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.
Providence Health Plan has grown organically over the last 30+ years. With the adoption of the Affordable Care Act as well as growth in other lines of business, the organization was at a crossroad. As new applications and vendors were introduced into the ecosystem to respond to the business’s need, system complexity had increased substantially. The result was a data management challenge that would limit ongoing business success if it remained unsolved.
Join Jaydeep Ghosh, Director of Data Services at Providence Health Plan, and Monica Mullen, Principal Solutions Marketing Manager at Informatica, as they discuss the enterprise data roadmap that the organization put together to address the current state, position the health plan for future growth, and address these top challenges:
• Time to business value
• Multiple, but incomplete, versions of the truth
• Overall lack of trust in data
How confident are you about successfully using Hadoop for your data management needs? Despite 60-80% of organizations experimenting with new big data technologies, only a few have been able to successfully extract value sustainably.
In this webinar, Navin Parmar, a highly experienced healthcare data management leader and longtime Informatica PowerCenter customer, will share the best practices he has gathered across multiple projects from modernizing data environments beyond traditional data warehousing and successfully leveraging big data technologies. Learn real world lessons from an experienced industry practitioner who has delivered compelling healthcare analytics outcomes, while ensuring regulatory compliance with completely trusted data.
This webinar is part of BrightTALK's Ask the Expert Series.
Join Christopher Brown, CTO of Uptime Institute and Kelly Harris, Senior Content Manager at BrightTALK, as they take a technical deep dive into data center infrastructure management in 2018.
Chris will answer questions related to trends from the field:
- What really makes a well-run data center?
- The changes we are seeing in the industry
- What Tier level do I need for my data center(s)?
- What can you tell us about the typical issues we see every day?
- What are the challenges ahead for data centers?
Audience members are encourage to send questions to the expert which will be answered during the live session.
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 >
Anyone who's ever analyzed data knows the pain of digging in only to find that it is poorly structured, full of inaccuracies, or just plain incomplete. But "dirty data" isn't just a pain point for analysts; it can have a major financial and cultural impact on an organization.
Attend this live webinar to learn four actionable ways to overcome common data preparation issues including how to establish a company standard for "clean data" and how to democratize data prep across your organization.
- Andy Cotgreave, Technical Evangelism Director, Tableau
- Jason Harmer, Data Analytics and Visualization Consultant, Nationwide Insurance
- Gordon Strodel, Information Management and Analytics Consultant, Slalom
Your journey to cloud can take many forms. For example, you may move your whole data center or just single applications and databases. You may move existing solutions or build new green-field ones on the cloud, such as Hadoop implementations and data lakes. And you may evolve into a cloud-only architecture or one that’s a hybrid mix of multiple platforms on clouds and on premises. All of these journeys involve a migration of massive amounts of diverse data, and so require substantial data management infrastructure, tools, and best practices, during both development and production. TDWI’s Philip Russom will tell you what data management best practices to pack for success in your journey to cloud, regardless of the path you take.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 >
D'ici à 2020, 50% des entreprises mettront en œuvre une forme de virtualisation des données comme une option pour l'intégration de données", selon le cabinet d’analystes Gartner. La virtualisation des données ou data virtualization est devenue une force motrice pour les entreprises pour la mise en œuvre d’une architecture de données d'entreprise agile, temps réel et flexible.
Denodo s’associe à Philippe Nieuwbourg, analyste BI et data et fondateur de la communauté Decideo, pour faire le point lors d’un webinar sur une approche innovante et disruptive de l’intégration de données : la Data Virtualization.
Au sommaire :
• Introduction by Philippe Nieuwbourg, Decideo
• Denodo et son positionnement sur le marché de la Data Virtualization
• Les principales fonctionnalités
• Les principaux cas d’usage. Présentation d'un cas client : comment Intel a repensé l’architecture de ses données avec la Data Virtualization
• Les ressources
Is Your Data Ready for GDPR?
As the deadline for GDPR approaches, it is time to get practical about protecting personal data.
We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.
A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.
Watch, as experts from MAPR and Talend show you how to:
- Diagnose the maturity of your GDPR compliance;
- Set up milestones and priorities to reach compliance;
- Create a foundation to manage personal data through a data lake;
- Master compliance operations - from data inventory to data transfers to individual rights management.
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
In this webinar, John L. Myers of Enterprise Management Associates (EMA) and Jack Norris of MapR will discuss how the new business advancements require data-rich applications that enable global, real-time data integration, microservices support, and in-place and continuous machine learning/AI and SQL capabilities.
Watch this video to learn:
- Examples of disruptive business models
- Drivers of changes to the management landscape
- Best practices associated with meeting requirements for data-driven applications
IBM and Aberdeen discuss Data Management in the CloudRead more >
The need for businesses to become more agile and lead intelligent disruptions using data has never been stronger. To help you unleash the disruptive power of data, Informatica reimagines data management with its latest release of 10.2, powered by the CLAIRE™ engine using metadata-driven Artificial Intelligence (AI). Informatica Release 10.2, provides an intelligent, scalable and integrated platform for managing any data across your enterprise to accelerate data-driven digital transformation.
Discover how AI enhances the intelligent capabilities of key products and solutions in 10.2 and deep dive into these offerings:
-Industry leading enterprise data catalog
-Out-of-the-box, end-to-end data governance and compliance
-Cloud data lake management and real-time intelligent streaming
-Detect and protect critical data with intelligent data security
-Enterprise-scalable hybrid and multi-cloud deployments
Achieving actionable insights from data is the goal of any organization. To help in this regard, data catalogs are being deployed to build an inventory of data assets that provides both business and IT users a way to discover, organize and describe enterprise data assets. This is a good first step that helps all types of users easily find relevant data to extract insights from.
