Model 204 CICS Transaction Slack Cache MariaDB parAccel SUPRA CICS Transaction for Twitter Couchbase Natural MongoDB WAND DB2/DB2 Warehouse File and XML ERP Gainsight MySQL Words Analysis EXASol DB Heritage Files JD Edwards Hyperledger Fabric Netezza EnterpriseOne Google BigQuery Delimited Files (CSV/ IMS Transaction Search Engines Nucleus/SAND TAB) JD Edwards World Greenplum DB IWAF Elastic Search ODBC Excel Lawson H2 Natural Batch Lucene Search ODBC Extender Fixed Format Files Microsoft Dynamics Hyperstage Query/400 Solr Search Oracle Informix CISAM CRM IBM i Access REST Oracle TimesTen JSON Oracle Applications Remote Storage IDMS/SQL Web Services PSQL Kafka PeopleSoft AWS S3 Informix PostgreSQL MicroFocus CISAM Salesforce.com Statistics and GIS Google Drive Ingres Presto OData SAP Python Interplex/Unisys Progress Siebel DMS 1100 VSAM Rserve SAP Hana DB XML Figure 2. ibi Open Visualizations offers access to more than 120 native enterprise connections (and growing). About ibi ibi is a data and analytics company that embeds intelligence into — everything. From the beginning, ibi has known the importance of data and insights to make better decisions. We help organizations get their complex and disconnected data in order, so they can build, embed, and automate intelligence into everything they do. By preparing organizations for the future and turning them into builders – information builders – everyone can use enterprise trusted data at scale to drive their growth. Whether our customers use pre-built applications or build their own solutions for their data and analytics challenges, ibi powers their innovation and reinvention. ibi’s open platform and industry-specific building blocks Request a demo accelerate speed to market, improve operational efficiency, and enhance See ibi in action and their customers’ experience. imagine what you will build. ibi.com/request-a-demo. ibi. build a better future. Contact us at ibi.com or email [email protected]. Copyright © 2020 by Information Builders, Inc. All rights reserved. All products and product names mentioned are trademarks or registered trademarks of their respective companies. 7510246.0620.
Recommended publications
  • Query All Tables in a Schema
    Query All Tables In A Schema Orchidaceous and unimpressionable Thor often air-dried some iceberg imperially or amortizing knee-high. Dotier Griffin smatter blindly. Zionism Danie industrializing her interlay so opaquely that Timmie exserts very fustily. Redshift Show Tables How your List Redshift Tables FlyData. How to query uses mutexes will only queried data but what is fine and built correctly: another advantage we can easily access a string. Exception to query below queries to list of. 1 Do amount of emergency following Select Tools List Tables On the toolbar click 2 In the. How can easily access their business. SQL to Search for her VALUE data all COLUMNS of all TABLES in. This system table has the user has a string value? Search path for improving our knowledge and dedicated professional with ai model for redshift list all constraints, views using schemas. Sqlite_temp_schema works without loop. True if you might cause all. Optional message bit after finishing an awesome blog. Easy way are a list all objects have logs all databases do you can be logged in lowercase, fully managed automatically by default description form. How do not running sap, and sizes of all object privileges granted, i understood you all redshift of how about data professional with sqlite? Any questions or. The following research will bowl the T-SQL needed to change every rule change the WHERE clause define the schema you need and replace. Lists all of schema name is there you can be specified on other roles held by email and systems still safe even following command? This data scientist, thanx for schemas that you learn from sysindexes as sqlite.
