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.
Today’s customers expect personalized experiences and there are a plethora of available martech solutions that claim to have the tools to help. And while marketers are able to collect a mass amount of data, it’s becoming increasingly challenging to be able to act on that data, and have one, central repository to be able to make informed decisions and drive more meaningful customer experiences.
A new martech solution has recently appeared into the space that does just that - the Customer Data Platform (CDP). In this webcast, you can expect to learn:
- How these purpose-built products assemble and distribute customer data
- Why CDPs are faster, easier to use, cheaper and more flexible than other solutions
- What problems a CDP can help the marketer solve
- The key ways to using a CDP in driving a business’s digital transformation
David Raab, Founder of the Customer Data Platform Institute
Adam Corey, VP of Marketing at Tealium
Learn about the advantages that come with bringing your data onto one platform in this on-demand webinar, and get a glimpse into Periscope Data’s vision for a unified data platform.Read more >
In this keynote Ashish Thusoo, CEO of Qubole, discusses the gap that enterprises face today when activating their big data. He makes a case for the shift that organizations need to make towards a big data activation strategy in order to put their data assets to use for differentiating and achieving business objectives. The session also covers key elements of big data activation supported by usage trends of Qubole's cloud-native big data activation platform. He presents various ways that enterprises can use to measure their own activation readiness, and demonstrate why Qubole provides the right approach to big data activation.Read more >
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.
With the growing number of data-driven organizations new approaches are needed to drive innovation in scaling and implementing data science. We will discuss how data and data science platforms take advantage of what we are calling DataOps. We will share background on this approach and how it supports putting data science models into production. We will provide best practices and a roadmap on how to implement these techniques to become a leader in machine learning and data science.
Watch the recording of this complimentary webinar with experts from DataScience.com & MapR to:
- Learn about the benefits of applying a DataOps approach to your data science workflow
- Review best practices for how IT teams can support their data science teams
- Hear how customers of MapR and DataScience.com have reaped the benefits of this new approach.
Digital marketers who use Facebook ads have noticed a drop in organic and paid reach due to the algorithm changes. This has resulted in ad costs rising and overall reach decreasing, a bad situation for any digital marketer.
Join our Director of Solutions Engineering as he demonstrates how using a Customer Data Platform (and the first party data you already have) with Facebook custom audiences can increase your reach and decrease your costs.
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
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.
Once you’ve done analysis and found insights in your data, determining the right visualization is crucial to telling the right story. Even if you’ve done the work of selecting the right dataset, asking the right initial question, following up with the right additional queries and using your personal expertise to pinpoint valuable information, all of that analytical work loses value if the charting process is not done correctly.
Choosing the right chart doesn’t need to be intimidating. Similar to data exploration, you just need to ask a series of straightforward questions.
Join Britton Stamper, sales engineer at Periscope Data, when he addresses:
- The first question to ask when building a data visualization
- The different types of data relationships
- When to use each type of chart
- What variables to be aware of when building your chart
This is the third webinar in our “Citizen Data Science Empowerment” series. The first webinar “Getting Started with Citizen Data Scientists” covered the importance of citizen data scientists, how to get them enabled and how to empower them. The second webinar, “How to Do Data Right,” covered the importance of making sure that you are doing your due diligence when evaluating the data to visualize.
The Under Armour built the world's most comprehensive health and wellness application: the Connected Fitness Data Platform. It consists of event streaming pipelines and processing using big data technologies like Hive, Presto and Spark to derive the insights needed to keep their users fit and healthy. Discover their step by step process.Read more >
Today, big data is enabling the advanced analytics that companies have dreamed of for driving their business. And as forward-thinking companies take advantage of big data and advanced analytics to drive digital transformation initiatives, it is forcing the laggards to realize that they will have to do the same if they want to survive.
The generally accepted architectural model for harnessing big data is a data lake. But data lakes, if leveraged simply as cheap storage within which to dump data, will inevitably disappoint. As the saying goes, garbage in, garbage out. Data lakes present unique challenges that must be dealt with if that big data set is going to be turned into actionable information.
So what does it take to succeed with a data lake? Why do some organizations get real value out of big data, while others struggle?
In this webinar, Matt Aslett, Research Director of Data Platform and Analytics at 451 Research and Kelly Schupp, VP of Data-driven Marketing at Zaloni, will discuss ideal data lake use cases such as Customer 360 and IoT. They will also discuss Zaloni’s data lake maturity model with which the data-eager company can chart its ideal course and roadmap.
