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 >
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 >
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 >
„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.
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 >
Business intelligence (BI) has been at the forefront of business decision-making for more than two decades. Then along came Big Data and it was thought that traditional BI technologies could never handle the volumes and performance issues associated with this unusual source of data.
So what do you do? Cast aside this critical form of analysis? Hardly a good answer. The better answer is to look for BI technologies that can keep up with Big Data, provide the same level of performance regardless of the volume or velocity of the data being analyzed, yet give the BI-savvy business users the familiar interface and multi-dimensionality they have come to know and love.
This webinar will present the findings from a recent survey of Big Data and the challenges and value many organizations have received from their implementations. In addition, the survey will supply a fascinating look into what Big Data technologies are most commonly used, the types of workloads supported, the most important capabilities for these platforms, the value and operational insights derived from the analytics performed in the environment, and the common use cases.
Attendees will also learn about a new BI technology built to handle Big Data queries with superior levels of scalability, performance and support for concurrent users. BI on Big Data platforms enables organizations to provide self-service and interactive on big data for all of their users across the enterprise.
Yes, now you CAN have BI on Big Data platforms!
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.
Watch this online session and learn how to reconcile the changing analytic needs of your business with the explosive pressures of modern big data.
Leading enterprises are taking a "BI with Big Data" approach, architecting data lakes to act as analytics data warehouses. In this session Scott Gidley, Head of Product at Zaloni is joined by Josh Klahr, Head of Product at AtScale. They share proven insights and action plans on how to define the ideal architecture for BI on Big Data.
In this webinar you will learn how to
- Make data consumption-ready and take advantage of a schema-on-read approach
- Leverage data warehouse and ETL investments and skillsets for BI on Big Data
- Deliver rapid-fire access to data in Hadoop, with governance and control
With increasing data volumes and sources, enterprises are outgrowing their traditional BI solutions and struggling to use the data collected on their new data platforms.
In this webinar, Ibrahim Itani, Executive Leader of Big Data Architecture and Technology, talks about Verizon’s big data journey and how they use new technologies to solve problems with data at scale without data movement.
Ibrahim is joined by Sanjay Kumar, General Manager of Telecom at Hortonworks, and Sancha Norris, Director of Product Marketing at Kyvos Insights, who shares additional use cases that leverage big data architectures and interactive BI to reach their business goals.
* Learn how to deal with the complexity of big data at rest and in motion
* The differences between traditional OLAP and the modern OLAP on Hadoop
* How to put together a Hadoop architecture for self-service interactive BI
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.
Implementing Hadoop can be complex, costly, and time-consuming. It can take months to get up and running, and each new user group typically requires their own infrastructure.
This on-demand webinar will explain how to tame the complexity of on-premises Big Data infrastructure. Tony Baer, Big Data analyst at Ovum, and BlueData will provide an in-depth look at Hadoop multi-tenancy and other key challenges.
Watch to learn about:
-The pitfalls to avoid when deploying Big Data infrastructure
- Real-world examples of multi-tenant Hadoop implementations
-How to achieve the simplicity and agility of Hadoop-as-a-Service – but on-premises
Gain insights and best practices for your Big Data deployment. Find out why data locality is no longer required for Hadoop; discover the benefits of scaling compute and storage independently. And more.
Big Data Analytics success has been constrained by the difficulty in accessing siloed data and by the traditional IT approach of gathering requirements, designing and building extracts to turn data into valuable data assets. As IT organizations are backlogged with servicing business requests, business analysts and data scientists are looking for alternative methods to discover relevant data, share data with colleagues across divisions or geographies and prepare data assets for actionable insights.
In this deep dive, you will have the opportunity to learn about new features of Informatica Big Data Management 10.1 and Informatica’s latest innovation, Intelligent Data Lake, leveraging self-service efficiency for business analysts and data scientists by incorporating semantic search, data discovery and data preparation for interactive analysis while governing data assets.
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 >
Every investment in big data, whether people or technology, should be measured by how quickly it generates value for the business. While big data uses cases may vary, the need to prioritize investments, control costs and measure impact is universal.
Like most CTOs, CIOs, VPs or Directors overseeing big data projects, you’re likely somewhere in between putting out fires and demonstrating how your big data projects are driving growth. If your focus, for example, is improving your users’ experience you need to be able to demonstrate a clear ROI in the form of higher customer retention or lifetime value.
