More and more companies are faced with the challenge of managing an explosion in data along with how to give a variety of users access to this information. This session discusses how Qlik’s data analytics platform can meet this challenge. By providing associative analytics to Big Data repositories, Qlik enables fast and engaging data discovery on massive data volumes while providing users with full access to all the details of the underlying data.Read more >
The cloud has the potential to deliver on the promise of big data processing for machine learning and analytics to help organizations become more data-driven, however, it presents its own set of challenges.
This webinar covers best practices in areas such as.
- Using automation in the cloud to derive more value from big data by delivering self-service access to data lakes for machine learning and analytics
- Enabling collaboration among data engineers, data scientists, and analysts for end-to-end data processing
- Implementing financial governance to ensure a sustainable program
- Managing security and compliance
- Realizing business value through more users and use cases
In addition, this webinar provides an overview of Qubole’s cloud-native data platform’s capabilities in areas described above.
About Our Speaker:
James Curtis is a Senior Analyst for the Data, AI & Analytics Channel at 451 Research. He has had experience covering the BI reporting and analytics sector and currently covers Hadoop, NoSQL and related analytic and operational database technologies.
James has over 20 years' experience in the IT and technology industry, serving in a number of senior roles in marketing and communications, touching a broad range of technologies. At iQor, he served as a VP for an upstart analytics group, overseeing marketing for custom, advanced analytic solutions. He also worked at Netezza and later at IBM, where he was a senior product marketing manager with responsibility for Hadoop and big data products. In addition, James has worked at Hewlett-Packard managing global programs and as a case editor at Harvard Business School.
James holds a bachelor's degree in English from Utah State University, a master's degree in writing from Northeastern University in Boston, and an MBA from Texas A&M University.
For decades people have been talking about self-service BI and analytics to enable business users to make better decisions in organizations. Yet, the big data era (Hadoop, Spark, data lakes, and more) has seemingly pushed us in the direction of automation, machine learning, and AI. Are your business users left out? Do they still ask for more control over reporting and analysis? What if you could provide the simplicity of Internet search for ALL users on ALL of your data in the organization? What if you could do all of this and provide security and privacy with the full power of visual analytics?
Join industry thought leaders from 451 Research and Arcadia Data on October 17th at 12 p.m. BST, 1 p.m. EET to learn about recent trends in big data analytics around natural language search, self-service, and AI-driven insights. In this webinar, you will learn:
•Why modern analytical environments need to focus more on business users.
•Why traditional BI approaches are falling short.
•How new innovations like search-based BI are redefining self-service BI.
When it comes to Big Data Analytics, do you know if you are on the right track to succeed in 2017?
Is Hadoop where you should place your bet? Is Big Data in the Cloud a viable choice? Can you leverage your traditional Big Data investment, and dip your toe in modern Data Lakes too? How are peer and competitor enterprises thinking about BI on Big Data?
Come learn 5 traps to avoid and 5 best practices to adopt, that leading enterprises use for their Big Data strategy that drive real, measurable business value.
In this session you’ll hear from Hal Lavender, Chief Architetect of Cognizant Technologies, Thomas Dinsmore, Big Data Analytics expert and author of ‘Disruptive Analytics: Charting Your Strategy for Next-Generation Business Analytics, along with Josh Klahr, VP of Product, as they share real world approaches and achievements from innovative enterprises across the globe.
Join this session to learn…
- Why leading enterprises are choosing Cloud for Big Data in 2017
- What 75% of enterprises plan to drive value out of their Big Data
- How you can deliver business user access along with security and governance controls
How do you avoid your enterprise data lake turning into a so-called data swamp? The explosion of structured, unstructured and streaming data can be overwhelming for data lake users, and make it unmanageable for IT. Without scalable, repeatable, and intelligent mechanisms for cataloguing and curating data, the advantages of data lakes diminish. The key to solving the problem of data swamps is Informatica’s metadata driven approach which leverages intelligent methods to automatically discover, profile and infer relationships about data assets. Enabling business analysts and citizen integrators to quickly find, understand and prepare the data they are looking for.Read more >
Do you know that your existing investments in Informatica PowerCenter can fast track you to Big Data and data lake technologies? We will demonstrate why our customers are moving from data warehouses to data lakes, leveraging big data and cloud ecosystems and how to do this rapidly, leveraging your existing investments in Informatica technology.Read more >
The shelf life of data is shrinking. A streaming shift is taking place and use cases such as IoT connected cars, real-time fraud detection and predictive maintenance using streaming analytics are becoming commonplace. You too can switch to the fast data lane with Informatica, leveraging Kafka and other big data technologies. So shift gears and change lanes with us while we take you on a journey into the world of streaming data.Read more >
„You can´t use it, if you can´t find it” – Heutzutage werden in Unternehmen mehr Daten denn je gesammelt, gespeichert und genutzt. Studien und Umfragen zeigen jedoch, dass lieber gesammelt als genutzt wird.