There was a time when the Enterprise Data Warehouse (EDW) was the only way to provide a 360-degree analytical view of the business. In recent years many organizations have deployed disparate analytics alternatives to the EDW, including: cloud data warehouses, machine learning frameworks, graph databases, geospatial tools, and other technologies. Often these new deployments have resulted in the creation of analytical silos that are too complex to integrate, seriously limiting global insights and innovation.
Join guest speaker, 451 Research’s Jim Curtis and Pivotal’s Jacque Istok for an interactive discussion about some of the overarching trends affecting the data warehousing market, as well as how to build a next generation data platform to accelerate business innovation. During this webinar you will learn:
- The significance of a multi-cloud, infrastructure-agnostic analytics
- What is working and what isn’t, when it comes to analytics integration
- The importance of seamlessly integrating all your analytics in one platform
- How to innovate faster, taking advantage of open source and agile software
We look forward to you joining us.
The Pivotal Team
Data storage, data compute. Data ingestion. Metadata management. Governance. Visibility. Privacy. Transparency. These are just a few of the considerations you must plan for when modernizing your data platform with a data lake. It can be overwhelming, especially if you try to stitch specialized point products together yourself. Data lake implementations can get out of scope and out of control quickly.
Why pull your hair out trying to do it yourself? An actionable data lake is within reach. Join us as Nikhil Goel, Zaloni’s Lead Architect in Product Management, discusses the benefits that a turnkey data lake solution can provide as your data grows to meet your organization. Some of the topics covered will be:
• Storage and compute layers for cloud and on-premises
• Managed ingestion
• Zone-based data architecture
• Self-service access to the data catalog
• Customer success stories
Data engineers today serve a wider audience than just a few years ago. Companies now need to apply machine learning (ML) techniques on their data in order to remain relevant. Among the new challenges faced by data engineers is the need to build and fill Data Lakes as well as reliably delivering complete large-volume data sets so that data scientists can train more accurate models.
Aside from dealing with larger data volumes, these pipelines need to be flexible in order to accommodate the variety of data and the high processing velocity required by the new ML applications. Qubole addresses these challenges by providing an auto-scaling cloud-native platform to build and run these data pipelines.
In this webinar we will cover:
- Some of the typical challenges faced by data engineers when building pipelines for machine learning.
- Typical uses of the various Qubole engines to address these challenges.
- Real-world customer examples
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
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.
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.
From chat bots, to recommendation engines, to Google Voice and Apple Siri, AI has begun to permeate our lives. In this keynote, IBM's Sumit Gupta demystifies what AI is, presents the differences between machine learning and deep learning, explains why the huge interest now, shows some fun use cases and demos, and then discusses use cases of how deep learning based AI methods can be used to garner insights from data for enterprises. Sumit also talks about what IBM is doing to make deep learning and machine learning more accessible and useful to a broader set of data scientists.Read more >
Every CEO aspires to create a data-driven culture that can activate 100s or 1000s of users and petabyte-scale data to continuously deliver true business value. This keynote panel explores the journey of 4 companies: Comcast, Qubole, Fanatics and MediaMath, that have chronicled their successes and challenges in two books by O’Reilly Media about Creating a Data-Driven Enterprise. The panelists talk not just about their technology strategy and choices but also how data-driven insights are powering their business and transforming the competitive dynamics of their industry.Read more >
This is the second webinar in our “Citizen Data Science” series. Our first webinar, “Getting Started with Citizen Data Scientists,” covered the importance of citizen data scientists, how to get them enabled and how to empower them.
To some citizen data scientists, data is a new language that they’re unfamiliar with translating into insights. For others, they’re eager to dive in and curious to explore, but reluctant to communicate their findings because they don’t have experience translating data into tactical operations.
These new business-focused analysts need to be confident that they’re utilizing data properly. To empower these new data dives, Kyle Dempsey, senior professional services engineer at Periscope Data pulled together a collection of tips to make sure first-time analysts are doing data right.
Join Kyle on September 27th, 2018 at 10 a.m. PDT as he walks through how to:
- Define your analysis and outcomes
- Understand what data is available and know how to ask for more
- Ask questions in a way that allows data to answer them
- Use data to inform decisions
- Enable collaboration between technical and nontechnical teams