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
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!
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
With new technologies such as Hive LLAP or Spark SQL, do you still need a data warehouse or can you just put everything in a data lake and report off of that? No! In the presentation, James will discuss why you still need a relational data warehouse and how to use a data lake and an RDBMS data warehouse to get the best of both worlds.
James will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. He'll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution, and he will put it all together by showing common big data architectures.
Selling your house in the financial crisis-stricken Greece is up to this day a great ordeal. When faced with such a challenge, I was baffled by the sparsity of conclusive data on land value at my birthplace city, Thessaloniki. Embarking on a personal mission and collecting and processing more than 10K online housing ads together with open data, I managed to render an insightful interactive visualization of the actual real estate values on borough and city block level that was published through the Greek media. Join me on this thought process journey to find out how to
o Gather vast online data with simple scripting
o Combine your data with open data into meaningful structures
o Create interactive data visualizations that have an actual impact @ infographeo.com
This will be an interactive session, so please feel free to bring your thoughts and questions to share during the session.
Data visualization requires data to be prepared before any meaningful analysis can be conducted. Finding insights, making correct observations and taking actions to drive outcomes therefore don't just depend on the way information is communicated but also on the preparation preceding the analysis.
In this webinar we discuss the key steps for data preparation to enable effective analysis and visual exploration of the data. We will show practical examples from projects we have worked on as well as share some simple data preparation ideas from our Makeover Monday challenges.
Lastly, we will show an example of how data preparation can enrich a dataset and enable further analysis.
This is the age of data science. We have more data, computing power and software packages than ever before, and we’re driving real value with data science. But challenges remain: fragmented and dirty data, collaboration issues, and long project cycle times.
Keys to success: a ‘data-first’ approach, enabling collaboration, and a focus on prediction. Cloudera Data Science delivers the unified platform you need for rapid time to value with the most advanced machine learning techniques, including deep learning.
Do you need to combine data from multiple sources to get business insights? Do you know if the data you rely on is always accurate and up-to-date? Do you want to have insights quicker to meet business needs? Do you rather spend time on strategic tasks than maintaining the data warehouse?
Crunchbase also experienced these pain points. With over 31 million visitors to their website each year, Crunchbase collects and uses an incredible amount of data, and therefore needs a powerful analytics platform to aggregate all the data to ask the right questions. Since deploying Periscope Data Warehouse, Crunchbase was able to take their analytics to the next level by allowing them to leverage all their data — from their marketing stack to Salesforce to website impression data — and to build a comprehensive view of their business and customers.
Join Ryan Seagar, Head of Sales Engineering of Periscope Data, as he presents a live 30-minute demo and the Crunchbase case study on how Periscope Data Warehouse enables data teams streamline their entire analytics workflow — from data ingestion to analysis and reporting, offloading the mundane maintenance tasks while still maintaining full control and visibility .
Do you know what your top ten 'happy' customers look like? Would you like to find ten more just like them? Come learn how to leverage 1st & 3rd party data to map your customer journey and drive users down a path where every interaction is personalized, fun, & data-driven. No more detractors, power your Customer Experience with data!
In this webinar you will learn:
-When, why, and how to leverage 1st, 2nd, and 3rd party data
-Tips & Tricks for marketers to become more data driven when launching their campaigns
-Why all marketers needs a 360 degree customer view
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.
Paul Bruton discusses the move to a holistic approach to next gen data management. Looking at digital transformation strategies, he explains how Hitachi Vantara’s object storage can address common challenges - from cloud complexity to data governance and compliance - with its advanced custom metadata architecture to make data more intelligent.Read more >
Jason Hardy speaks about the evolution of the Hitachi Content Platform. Focusing on the latest addition to the portfolio, Hitachi Content Intelligence (HCI), he explains how it delivers a superior enterprise search experience. Learn how HCI can process and discover information from multiple data streams and find meaningful correlations between that data to enable data-driven decision-making.Read more >
When monitoring an increasing number of machines, the infrastructure and tools need to be rethinked. A new tool, ExDeMon, for detecting anomalies and raising actions, has been developed to perform well on this growing infrastructure. Considerations of the development and implementation will be shared.
Daniel has been working at CERN for more than 3 years as Big Data developer, he has been implementing different tools for monitoring the computing infrastructure in the organisation.
The Data Centre Maturity Model (DCMM) was created by The Green Grid and can help assess the current and desired states of your data centre(s), highlighting areas that could be enhanced using a maturity modeling approach.
*Please note: this webinar will be presented in Dutch.
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.