The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
Case Study - Spotad: Rebuilding And Optimizing Real-Time Mobile Adverting Bid...Vasu S
Find out how Qubole helped Spotad, Inc's mobile advertising platform, save 50 percent in its operating costs almost instantly after their migration.
https://www.qubole.com/resources/case-study/spotad
The Hadoop market is currently growing at a faster pace due to the rising demand for quicker access to relevant data for everyday functioning across industry sectors.
Apache Spark is a rapidly growing and highly popular open source project that was Initially developed at the University of California, Berkeley’s AMPLab, starting in 2009. Spark is a next-generation, cluster-computing, runtime processing environment and development framework for in-memory advanced analytics. It continues to deepen open source platforms in the big data arena by leveraging and extending Apache Hadoop framework investments into machine learning, streaming data, SQL and graph analytics use cases. This slideshow presentation provides a crisp, ten-point summary of what every data analytics professional should know about Spark.
Hungry for more information on Spark? Learn more about Spark today at BigDataUniversity.com
Case Study - Spotad: Rebuilding And Optimizing Real-Time Mobile Adverting Bid...Vasu S
Find out how Qubole helped Spotad, Inc's mobile advertising platform, save 50 percent in its operating costs almost instantly after their migration.
https://www.qubole.com/resources/case-study/spotad
The Hadoop market is currently growing at a faster pace due to the rising demand for quicker access to relevant data for everyday functioning across industry sectors.
Apache Spark is a rapidly growing and highly popular open source project that was Initially developed at the University of California, Berkeley’s AMPLab, starting in 2009. Spark is a next-generation, cluster-computing, runtime processing environment and development framework for in-memory advanced analytics. It continues to deepen open source platforms in the big data arena by leveraging and extending Apache Hadoop framework investments into machine learning, streaming data, SQL and graph analytics use cases. This slideshow presentation provides a crisp, ten-point summary of what every data analytics professional should know about Spark.
Hungry for more information on Spark? Learn more about Spark today at BigDataUniversity.com
As we begin to dive deeper into the connected world, there has been an explosion of structured and unstructured data. Additionally, advancements in Apache Hadoop and other Big Data technologies, cloud computing and machine learning tools all play into how this world will evolve. Over the last ten years, Apache Hadoop has proven to be a popular platform among seasoned developers who require a technology that can power large, complex applications. However, for customers, partners and application ISVs who write on-top of Hadoop, there is still one huge issue that remains; Interoperability. In this talk, john Mertic will take a closer look at how Apache Hadoop can become more interoperable to accelerate big data implementations.
Case Study - Gordon Foods Delivers Fresh Data to the CloudDATAVERSITY
The traditional ETL approach for moving data to the cloud is labor-intensive and costly, not to mention brittle and slow, draining organizations of time and resources that they just do not have.
In this webinar, you will hear from Gordon Food Service and how they sharpened their competitive edge by delivering the freshest data to Google Cloud and dished up a better customer experience through real-time data insights. You will discover how Qlik’s data integration platform enabled Gordon Food Service to successfully run their Data Modernization Analytics Program and build real-time analytic data pipelines, unlocking multiple data sources, to Google Cloud with simple yet powerful data delivery.
Register today and learn how Gordon Foods:
• Improved their Customer Experience
• Replaced slow custom replication scripts and speed up analytics
• Simplify and automate their real-time data streaming process
• Moves thousands of objects on a daily basis
Find out how your organization can breathe new life into your data in the cloud, stay ahead of changing demands while lowering over-reliance on resources, production time and costs.
The Modern Data Architecture for Predictive Analytics with Hortonworks and Re...Revolution Analytics
Hortonworks and Revolution Analytics have teamed up to bring the predictive analytics power of R to Hortonworks Data Platform.
Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly. R, being a prolific and rapidly growing data analysis language, now has a place in the Hadoop ecosystem.
This presentation covers:
- Trends and business drivers for Hadoop
- How Hortonworks and Revolution Analytics play a role in the modern data architecture
- How you can run R natively in Hortonworks Data Platform to simply move your R-powered analytics to Hadoop
Presentation replay at:
http://www.revolutionanalytics.com/news-events/free-webinars/2013/modern-data-architecture-revolution-hortonworks/
Big Data kennen sehr viele IT-Experten, wenigstens haben Sie eine Vorstellung davon. In der Praxis arbeiten damit in Deutschland derzeit nur wenige. Dabei bringt Big Data ein ganz neues Momentum in moderne Softwarelösungen und ist im Kontext der Mobil-, Cloud- und Social-Veränderungen nicht wegzudenken. Big Data macht Software intelligent und damit auf eine ganz neue Art für die Benutzer erlebbar. Mit Big Data entstehen neue Softwarearchitekturen, weil Informationen völlig anders verarbeitet werden - nämlich schneller, differenzierter und oft mit dem Ziel, Schlüsse zu ziehen und Vorhersagen zu treffen.
