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 >
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 >
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
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.
Aborder un projet Big Data avec comme unique préoccupation d’alimenter le data lake est à la fois restrictif et dangereux pour tenir le budget et les délais de livraison. Prendre en compte la sécurité des données, leur qualité et leur gouvernance dès le départ assurera la bonne conduite du projet et inscrira durablement ces nouvelles technologies dans le SI.Read 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.
Data lakes are centralized data repositories. Data needed by data scientists is physically
copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment.
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
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!
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.
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
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
This 1-hour webinar from GigaOm Research brings together leading minds in cloud data analytics, featuring GigaOm analyst Andrew Brust, joined by guests from cloud big data platform pioneer Qubole and cloud data warehouse juggernaut Snowflake Computing. The roundtable discussion will focus on enabling Enterprise ML and AI by bringing together data from different platforms, with efficiency and common sense.
In this 1-hour webinar, you will discover:
- How the elasticity and storage economics of the cloud have made AI, ML and data analytics on high-volume data feasible, using a variety of technologies.
- That the key to success in this new world of analytics is integrating platforms, so they can work together and share data
- How this enables building accurate, business-critical machine leaning models and produces the data-driven insights that customers need and the industry has promised
- How to make the lake, the warehouse, ML and AI technologies and the cloud work together, technically and strategically.
Register now to join GigaOm Research, Qubole and Snowflake for this free expert webinar.
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.
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
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.
Today's enterprises need broader access to data for a wider array of use cases to derive more value from data and get to business insights faster. However, it is critical that companies also ensure the proper controls are in place to safeguard data privacy and comply with regulatory requirements.
What does this look like? What are best practices to create a modern, scalable data infrastructure that can support this business challenge?
Zaloni partnered with industry-leading insurance company AIG to implement a data lake to tackle this very problem successfully. During this webcast, AIG's VP of Global Data Platforms, Carlos Matos, and Zaloni CEO, Ben Sharma will share insights from their real-world experience and discuss:
- Best practices for architecture, technology, data management and governance to enable centralized data services
- How to address lineage, data quality and privacy and security, and data lifecycle management
- Strategies for developing an enterprise-wide data lake service for advanced analytics that can bridge the gaps between different lines of business, financial systems and drive shared data insights across the organization
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.
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.