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
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.
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.
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.
Organisations often struggle with a range of strategy, people, process and technology topics related to big data. Discover how to reduce data silos, derive insights from data, and deliver meaningful business outcomes for customers across a range of different use cases.Read more >
It is the insights from big data that can be so illuminating. They show the new services that can differentiate your business. They enable you to create the customer-centric organisation by understanding what consumers expect.
From supply chains to business processes, you will have the visibility to improve efficiency, while saving money and cutting risk. The potential result? The right products and services, delivered at the right time – extending your reach to new markets and opportunities.
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!
How do you make sure your data is bit correct in the source and target systems? In this video, learn how the Big Data Compare feature in HVR enables you to make sure your data is correct and in sync.
VP of Field Engineering, Joe deBuzna, explains how the Big Data Compare function works in HVR, why it is important for your business, and how it can identify and mitigate errors.
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"Big data is all about delivering value from advanced analytics and a single version of truth at all scales. It’s time to discuss the growing role of data warehousing within your big data analytics ecosystem. Hear James Kobielus, IBM Big Data Evangelist, outline the major components, tool suites, databases, and industry approaches for leveraging of data warehouses within the astonishingly innovative big-data universe.
View this webinar to learn how to:
- Build a culture that infuses analytics everywhere
- Invest in a big data & analytics platform
- Be proactive about privacy, security and governance"
Big Data analysis is having an impact on every industry today. Industry leaders are capitalizing on these new business insights to drive competitive advantage. Apache Hadoop is the most common Big Data framework, but the technology is evolving rapidly – and one of the latest innovations is Apache Spark.
So what is Apache Spark and what real-world business problems will it help solve? Join Big Data experts from Intel and BlueData for an in-depth look at Apache Spark and learn:
- Real-world use cases and applications for Big Data analytics with Apache Spark
- How to leverage the power of Spark for iterative algorithms such as machine learning
- Deployment strategies for Spark, leveraging your on-premises data center infrastructure
niu Solutions leverages real-time big data analytics through Logtrust’s cloud platform to achieve improved operational efficiencies, log management, predictive analytics, and time to insight. Through Logtrust and key business insights in real-time, niu Solutions continues to:
- Reduce unnecessary business overhead
- Decrease problem resolution time by more than 10x
- Gain competitive advantage with trending and predictive analytics rather than be reactive
- Achieve ROI due to recurring Opex pricing offered by Logtrust
Read the attached niu Solutions Customer Success Story.
Logtrust is a real-time big data analytics platform for business insights, offering Fast Data, Big Data analytics through a solution that enables real-time analytics for operations, fraud, security, marketing, IoT and other aspects of business. Logtrust provides the ability to ingest, store and analyze massive, varied and dynamic data sets at high speed through its flexible cloud, on-premise and hybrid deployments. Please visit https://www.logtrust.com for more information.
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
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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
Being able to set the appropriate context to ask real questions of this massive quantity of data is how companies will spark change that truly matters. And we must be able to do that now, in the moment – and not minutes, days, weeks, or even months later.Read more >
It is easy to talk about the "Data Lake” as the answer to all data storage problems. However, not all Data Lakes are the same, and it is important to choose the right architecture for your data and use cases.
In this webinar, we will explore different Data Lake architectures - logical, storage, analytical etc. - from the point of view of the big data architect and user. We’ll understand the benefits of each, with examples drawn from the real-world experience of Hitachi Vantara in industries like manufacturing and finance.
Attendees will learn not only how to choose the model that works best for them, but will also come away with a sound understanding of the potential for analytics and intelligent applications built on their Data Lake architecture.