In diesem Vortrag wird erläutert, wie moderne Softwarearchitekturen gestaltet werden, sodass Sie Big Data Paradigmen erfolgreich umsetzen und welche Vorteile sich für die zunehmend mobilen Softwarelösungen ergeben. Wir werfen zudem einen Blick auf die Potentiale und Optionen in Branchen wie Banken, Versicherung oder Handel.
Global Manufacturer Averts Data Swamp with New Data Lake ArchitectureAntuit
With the solid data architecture
designed by Antuit in place, the
organization now has accessible data
at their fingertips. Antuit continues
to work with the organization to build
new analytical models that solve
business problems extract value from
their growing data sets.
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/yVJnti
Data virtualization starts with democratizing data access for business users, but goes well beyond to enable entire analytics life cycle. This session will discuss the critical role of data virtualization in the four key phases of big data analytics: Discovery of raw and enriched data, Analytic Exploration, Real-time Operationalization, and Predictive Intervention.
In this session, you will learn:
• Design of advanced analytics with view towards business goal realization
• The role of data virtualization in enabling analytics through four key phases
• How to exploit product capabilities relevant to each stage
• Creating a system of governed self-service and collaborative analytics
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
MicroStrategy abstracted the SAP HANA data schema, along with other data warehouses and multi-dimensional sources, into one unified system of record, hiding the underlying complexity from end users.
Use cases for Hadoop and Big Data Analytics - InfoSphere BigInsightsGord Sissons
This presentation is from TDWI's event in Boston during the summer of 2014. IBM InfoSphere BigInsights is IBM's enterprise grade Hadoop offering. It combines the best of open-source Hadoop, with advanced capabilities including Big SQL that clients can optionally deploy to get to market faster with a variety of big data and analytic applications.
Demo: Report Authoring in IBM Cognos AnalyticsSenturus
Demos showing the new reporting interface in Cognos Analytics. View the webinar video recording and download this deck: http://www.senturus.com/resources/demo-report-authoring-cognos-analytics/.
We show how authoring works in Cognos Analytics 11 and how it contrasts to Cognos BI 10. Topics include: new vs. legacy templates, adding new report data, creating calculations, new data discovery features and new OLAP features.
Senturus, a business analytics consultancy, has a resource library with hundreds free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
The Microsoft business intelligence front end tools are rich and varied and have also changed and grown over the years. We give an overview of the Microsoft business analytics tools, from Power BI to Excel and SQL Server and include a chart that compares their features.
As we begin to dive deeper into the connected world, there has been an explosion of structured and unstructured data. Additionally, advancements in Apache Hadoop and other Big Data technologies, cloud computing and machine learning tools all play into how this world will evolve. Over the last ten years, Apache Hadoop has proven to be a popular platform among seasoned developers who require a technology that can power large, complex applications. However, for customers, partners and application ISVs who write on-top of Hadoop, there is still one huge issue that remains; Interoperability. In this talk, john Mertic will take a closer look at how Apache Hadoop can become more interoperable to accelerate big data implementations.
Case Study - Gordon Foods Delivers Fresh Data to the CloudDATAVERSITY
The traditional ETL approach for moving data to the cloud is labor-intensive and costly, not to mention brittle and slow, draining organizations of time and resources that they just do not have.
In this webinar, you will hear from Gordon Food Service and how they sharpened their competitive edge by delivering the freshest data to Google Cloud and dished up a better customer experience through real-time data insights. You will discover how Qlik’s data integration platform enabled Gordon Food Service to successfully run their Data Modernization Analytics Program and build real-time analytic data pipelines, unlocking multiple data sources, to Google Cloud with simple yet powerful data delivery.
Register today and learn how Gordon Foods:
• Improved their Customer Experience
• Replaced slow custom replication scripts and speed up analytics
• Simplify and automate their real-time data streaming process
• Moves thousands of objects on a daily basis
Find out how your organization can breathe new life into your data in the cloud, stay ahead of changing demands while lowering over-reliance on resources, production time and costs.