Woran liegt das? Eine Ursache hierfür ist, dass Unternehmen gar nicht wissen, welche Daten gesammelt werden, wo sie gespeichert werden und wie diese genutzt werden können. Es fehlt an Transparenz und Struktur.
In unserem 45-minütigen Webinar möchten wir Ihnen anhand einiger Praxisbeispiele zeigen, wie Sie mit einem Data Catalog Informationen über alle Datentöpfe hinweg zentral zur Verfügung stellen können. Erfahren Sie, wie unsere Kunden davon profitieren und welche Herausforderungen mit einem Data Catalog gemeistert werden können.
Learn the origin of big data applications, how new data pipelines require a new infrastructure toolset and why both containers and shared storage are the fundamental infrastructure building blocks for future data pipelines.
We will first discuss the factors driving changes in the big-data ecosystem: ever-greater increases in the three Vs of data volume, velocity, and variety. The data lake concept was originally conceived as a single location for all data, but the reality is that multiple pipelines and storage systems quickly lead to complex data silos. We then contrast the legacy Hadoop applications, which are built only for volume, and the next generation of applications, like Spark and Kafka, which solves for all three Vs. Finally, we end with how to build infrastructure to support this new generation of applications, as well as applications not yet in existence.
About the Speakers:
Ivan Jibaja, Tech Lead, Pure Storage Ivan Jibaja is currently a tech lead for the Big Data Analytics team inside Pure Engineering. Prior to this, he was a part of the core development team that built the FlashBlade from the ground-up. Ivan graduated with a PhD in Computer Science from the University of Texas at Austin, with a focus on systems and compilers.
Joshua Robinson, Founding Engineer, FlashBlade, Pure Storage Joshua builds Pure's expertise in big-data, advanced analytics, and AI. His focus is on organizing a cross-functional team, technical validation, performance benchmarking, solution architectures, collecting customer feedback, customer consultations, and company-wide trainings. Joshua specializes in several data analytics tools, including Hadoop, Spark, ElasticSearch, Kafka, and TensorFlow.
You've got data. Too much data. How do you not only analyze it, but access relevant insights from it to transform your business?
Start by hearing our customer success story from UK building merchant supplier and home-improvement leader, Travis Perkins, and how they: .
• Seamlessly load and access Big Data
• Enabled their suppliers to access and analyze the key insights in their Big Data
• Quickly improved on-time deliveries using data analytics with Qlik
No Code, Low Code Big Data Analytics from Simple Search to Complex Event Processing.
Logtrust is designed for fast data exploration and interaction with real-time visualizations on complex data streams and historical data at rest such as:
- Machine behavior during attacks
- Network traffic flow analytics
- Firewall events
- Application performance metrics
- Real-time threat hunting and cyber security
- IoT analytics
Explore Petabytes of data with Logtrust without worrying about storage costs or indexers, analyze billions of events per day with ultra-low latency queries, and experience unique real-time performance on trillions of events with over +150,000 ingest EPS per core, +1,000,000 search EPS per core, and +65,000 complex event processing EPS per core.
Live Data Exploration
Logtrust data is always fresh with real-time data updates in their native formats. Slice and dice subsets of data at any point in time for exploration and deep forensics on real-time data streams.
Powerful Data Exploration & Analytics
Accelerate time-to-insights and rich visualizations with simple point and click. Empower your team to quickly harness insights and make faster, smarter decisions. Optionally, use a single compact expressive SQL language (LINQ) and create reusable callable queries for more complex event processing operations.
Watch our video featuring, Yogesh Joshi, Head of Big Data and Analytics, AIG and Ajay Anand, VP Products & Marketing at Kyvos Insights, Inc. where they discuss if OLAP is still relevant in the age of Big Data and showcase various methods for performing iterative, interactive analytics on Hadoop.Read more >
As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.
Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.
The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.
Another huge advantage of the data lake approach is the ability to annotate data sets and data granules with intelligent, searchable, reusable, flexible, user-generated, semantic, and contextual metatags. This tag layer makes your data "smart" -- and that makes your agile big data environment smart also!
Im zweiten Webinar unserer „Big Data im Fokus“ Serie wird es pragmatisch, praktisch, gut. Wir zeigen Ihnen, wie Sie die richtigen Daten nicht nur finden können, sondern auch direkt für Ihre Anwendungsfälle sauber und nachvollziehbar aufbereiten können. Mit unserer Self-Service Data Preparation Lösung nehmen wir Rohdaten in die Hand, bringen diese in Form, stellen die Qualität sicher und erstellen uns eine perfekte Datenbasis für analytische Anwendungsfälle. Anhand eines praktischen Beispiels sehen Sie, wie ein Fachanwender Schritt für Schritt ans Ziel kommt, ohne dabei eine Zeile Programmcode zu schreiben.Read more >
Today's PLM programs are helping companies build new capabilities to improve product portfolio management, sku-level profitability, customer service levels, and regulatory compliance. Data management, migration, and governance are a critical foundation for success, but have added complexity for PLM since much of the data is in "unstructured" sources like PDFs.
Please join this webinar to hear Chris Knerr, BackOffice Associate's Global Big Data & Analytics Leader, share Life Sciences and CPG best practices for PLM programs.
Advancements in data management technology are enabling retailers to reinvent themselves to rapidly respond to changing customer expectations. In this session we will look at how Big data and streaming analytics allows retailers to derive insights from data generated by new technology deployed instore and online to offer unique and compelling customer experiences across all channels.
Louis Polycarpou shares his knowledge of how big data management and streaming technologies are being used within the retail sector to better engage consumers and boost profits.
We have come a long way since the term "Big Data" swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop, NoSQL and Spark have become members of the enterprise IT landscape, data lakes have evolved as a real strategy and migration to the cloud has accelerated across service and deployment models.
On the road ahead, the demand for real-time analytics will continue to skyrocket alongside growth in IoT, machine learning, and cognitive applications. Meeting the speed and scalability requirements of these types of workloads requires more flexible and efficient data management processes – both on-premises and in the cloud. Flexible deployment and integration options will become a must-have for projects.
Finally, the need for data governance and security is intensifying as businesses adopt new approaches to expand their data storage and access via data lakes and self-service analytics programs. As data, along with its sources and users, continues to proliferate, so do the risks and responsibilities of ensuring its quality and protection.
Join us to watch the replay of "What's Ahead in Big Data and Analytics" to get real direction and practical advice on the challenges and opportunities to tackle in 2018.
The big data analytics market has undergone continuous transformation since its’ inception and continued in 2017 with new innovations and a strong move to the cloud. But from the view of a customer, the world should be getting simpler, not more complex, and they expect products to make deployments faster and easier.
Instead of complex, “piece together your own architecture” approaches, 2018 will be a year in which customers can really focus on what’s important – the data and analytics – and not the underlying technologies that support them, whether on-premise, in the cloud, or hybrid.
In this session, John will explore five ways in which modern big data platforms will enable to you:
-Accelerate your big data initiatives
-Get more value from your data lakes
-Drive faster, more innovative analytics
Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.
Join 451 Research and Kinetica to learn:
•An overview of the business and technical trends driving widespread interest in real-time analytics
•Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
•How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
•Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
In this on-demand webinar, Google and Talend experts demonstrate how to implement machine learning algorithms into analytics pipelines, and extract sentiment analysis to achieve a new level of insight and opportunity.Read more >
Today, the marketing industry is flooded with automation and optimization tools, but all these tools live as separate platforms with multiple performance reports. You have all the data but you can't see blended campaign results across all those platforms without hours or days of manual intervention? Learn about:
• Which stage your marketing data is in and what are the barriers to rolling and blending all those campaign metrics to single dashboard views - without manual intervention
• At what stage can you wean your marketing users away from the analyst queue – they get insights at a few clicks of a button – and your analysts can focus on bigger projects
• How to bring predictive models into a single platform for analysis to share it with the business