However, in addition to driving growth, you’re also responsible for managing costs. Here’s the rub-- if you’re successful in driving growth, your big data costs will only go up. That’s the consequence of successful big data use cases. How then, when you have success, do you limit and manage rising cloud costs?
In this webinar, you’ll learn:
- How to measure business value from big data use cases
- Typical bottlenecks that delay time to value and ways to address them
- Strategies for managing rising cloud and people costs
- How best-in-class companies are generating value from big data use cases while also managing their costs
Organizations are faced with countless obstacles to achieving big data success, including platform, application and user issues, as well as limited resources. This webinar will answer operational management questions around optimizing performance and maximizing capacity, such as “Who’s blowing up our cluster?, “How can I run more applications?” and more. You will learn from our expert, based on real-world deployments, how a complete APM solution provides:
– Reduced mean time to problem resolution.
– An accurate understanding of the most expensive users.
– Improved platform throughput, uptime, efficiency and performance.
– Reduced backlog.
– And more.
Alex Pierce joined Pepperdata in 2014. Previously, he worked as a senior solution architect at WanDisco. Before that, he was the senior solution architect at Red Hat. Alex has a strong background in system administration and big data.
Hadoop is not just for play anymore. Companies that are turning petabytes into profit have realized that Big Data Management is the foundation for successful Big Data projects.
Informatica Big Data Management delivers the industry’s first and most comprehensive solution to natively ingest, integrate, clean, govern, and secure big data workloads in Hadoop.
In this webinar you’ll learn through in depth product demos about new features that help you increase productivity, scale and optimize performance, and manage metadata such as:
• Dynamic Mappings – enables mass ingestion & agile data integration with mapping templates, parameters and rules
• Smarter Execution Optimization – higher performance with pushdown to DB, auto-partitioning and runtime job execution optimization
• Blaze – high performance execution engine on YARN for complex batch processing
• Live Data Map – Universal metadata catalog for users to easily search and discover data properties, patterns, domain, lineage and relationships
Register today for this deep dive and demo.
The German Cancer Research Center (DKFZ) uses self-service big data analytics to radically improve the genomic research process. Their new insights have allowed them to identify better treatment plans for cancer patients.
During this one-hour on-demand webinar, Dr. Fritz Schinkel, head of Fujitsu’s Big Data Competence Center and a Fujitsu Distinguished Engineer, discusses how the combined Datameer and Fujitsu platform helps the DKFZ:
--Perform deeper analysis on raw datasets representing millions of genomic positions without requiring data reduction techniques that can compromise results
--Dramatically reduce the time it takes to analyze raw genomic datasets for each patient to speed creating patient treatments
Join our Big Data Activation Report Webinar where our CEO Ashish Thusoo will go in-depth into our 2018 Qubole Big Data Activation Report findings and share how customers are using multiple engines to get the most out of their big data.
The report analyzes usage data from over 200 Qubole customers to provide answers to key questions such as:
- How fast is usage of open source big data engines like Apache Spark, Presto and Apache Hive/Hadoop growing?
- What engines are used most and for what?
- What engines and big data tools are rising stars?
- How successful are companies at providing their users access to data?
- What are the cost saving benefits of doing big data in the cloud?
You'll come away with both hard data and a few ideas for how to get more out of your big data initiatives.
Watch this on-demand webinar to learn about use cases for Big-Data-as-a-Service (BDaaS) – to jumpstart your journey with Hadoop, Spark, and other Big Data tools.
Enterprises in all industries are embracing digital transformation and data-driven insights for competitive advantage. But embarking on this Big Data journey is a complex undertaking and deployments tend to happen in fits and spurts. BDaaS can help simplify Big Data deployments and ensure faster time-to-value.
In this webinar, you'll hear about a range of different BDaaS deployment use cases:
-Sandbox: Provide data science teams with a sandbox for experimentation and prototyping, including on-demand clusters and easy access to existing data.
-Staging: Accelerate Hadoop / Spark deployments, de-risk upgrades to new versions, and quickly set up testing and staging environments prior to rollout.
-Multi-cluster: Run multiple clusters on shared infrastructure. Set quotas and resource guarantees, with logical separation and secure multi-tenancy.
-Multi-cloud: Leverage the portability of Docker containers to deploy workloads on-premises, in the public cloud, or in hybrid and multi-cloud architectures.
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.