The Modern Data Architecture for Predictive Analytics with Hortonworks and Re...Revolution Analytics
Hortonworks and Revolution Analytics have teamed up to bring the predictive analytics power of R to Hortonworks Data Platform.
Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly. R, being a prolific and rapidly growing data analysis language, now has a place in the Hadoop ecosystem.
This presentation covers:
- Trends and business drivers for Hadoop
- How Hortonworks and Revolution Analytics play a role in the modern data architecture
- How you can run R natively in Hortonworks Data Platform to simply move your R-powered analytics to Hadoop
Presentation replay at:
http://www.revolutionanalytics.com/news-events/free-webinars/2013/modern-data-architecture-revolution-hortonworks/
Big Data kennen sehr viele IT-Experten, wenigstens haben Sie eine Vorstellung davon. In der Praxis arbeiten damit in Deutschland derzeit nur wenige. Dabei bringt Big Data ein ganz neues Momentum in moderne Softwarelösungen und ist im Kontext der Mobil-, Cloud- und Social-Veränderungen nicht wegzudenken. Big Data macht Software intelligent und damit auf eine ganz neue Art für die Benutzer erlebbar. Mit Big Data entstehen neue Softwarearchitekturen, weil Informationen völlig anders verarbeitet werden - nämlich schneller, differenzierter und oft mit dem Ziel, Schlüsse zu ziehen und Vorhersagen zu treffen.
In diesem Vortrag wird erläutert, wie moderne Softwarearchitekturen gestaltet werden, sodass Sie Big Data Paradigmen erfolgreich umsetzen und welche Vorteile sich für die zunehmend mobilen Softwarelösungen ergeben. Wir werfen zudem einen Blick auf die Potentiale und Optionen in Branchen wie Banken, Versicherung oder Handel.
Global Manufacturer Averts Data Swamp with New Data Lake ArchitectureAntuit
With the solid data architecture
designed by Antuit in place, the
organization now has accessible data
at their fingertips. Antuit continues
to work with the organization to build
new analytical models that solve
business problems extract value from
their growing data sets.
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/yVJnti
Data virtualization starts with democratizing data access for business users, but goes well beyond to enable entire analytics life cycle. This session will discuss the critical role of data virtualization in the four key phases of big data analytics: Discovery of raw and enriched data, Analytic Exploration, Real-time Operationalization, and Predictive Intervention.
In this session, you will learn:
• Design of advanced analytics with view towards business goal realization
• The role of data virtualization in enabling analytics through four key phases
• How to exploit product capabilities relevant to each stage
• Creating a system of governed self-service and collaborative analytics
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
MicroStrategy abstracted the SAP HANA data schema, along with other data warehouses and multi-dimensional sources, into one unified system of record, hiding the underlying complexity from end users.
Use cases for Hadoop and Big Data Analytics - InfoSphere BigInsightsGord Sissons
This presentation is from TDWI's event in Boston during the summer of 2014. IBM InfoSphere BigInsights is IBM's enterprise grade Hadoop offering. It combines the best of open-source Hadoop, with advanced capabilities including Big SQL that clients can optionally deploy to get to market faster with a variety of big data and analytic applications.
Demo: Report Authoring in IBM Cognos AnalyticsSenturus
Demos showing the new reporting interface in Cognos Analytics. View the webinar video recording and download this deck: http://www.senturus.com/resources/demo-report-authoring-cognos-analytics/.
We show how authoring works in Cognos Analytics 11 and how it contrasts to Cognos BI 10. Topics include: new vs. legacy templates, adding new report data, creating calculations, new data discovery features and new OLAP features.
Senturus, a business analytics consultancy, has a resource library with hundreds free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
The Microsoft business intelligence front end tools are rich and varied and have also changed and grown over the years. We give an overview of the Microsoft business analytics tools, from Power BI to Excel and SQL Server and include a chart that compares their features.
Demos of the new reporting interface in Cognos Analytics 11. View the webinar video recording and download this deck: http://www.senturus.com/resources/demo-report-authoring-cognos-analytics/.
We show you how authoring works in Cognos Analytics 11 and how it contrasts to Cognos BI 10. We go deep on various aspects of report creation including: new vs. legacy templates, adding new report data, creating calculations, new data discovery features, new OLAP features.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
30 Reasons You Still Need to Stage Your Data Senturus BlogSenturus
If you think you’ve got a data warehouse, or don’t even need one, you might want to think again. Our CEO John Peterson gives real-life, practical reasons for why you still need a real data warehouse…and tells the advantages of properly staged data.