    [Show full text]
  • A Platform for Networked Business Analytics BUSINESS INTELLIGENCE
    BROCHURE A platform for networked business analytics BUSINESS INTELLIGENCE Infor® Birst's unique networked business analytics technology enables centralized and decentralized teams to work collaboratively by unifying Leveraging Birst with existing IT-managed enterprise data with user-owned data. Birst automates the enterprise BI platforms process of preparing data and adds an adaptive user experience for business users that works across any device. Birst networked business analytics technology also enables customers to This white paper will explain: leverage and extend their investment in ■ Birst’s primary design principles existing legacy business intelligence (BI) solutions. With the ability to directly connect ■ How Infor Birst® provides a complete unified data and analytics platform to Oracle Business Intelligence Enterprise ■ The key elements of Birst’s cloud architecture Edition (OBIEE) semantic layer, via ODBC, ■ An overview of Birst security and reliability. Birst can map the existing logical schema directly into Birst’s logical model, enabling Birst to join this Enterprise Data Tier with other data in the analytics fabric. Birst can also map to existing Business Objects Universes via web services and Microsoft Analysis Services Cubes and Hyperion Essbase cubes via MDX and extend those schemas, enabling true self-service for all users in the enterprise. 61% of Birst’s surveyed reference customers use Birst as their only analytics and BI standard.1 infor.com Contents Agile, governed analytics Birst high-performance in the era of
    [Show full text]
  • Exasol User Manual Version 6.0.8
    Exasol User Manual Version 6.0.8 Empowering analytics. Experience the world´s fastest, most intelligent, in-memory analytics database. Copyright © 2018 Exasol AG. All rights reserved. The information in this publication is subject to change without notice. EXASOL SHALL NOT BE HELD LIABLE FOR TECHNICAL OR EDITORIAL ERRORS OR OMISSIONS CONTAINED HEREIN NOR FOR ACCIDENTAL OR CONSEQUENTIAL DAMAGES RES- ULTING FROM THE FURNISHING, PERFORMANCE, OR USE OF. No part of this publication may be photocopied or reproduced in any form without prior written consent from Exasol. All named trademarks and registered trademarks are the property of their respective owners. Exasol User Manual Table of Contents Foreword ..................................................................................................................................... ix Conventions ................................................................................................................................. xi Changes in Version 6.0 ................................................................................................................. xiii 1. What is Exasol? .......................................................................................................................... 1 2. SQL reference ............................................................................................................................ 5 2.1. Basic language elements .................................................................................................... 5 2.1.1. Comments
    [Show full text]
  • Finding Fraud in Large and Diverse Data Sets
    Business white paper Finding fraud in large and diverse data sets Applying real-time, next-generation analytics to fraud detection and prevention using the HP Vertica Analytics Platform Developments in data mining In the effort to identify and deter fraud, conventional wisdom still applies: Follow the money. That simple adage notwithstanding, the task of tracking fraud and its perpetrators continues to vex both private and public organizations. Clearly, advancements in information technology have made it possible to capture transaction data at the most granular level. For instance, in the retail trade alone, transmissions of up to 500 megabytes daily between individual point-of-sale sites and their data centers are typical.2 Logically, such detail should result in greater transparency and greater capacity to fight fraud. Yet, the sheer volume of data that organizations now maintain, pulled from so many sources and stored across a range of locations has made the same organizations more vulnerable.3 More points of entry amount to more opportunities for fraud. In its annual Global Fraud Report, The Economist found that 50% of all businesses surveyed acknowledged they were vulnerable to fraud; 35% of North American companies specifically cited IT complexity for increasing their exposure to risk. Accordingly, the application of data mining as a security measure A solution for real-time fraud detection has become increasingly germane to modern fraud detection. Historically, data mining as a means of identifying trends from raw Fraud saps hundreds of billions of dollars each year from the statistics can be traced back to the 1700s with the introduction of bottom line of industries such as banking, insurance, retail, Bayes’ theorem.
    [Show full text]
  • SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
    SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes.
    [Show full text]
  • The Vertica Analytic Database: C-Store 7 Years Later
    The Vertica Analytic Database: C-Store 7 Years Later Andrew Lamb, Matt Fuller, Ramakrishna Varadarajan Nga Tran, Ben Vandiver, Lyric Doshi, Chuck Bear Vertica Systems, An HP Company Cambridge, MA {alamb, mfuller, rvaradarajan, ntran, bvandiver, ldoshi, cbear} @vertica.com ABSTRACT that they were designed for transactional workloads on late- This paper describes the system architecture of the Ver- model computer hardware 40 years ago. Vertica is designed tica Analytic Database (Vertica), a commercialization of the for analytic workloads on modern hardware and its success design of the C-Store research prototype. Vertica demon- proves the commercial and technical viability of large scale strates a modern commercial RDBMS system that presents distributed databases which offer fully ACID transactions a classical relational interface while at the same time achiev- yet efficiently process petabytes of structured data. ing the high performance expected from modern “web scale” This main contributions of this paper are: analytic systems by making appropriate architectural choices. 1. An overview of the architecture of the Vertica Analytic Vertica is also an instructive lesson in how academic systems Database, focusing on deviations from C-Store. research can be directly commercialized into a successful product. 2. Implementation and deployment lessons that led to those differences. 1. INTRODUCTION 3. Observations on real-world experiences that can in- The Vertica Analytic Database (Vertica) is a distributed1, form future research directions for large scale analytic massively parallel RDBMS system that commercializes the systems. ideas of the C-Store[21] project. It is one of the few new commercial relational database systems that is widely used We hope that this paper contributes a perspective on com- in business critical systems.
