Introduction to Cloudera&#x27;s Unique Architecture &amp; Competitive Advantages

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We live in the Age of Data. Now more than ever, it is crucial that organizations can connect, analyze and act on the vast amounts of data that surrounds them in order to succeed long-term. This session will discuss the Age of Data and how companies can deploy technology such as Actian ParAccel SMP, a fast analytic database platform that runs on standard hardware, in order to run sophisticated, unconstrained analytics on massive amounts of data (structured, unstructured, Hadoop etc) and turn their data into business value. Christian Raza - Director of Sales SEMEA, @Actian Corporation Actian presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013

BI Solutions sometimes are required to do more than analyzing data, you can use that information to act upon the outside world and close the loop. During this presentation we will see how to use Sparkl, a plugin that has just been released, enabling to easily create screens that allow you to take actions. During this demo the Mongo DB plugin from Pentaho will be leveraged to show how we can even integrate with NoSQL databases. Pedro Martins - Head of Implementations, @Webdetails - Pentaho

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By Loreto Fernández Costas and Adrián Doncel Gabaldó. Digital Transformation starts with data. What if a solution existed that put data at the center, in a single place, serving all applications around it – A distributed data centric solution that combined the operational and the informational, managed by a single data center operating system? This session will provide a detailed explanation of such a solution, bringing the concept of data centricity to life. We will cover the details of the array of open source technologies that come together to create a transformational solution to the historic problem of physical companies: From multiple data stores, distributed run-time engines and SQL engines based on Spark, to microservices, Machine Learning and Deep Learning Algorithms. Big Data 3.0 is just round the corner.

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Delivered to SQL Saturday BI Edition -- Atlanta, GA Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.

How to Swiftly Operationalize the Data Lake for Advanced Analytics Using a Lo...

Watch full webinar here: https://bit.ly/3mfFJqb Presented at Chief Data Officer Live Series 2021, ASEAN (August Edition) While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best-of-breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform, and provide real-time data integration while delivering a self-service data platform to business users. Watch this on-demand session to learn how big data fabric enabled by Data Virtualization: - Provides lightning fast self-service data access to business users - Centralizes data security, governance, and data privacy - Fulfills the promise of data lakes to provide actionable insights

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Microsoft Data Science Technologies 201608

Delivered to SQL Saturday Columbus, GA Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.

Data Virtualization: From Zero to Hero

Watch full webinar here: https://bit.ly/2xc6IO0 To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture. In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn: - What data virtualization really is - How it differs from other enterprise data integration technologies - Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations

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Big Data Hadoop Training & Certification online. Clear CCA175 & CCAH exams. 9 Real life Big Data projects. Led by industry experts.This online business analytics course introduces quantitative methods to analyze data and make better decisions.Our self-paced Data Warehousing training helps you master Data Warehousing Tools and concepts. The course also earns you a Data Warehousing ... Job Assistance. Enroll Now +1(210)503-7100 .For More Information : http://radiantits.com/.

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As more companies grow their business in global markets, they discover the need to capture new opportunities in a matter of days rather than months to have competitive advantage and to capture new market share. Their machines are producing terabytes of various data types — video, audio, Microsoft® SharePoint®, sensor data, Microsoft Excel® files — and leaders are searching for the right technologies to capture this data and help provide a better understanding of their business. The HDS big data product roadmap will help customers build a big data enterprise plan that ingests data faster and correlate meaningful data sets to create intelligence that’s easy to consume and helps leaders make the right business decisions. View this webcast to learn about Hitachi’s product roadmap to big data. For more information on HDS Big Data Solutions please visit: http://www.hds.com/solutions/it-strategies/big-data/?WT.ac=us_mg_sol_bigdat

Virtualisation de données : Enjeux, Usages & Bénéfices

Watch full webinar here: https://bit.ly/3oah4ng Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données. Découvrez : - Les bénéfices d’une plateforme de virtualisation de données - La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT - La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance - La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride

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The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...

The Hive

Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation. Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit. While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.

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To watch full webinar, follow this link: https://goo.gl/3s9hRG The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed such as those found in cloud data sources to make a “full centralization” strategy successful. Attend this webinar to learn: • Why Logical architectures are the best option when integrating Big Data. • How Denodo’s parallel in-memory capabilities with dynamic query optimization redefine analytics architectures. • How IT can meet business demands for data much faster with Data Virtualization. Agenda: • Challenges with traditional approaches for analytics architectures. • Overview of Denodo's parallel in-memory capabilities. • Product Demo of parallel in-memory capabilities accelerating analytics performance. • Q&A. To watch all webinars in Denodo's Packed Lunch Webinar Series, follow this link: https://goo.gl/4xL9wM

Next-Gen Cloud Analytics with AWS, Big Data and Data Virtualization

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Watch Tekin's keynote presentation from Fast Data Strategy Virtual Summit here: https://goo.gl/RJon7n The Denodo Platform for AWS enabled Logitech's cloud journey with minimal impact on business operations. The Denodo Platform acts a big data fabric layer and sources data for all of Logitech's analytics initiatives from descriptive to prescriptive to predictive analytics including NLP processing engines. Attend this session to learn how Logitech: • Reduced TCO such as infrastructure and operational expenses • Empowered their business users with advanced analytics capabilities • Uses AWS and Denodo as their innovation engine

When SAP alone is not enough

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To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.

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Datumize Deck 2019

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Datumize is a software vendor established in 2014 in Barcelona (Spain) working on data integration technology. We develop innovative products that allow companies to enjoy actionable insights based on Dark Data - data not stored and therefore not used. Our secret sauce is a proprietary and powerful data collection engine, Datumize Data Collector (DDC), that gets data from fancy sources that most other vendors do not consider.

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