Using Cognos as a Data Source for Tableau: Demo & Live Case Study with IxiaSenturus
The Senturus Analytics Connector allows Tableau to access the secure, governed and business-friendly data that lives in Cognos, preventing the labor-intensive process of re-remodeling data. Analytics Connector user, Ixia, demonstrated how it used the Connector to reduce Cognos-to-Tableau data prep time from days to minutes and experienced an immediate 150% ROI.
View the webinar video recording and download this deck: http://www.senturus.com/resources/using-cognos-as-a-data-source-for-tableau/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Scaling Tableau to the Enterprise: The Perks and Pitfalls of Tableau Server W...Senturus
Learn the perks of Tableau Server while paying attention to the avoidable pitfalls and important considerations that impact performance, scale and user adoption. View the webinar video recording: http://www.senturus.com/resources/going-big-time-with-tableau-server/
Topics covered include: how Tableau Server allows users to share their work, with anyone, via any browser, in a secure environment; how Tableau Server enables self-service analytics over the web and via mobile devices; how Tableau Server can help control your Tableau spend; how under powered hardware, installing with defaults and insufficient care and feeding negatively impact performance; the #1 barrier to entry and user adoption and how Tableau provides flexible deployment options (in-house / cloud) as well as licensing options (named user / unlimited).
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Learn how you can increase performance in IBM Cognos. Learn about PureData for Analytics, why it's fast and how to integrate with IBM Cognos Analytics. For more information about PureData for Analytics and for a free whitepaper, email info@crescointl.com, or visit http://www.crescointl.com.
IBM Cognos Analytics - Cognos Business Intelligence version 11Cresco International
Learn more about the new IBM Cognos Analytics - a self-service analytics and reporting tool that you can use to gain better insight on key data for your organization. For more information and to start your trial, visit http://www.crescointl.com.
See how easily you can use Cognos as a data source for Power BI and Tableau using the Senturus Analytics Connector. First introduced in 2015, the Senturus Analytics Connector continues to add features and functionality. View the recording and deck of the most recent release: https://senturus.com/resources/use-visual-analytics-tools-with-trusted-cognos-data/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
The book sold on:
http://www.amazon.co.uk/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.com/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.in/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
Originally Published on Sep 23, 2014
IBM InfoSphere BigInsights, an enterprise-ready distribution of Hadoop, is designed to address the challenges of big data and modern IT by analyzing larger volumes of data more cost-effectively. Deployed on the cloud, it enables rapid deployment of clusters and real-time analytics.
FYI: The value of Hadoop and many more questions will be pondered at this year’s Strata/Hadoop World event in NYC (October 15-17, 2014) and certainly at IBM Insight (October 26-30, 2014).
In today’s context, the big data market is rapidly undergoing contortions that define market maturity, such as consolidation. Big data refers to large volumes of data. This can be both structured and unstructured data. Big data is data that is huge in size and grows exponentially with time. As the data is too large and complex, traditional data management tools are not sufficient for storing or processing it efficiently. But analyzing big data is crucial to know the patterns and trends to be adopted to improve your business.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Asterix Solution’s Hadoop Training is designed to help applications scale up from single servers to thousands of machines. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Duration - 25 hrs
Session - 2 per week
Live Case Studies - 6
Students - 16 per batch
Venue - Thane
BIG Data & Hadoop Applications in Social MediaSkillspeed
Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed.
BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences.
Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Learn about IBM's Hadoop offering called BigInsights. We will look at the new features in version 4 (including a discussion on the Open Data Platform), review a couple of customer examples, talk about the overall offering and differentiators, and then provide a brief demonstration on how to get started quickly by creating a new cloud instance, uploading data, and generating a visualization using the built-in spreadsheet tooling called BigSheets.
With many organisations considering getting on the Hadoop bandwagon, this document provides an overview of the planned use cases for Hadoop, an illustration of some of the common technology components, suggestions on when Hadoop is worth considering, some the challenges organisations are experiencing, cost considerations and finally, how an organisation should position for a Big Data initiative. Any organisation considering a Big Data initiative with Hadoop should thoroughly consider each of these areas before embarking on a course of action.
Data is like the new currency, and myriad technologies revolves around the idea of putting Big Data into work, while enhancing the levels of ROI. Gear up to witness a humongous growth of data in the 2017, both in respect to variety and volume.