Increasingly, end users want to take the next step in provisioning or procuring this data into a sandbox or analytics environment for further use. Attend this session to see how organizations are looking to build actionable data catalogs via a data marketplace, that allow self-service access to data without sacrificing data governance and security policies.
Learn how to provide governed access and visibility to the data lake while still staying on track and within budget. Join Scott Gidley, Zaloni’s Vice President of Product, as he discusses:
- Architecting your data lake to support next-gen data catalogs
- Rightsizing governance for self-service data
- Where a data catalog falls short and how to address
- Success use cases
The modern, data-rich enterprise demands access to data at a pace that has outclassed traditional data management platforms. Whether they are utilizing a cloud, hybrid, or on-prem solution, these organizations require capabilities that are vendor-neutral and often implemented with microservices to ensure an agile environment at scale.
In this webinar, Scott Gidley, Zaloni’s Vice President of Product, will showcase the latest version of the Zaloni Data Platform. This version provides exciting new features to address the growing demands of data-driven companies, including:
- Managing hybrid and multi-cloud environments
- Managing your data with zones
- Cloud-native support
- Ingestion wizard
- Platform global search
- Persona-driven homepage
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.
As data is growing at an exponential rate, organizations are increasingly looking to leverage streaming data from mobile devices, wearable technology and sensors for real-time processing and analytics. Gartner estimates that, “By 2020, 70% of organizations will adopt data streaming to enable real-time analytics.” However, implementing real-time data ingest, processing and delivering insights at scale requires infrastructure with zero latency and easy access to information when it is required.
In the webinar, we’ll discuss:
- Adopting Modern Data Lake with the Hortonworks Data Platform (HDP)
- Accelerating real-time data analytics with Hortonworks Data Flow (HDFTM) and Attunity to build a data lake
- Solving challenges with real-time data ingest and managing data in motion workloads
Join subject matter experts from IBM and Hortonworks for a joint webcast to help you accelerate real-time data analytics and manage your data workloads efficiently.
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 >
Consumers are engaging with brands across multiple touchpoints, channels, and devices, generating massive amounts of valuable data. Organizations are quickly adopting a number of solutions to keep up with this explosion of customer data and better capture and correlate user behavior.
Two common solutions brands are leveraging to house and analyze all of this customer data are Enterprise Data Warehouses (EDW) and Data Lakes. Register now for this 30-minute webinar and learn:
- Key benefits of each and which is best for your brand
- Why pairing your enterprise data storage solution with customer data initiatives makes your tech stack even more powerful
- How an automated data supply chain fits in a modern EDW and data lake environment
- And more!
The webinar will conclude with a live Q&A Chat with questions from the audience on all things enterprise data storage.
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.
The verdict is in. Data is now broadly perceived as a source of competitive advantage. No wonder many organizations view their Analytics initiative as highly strategic. Yet, many Analytics initiatives fail to deliver their promised value. Pretty visualizations and dashboards are only as good as their underlying unrefined data. Simply put: ‘garbage in-garbage out’. What is needed is great data.
However, business leaders often fail to recognize the inherent complexities in building and maintaining a great data foundation for Analytics. Oversimplification leads to disappointing Analytics initiatives, and hence bad decision making.
In order to deliver great data you need to:
•Integrate data from many different systems, on-premise, in Hadoop, or in the cloud
•Combine data from all the different data sources
•Ensure the data is of the highest quality
•Operationalize a repeatable process for generating and modifying reusable reports at the speed of business.
In this webinar, hosted by David Lyle of Informatica, Philip Russom of TDWI will walk us through the potential data pitfalls, which a corporation should consider when designing a successful Analytics effort. Philip will share best practices for managing data, in order to promote an Analytics initiative that is truly based on great data. And David will discuss how Informatica can help you make better decisions with great data that’s refined, so you spend more time analyzing and less time finding and fixing data errors.
The data contained in the data lake is too valuable to restrict its use to just data scientists. It would make the investment in a data lake more worthwhile if the target audience can be enlarged without hindering the original users. However, this is not the case today, most data lakes are single-purpose. Also, the physical nature of data lakes have potential disadvantages and limitations weakening the benefits and possibly even killing a data lake project entirely.
A multi-purpose data lake allows a broader and greater use of the data lake investment without minimizing the potential value for data science or for making it a less flexible environment. Multi-purpose data lakes are data delivery environments architected to support a broad range of users, from traditional self-service BI users to sophisticated data scientists.
Attend this session to learn:
* The challenges of a physical data lake
* How to create an architecture that makes a physical data lake more flexible
* How to drive the adoption of the data lake by a larger audience
Ready access to research data is a cornerstone for success in science. Researchers need to keep track of their data & improve its impact through increased re-use. Universities want to eliminate re-work, showcase research outputs & improve collaboration inside and outside to drive research performance. These needs become more urgent as international funding bodies revise their policies to encourage, or even mandate, institutions and their researchers to make research data available.
In this webinar we will introduce Mendeley Data, a platform designed to facilitate the comprehensive utilization of data. Consisting of five modules, this open, cloud-based platform helps research institutions to manage the entire life-cycle of research data, and enables researchers to safely access and share information wherever they are.