    [Show full text]
  • Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing
    Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing Dennis Butterstein Daniel Martin Jia Zhong Lingyun Wang Knut Stolze Felix Beier IBM Silicon Valley Lab IBM Research & Development GmbH 555 Bailey Ave Schonaicher¨ Strasse 220 San Jose, CA 95141, United States 71032 Boblingen,¨ Germany [email protected] [email protected] [email protected] fdanmartin,stolze,[email protected] ABSTRACT engine was first replaced with Netezza (IBM PureData Sys- 2 The IBM Db2 Analytics Accelerator (IDAA) is a state- tem for Analytics ). Netezza's design is to always use table of-the art hybrid database system that seamlessly extends scans on all of its disks in parallel, leveraging FPGAs to ap- the strong transactional capabilities of Db2 for z/OS with ply decompression, projection and filtering operations before the very fast column-store processing in Db2 Warehouse. the data hits the main processors of the cluster nodes. The The Accelerator maintains a copy of the data from Db2 for row-major organized tables were hash-distributed across all z/OS in its backend database. Data can be synchronized nodes (and disks) based on selected columns (to facilitate at a single point in time with a granularity of a table, one co-located joins) or in a random fashion. The engine itself or more of its partitions, or incrementally as rows changed is optimized for table scans; besides Zone Maps there are no using replication technology. other structures (e. g., indices) that optimize the processing IBM Change Data Capture (CDC) has been employed as of predicates.
    [Show full text]
  • A Peek Under the Hood
    White paper Technical A Peek under the hood White paper Technical Contents A peek under the hood 01 02 03 Introduction 3 Being really fast 5 Providing a Great User Experience 12 Massively Parallel Processing MPP 5 Self-Optimization 13 Large-Scale In-Memory Advanced Analytics 04 Architecture 9 and Data Science 14 Supporting Business Integration Filters, Joins and Sorting 10 and Day-to-Day Operation 16 Query Optimizer and Interfaces and Tool Query Cache 11 Integration 17 Data Ingestion and Data Integration 18 The Virtual Schema Framework for Data Virtualization & 05 Hybrid Clouds 20 Summary 13 Fail Safety, Dual Data Center Operation and Backup/Restore 24 SQL Preprocessor 25 01 White paper Technical 3 Introduction Exasol was founded in founders recognized that database designed specifically Nuremberg, Germany, in the year new opportunities were made for analytics. Exasol holds 2000 – a time when two trends possible by these trends. With performance records in the in hardware were starting to RAM falling in cost and rising in TPC-H online transaction emerge: capacity and cluster computing processing benchmark from being merely a commodity, it the Transaction Processing Major improvements in was now conceivable to apply Performance Council (TPC) for processing power were no the principles and architectures decision-support databases, longer coming from ever of high-performance computing outperforming competitors by increasing clock speeds of to database design. In the years orders of magnitudes and scaling central processing units (CPUs), that followed, the company up to hundreds of terabytes of but instead from parallel and exclusively focused on delivering data. distributed systems.