Hadoop is an Apache project to store & process Big Data. Hadoop stores large chunk of data called Big Data in a distributed & fault tolerant manner over commodity hardware. After storing, Hadoop tools are used to perform data processing over HDFS (Hadoop Distributed File System).
Similar to The Big Picture on Big Data and Cognos (20)
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Tableau: 6 Ways to Publish & Share DashboardsSenturus
See demos of the steps to take and learn the pros, cons and requirements for each option. We discuss Tableau Desktop, Tableau Public, Tableau Server, Tableau Metrics, Tableau mobile for publishing and sharing Tableau content. Watch the demos and download this deck at: https://senturus.com/resources/6-ways-to-publish-share-tableau-dashboards/.
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Demos of Cognos 11.2 new features such as dashboard performance Improvements, new home page customizations and onboarding experience, improved search functions, enhancements to AI assisted data exploration, natural language generation, natural language questions and new automated statistical forecasts. Watch the demos and download this deck at: https://senturus.com/resources/whats-new-in-cognos-analytics-11-2-0/.
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Azure Synapse vs. Snowflake: The Data Warehouse Dating GameSenturus
Learn the complexities and unique aspects of the two leading cloud-based data warehouse contenders that are most likely tugging at your heartstrings: Snowflake Data Cloud and Azure Synapse Analytics.
Deciding on one is a long-term commitment. Before you put on ring on it, watch the recording to see demos of each data cloud, learn similarities, strengths and weaknesses of each and get criteria to help determine which is the right choice for your organization. View our on-demand webinar and download this deck at: https://senturus.com/resources/azure-synapse-vs-snowflake-price-performance/
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Secrets of High Performing Report Development TeamsSenturus
Learn how BI teams build great reports consistently. We discuss how to improve report-building workflow and delivery rate while reducing mistakes. View our on-demand webinar and download this deck at: https://senturus.com/resources/secrets-of-high-performing-report-development-teams/.
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Power BI: Data Cleansing & Power Query EditorSenturus
Learn how to transform data into a format that aligns with the metrics you need. Power BI data cleansing technique demos include removing null values, removing columns, unpivoting data, add custom columns, merging queries and working with an M script. Watch the demos and download this deck at: https://senturus.com/resources/power-bi-data-cleansing-power-query-editor/
Senturus offers a full spectrum of services across the BI stack plus training on Power BI, Cognos and Tableau. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
The Big Picture on Big Data and Cognos
1. The Big Picture on Big Data and Cognos
www.senturus.com/blog/big-picture-big-data-cognos/
August 1, 2016 Business Strategy & Perspectives
IBM has a long history of supporting major open source projects and the most widely adopted open standards. Their
enterprise customers have benefited from the flexibility, choice, and innovation that come with the open source
philosophy. Major projects include SOA (Service-Oriented Architecture), Linux, Eclipse, and now Hadoop. The big
data analytics open source offering is known as the IBM Open Platform with Apache Hadoop. The commercial side
of this platform, announced in early 2015, is a suite of products for the enterprise branded as BigInsights.
To better understand IBM's big data offerings around Hadoop and its open data platform, it is helpful to put this in
context of the overall vision for the platform and the three phases of the IBM Big Data Analytics lifecycle:
1. Pull in all types of data from disparate sources
2. Put the data into a business context
3. Produce intelligent, data driven business outcomes, for example, operational efficiency, customer
engagement, or risk management
IBM endeavors to cover a lot of business territory with its analytics platform. For the enterprise IT department, the
technology enables data integration, governance, security, and regulatory compliance. For line of business
managers, the analytics environment is the home of customer and operational intelligence. While analytics play an
important role in increasing operational efficiency and eliminating business process bottlenecks, it is the customer-
centric analytics that have captured the imagination of business executives. Big data analytics offers many
opportunities for improving customer relationships and increasing engagement across marketing channels.
A common big data use case is delivering relevant promotions to customers. We all share the experience of
receiving credit card offers in the mail from the bank and tossing the envelope directly into the recycling bin without
even thinking about it. Despite the dismal response rate, it was cost effective for the bank to send the same direct
mail piece to everyone. With a big data platform, it is possible to develop customer profiles and create targeted
offers for each segment. For example, customers that have a single account and a short customer history would be
candidates for a different array of promotions than someone who has been a customer for decades. The cost of
amassing enough data and having the processing power to crunch the numbers in a timely fashion has dropped
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2. enough to make it profitable to do so.
