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 >
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 >
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
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 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.
Join us to learn:
- 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.
Get your questions answered and hear how the Spin-Merge benefits our abilities to deliver advanced analytics and machine learning for your Big Data needs.
Join two of our HPE Software Big Data leaders to hear firsthand about the recently announced spin-merge. Gain direct insight into what it means for you. This is a big opportunity for us to deliver even more of the advanced analytics at Exabyte scale that all data driven organizations depend on in our fast moving world. Hear about our Big Data portfolio strategy including upcoming innovations addressing performance at scale for tomorrow’s workloads, infrastructure independent deployments and a growing set of in database machine learning algorithms. Bring your questions and join us on this accelerated journey to success.
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
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!
Join this 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.
Don't miss this informative webinar.
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.
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.
Join this webinar to learn how to deploy a scalable and elastic architecture for Big Data analytics.
Hadoop and related technologies for Big Data analytics can deliver tremendous business value, and at a lower cost than traditional data management approaches. But early adopters have encountered challenges and learned lessons over the past few years.
In this webinar, we’ll discuss:
-The five worst practices in early Hadoop deployments and how to avoid them
-Best practices for the right architecture to meet the needs of the business
-The case study and Big Data journey for a large global financial services organization
-How to ensure highly scalable and elastic Big Data infrastructure
Discover the most common mistakes for Hadoop deployments – and learn how to deliver an elastic Big Data solution.
Watch this webinar to learn about Big-Data-as-a-Service from experts at Dell and BlueData.
Enterprises have been using both Big Data and Cloud Computing technologies for years. Until recently, the two have not been combined.
Now the agility and efficiency benefits of self-service elastic infrastructure are being extended to big data initiatives – whether on-premises or in the public cloud.
In this webinar, you’ll learn about:
- The benefits of Big-Data-as-a-Service – including agility, cost-savings, and separation of compute from storage
- Innovations that enable an on-demand cloud operating model for on-premises Hadoop and Spark deployments
- The use of container technology to deliver equivalent performance to bare-metal for Big Data workloads
- Tradeoffs, requirements, and key considerations for Big-Data-as-a-Service in the enterprise
Malgré quelques similarités, Hadoop et Spark sont souvent considérées comme la même technologie. Durant ce webcast, Marc Royer, Spécialiste SE et Big Data chez Dell EMC vous guidera à travers les différences entre ces outils, afin que vous puissiez choisir le bon pour votre projet Big Data.
Pendant ces 30 minutes vous découvrirez notamment :
- Pourquoi et comment les organisations se tournent vers le Big Data pour innover
- Spark et Hadoop, et les spécificités des deux outils
- Quelques cas d’usage de ces technologies
- Une méthodologie pour réussir votre projet Big Data et les bonnes questions à se poser pour choisir la technologie appropriée
Join Matt Aslett of 451 Research for a briefing on the current big data analytics trends that are driving customers to utilize fast big data applications for increased customer engagement, reduced risk, and greater operational efficiency. After which, Nathan Trueblood will share DataTorrent's direct experiences working with enterprise organizations who are deploying fast big data apps to accelerate business outcomes TODAY and why they believe their customers' use of these applications will be the difference between success or failure in the future.Read more >
Data is collected in IoT solutions for a purpose - it is transformed into information which is subsequently used to produce actionable insights.
The three primary types of IoT data, in order of volume, are:
- Time based (time series, time interval), e.g. power, voltage, current, temperature and humidity
- Geospatial, e.g. person/device location
- Asset specific data
These types of data have special characteristics that need to be catered to. Join this webinar with Cloud Technology Partners Joey Jablonski, VP of Big Data & Analytics and Ken Carroll, VP of IoT, as they discuss some important aspects of how such data can be ingested, modeled, stored and used in IoT solutions.
In financial services, the top big data analytics use cases include customer analytics to understand customer journey using data from all customer interaction channels, predict and avoid customer churn, and fraud and compliance. The financial and corporate benefits of these use cases range from improving customer retention, to hundreds of millions of dollars in incremental revenue and protection of shareholder value.
In this webinar, learn from big data analytics experts:
- Top 3 use cases in financial services
- The importance of applying the appropriate technologies
- The data driven insights that will give companies a competitive edge