    [Show full text]
  • Database Software Market: Billy Fitzsimmons +1 312 364 5112
    Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions
    [Show full text]
  • Vertica Advanced Analytics Platform
    Data Sheet Vertica Advanced Analytics Platform Vertica Advanced Analytics Platform The Vertica Advanced Analytics Platform is consciously designed with speed, scalability, simplicity, and openness at its core and architected to handle analytical workloads via a distributed compressed columnar architecture. Vertica Advanced Analytics Platform provides blazingly fast speed (queries run 10–50X faster), exabyte scale (store 10–30X more data per server), openness, and simplicity (use any business intelligence [BI]/ETL tools, Hadoop, etc.)—at a much lower cost than traditional data warehouse solutions and a better time to market than unproven open source solutions. Handling Today’s Massive What Are the Key Technology Quick View Requirements of a Big Data Data Volumes At the core of the Vertica Advanced Analytics In modern data infrastructures, data comes Analytics Platform? Platform is a column-oriented, relational database from everywhere: business systems like CRM So, just what should you look for in a data ana- built specifically to handle today’s analytic workloads. and ERP, IoT sensors, tweets and other social lytics solution to address today and tomorrow’s Unlike commercial and open-source row stores, media data, Web logs and data streams, gas data challenges? Consider the following: which were designed long ago to support small data, the Vertica Advanced Analytics Platform provides and electrical grids, and mobile networks to Analyze huge data volumes in a unified customers with: name a few. With all this data generated from so manner: You are likely looking to analyze many places, companies are turning to dispa- data at unlimited scale combined with the • Complete and advanced SQL-based analytical functions to provide powerful SQL analytics rate, lower-cost storage locations to store and need to store your data in the right place manage these volumes, adding complexity and at the right time.
    [Show full text]
  • EXASOL AG Our History – Inventing World’S Fastest In-Memory Database
    The Most Powerful In-memory Analytic Database Introduction @ Sphinx IT in Vienna 25.11.2016 © 2016 EXASOL AG Our history – inventing world’s fastest in-memory database 2008 2012 Record in Inclusion in Gartner’s 2000 TPC-H Benchmark „Magic Quadrant for Company foundation („Oracle dethroned“) Data Management Systems“ 90ies 2006 2010 2014 early Research Success Pilot Customer Karstadt- Most Successful Vendor of Successful global expansion, (University Erlangen- Quelle uses EXASolution in analytical database systems in 400+ customers across 12 countries Nürnberg) Production Germany (BARC) 100TB TPC-H benchmark © 2016 EXASOL AG Great recognition in the market 2016 © 2016 EXASOL AG What Gartner says about EXASOL “EXASOL is a prime example of what Gartner considers to be the future of DBMS” Source: Gartner © 2016 EXASOL AG Why would you be looking for a new Database Performance/ Pricing Issues with New Requests for Existing DWH agile or predictive Analytics Changing Plattforms or Regulatory Issues growing DataSources © 2016 EXASOL AG King: leading interactive entertainment company . Analyzes customer behavior . Optimizes game revenues . Lots and lots of data . 100 million daily active users . 1 billion game plays per day . 10 billion events per day EXASOL database: 200TB © 2016 EXASOL AG Zalando: Rising star of e-commerce . Online fashion retailer with 14m+ customers across Europe . 150,000 products available online . EXASOL complements DWH to enable fast analytics and reporting . Database optimizes stock availability, returns process and targeted marketing EXASOL database: 15TB © 2016 EXASOL AG Adidas: CRM . Several projects in different regions (Europe, USA, Russia) . Agile BI -> flexible reporting functionality for quick projects . BW on HANA: “Cruise liner” .
    [Show full text]
  • Gartner Magic Quadrant for Data Management Solutions for Analytics
    16/09/2019 Gartner Reprint Licensed for Distribution Magic Quadrant for Data Management Solutions for Analytics Published 21 January 2019 - ID G00353775 - 74 min read By Analysts Adam Ronthal, Roxane Edjlali, Rick Greenwald Disruption slows as cloud and nonrelational technology take their place beside traditional approaches, the leaders extend their lead, and distributed data approaches solidify their place as a best practice for DMSA. We help data and analytics leaders evaluate DMSAs in an increasingly split market. Market Definition/Description Gartner defines a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or many file management systems, most commonly a database or multiple databases. These management systems include specific optimization strategies designed for supporting analytical processing — including, but not limited to, relational processing, nonrelational processing (such as graph processing), and machine learning or programming languages such as Python or R. Data is not necessarily stored in a relational structure, and can use multiple data models — relational, XML, JavaScript Object Notation (JSON), key-value, graph, geospatial and others. Our definition also states that: ■ A DMSA is a system for storing, accessing, processing and delivering data intended for one or more of the four primary use cases Gartner identifies that support analytics (see Note 1). ■ A DMSA is not a specific class or type of technology; it is a use case. ■ A DMSA may consist of many different technologies in combination. However, any offering or combination of offerings must, at its core, exhibit the capability of providing access to the data under management by open-access tools.
    [Show full text]