With digital advertising and social media data, analysis is required on huge amounts of unstructured data. A couple
of years ago this was experimental at best, but now Hadoop software enables capturing and processing
unprecedented amounts of data. It complements the enterprise data warehouse and is an integral part of the
business intelligence ecosystem.
Open Data Platform ODPi
The ODPi open data platform is a consortium of IBM and 18 other enterprise software vendors working together to
maximize the adoption of technologies based on Apache Hadoop. The goal of ODPi is to accelerate software
development by providing a standard Hadoop solution on which an applications can be run, whether it is
commercial software, open source, or custom code developed in-house. This gives enterprise customers assurance
that they are not locking themselves into a single vendor's Hadoop solution. It also permits using a Hadoop
implementation with products from multiple vendors. For Hadoop to fulfill its role as an enterprise data source, it
must accommodate a broad audience who will be using many different applications.
To that end, the ODPi provides a core platform of agreed on and tested big data Apache Hadoop modules. This is
the ODPi standard, on which the vendors build their applications. For example, Hortonworks, IBM Open Platform
4.0 with Apache Hadoop, EMC Pivotal HD 3.0, and Infosys IIP all adhere to the ODPi standard. Analytics software
vendors or in-house development shops can concentrate on developing applications further up the stack, knowing
that the Hadoop core adheres to a standard and its application will interoperate with any compliant Hadoop system.
This accelerates development, promotes code re-use, and simplifies the technical architecture. Implementing a
Hadoop distribution that adheres to the ODPi standard means not being locked into a proprietary technology.
As a standard, only time will tell if the ODPi will have a lasting impact. The organization has been criticized as being
nothing more than a joint marketing effort for vendors pushing their own commercial flavor of Hadoop. Also to note
are the big data vendors who are conspicuous by their absence: Cloudera, MapR, and Amazon (AWS – EMR
Elastic MapReduce).
IBM BigInsights and Cognos
On top of Hadoop, IBM has developed a suite of big data and analytics tools under the BigInsights brand. There are
tools for scaling and managing the platform (BigInsights Enterprise Management), a machine learning engine
(BigInsights Data Scientist – Decision Trees, PageRank, Clustering) and a data exploration and discovery tool
(BigSheets). Of particular interest to Cognos customers is BigSQL which runs SQL queries against Hadoop or in
other words, BigSQL permits Cognos to use Hadoop as a data source.
This is interesting as data stored in Hadoop only becomes useful when it is put into a business context. Cognos
Analytics (V11) is well suited for this role. It is a powerful tool for BI developers and business power users, enabling
the presentation of Hadoop data in a visually appealing format for executives, managers, and line of business
staffers. Big data becomes much more valuable when it can be interpreted and understood by non-technical users.
Cognos supports connecting to Hadoop using Hive, which translates code from SQL to MapReduce to get results
from Hadoop. There will always be some latency as Hive cannot change the nature of MapReduce, which
distributes processing work across Hadoop nodes. The query is split into discrete chunks of work and the results are
assembled as they are returned. SQL join conditions, which are commonplace in Cognos generated SQL, create an
additional layer of complexity for MapReduce. This further increases the query processing time and will prevent
some queries from running at all.
IBM addresses these problems with BigSQL. It works on the same Hive megastore, but produces faster and more
reliable results. BigSQL is not just about performance, but also assuring that the SQL query will run. It optimizes
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3. SQL for MapReduce so that it will run faster and prevent having to modify the Cognos Framework Manager model
or hand code SQL inside of Cognos. An alternative to Hive and BigSQL is Impala, which makes similar claims to
performance.
Success with Big Data requires getting key pieces to work together. With BigInsights and BigSQL, IBM is providing
tools for facilitating Hadoop adoption, including interoperability with existing Cognos infrastructure and functionality.
Stay on top of business intelligence topics, read other Senturus blogs at: http://www.senturus.com/blog/.
Resources
Senturus webinar Running Cognos on Hadoop:
http://www.senturus.com/resources/running-cognos-on-hadoop/
Video of Hive and BigSQL performance test results:
https://developer.ibm.com/hadoop/blog/2015/10/23/hive-and-big-sql-performance-test-update/
IBM BigSQL technology sandbox demo cloud environment for Hadoop and BigSQL:
https://my.imdemocloud.com/projects/3467
Thanks to David Currie for contributing this article. David is a long-time business analytics consultant. He blogs
about business intelligence and big data at davidpcurrie.com.
Big Data / IBM Cognos
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