October 17, 2022 | 3:00 PM ET
Data mesh is not something enterprises can buy off the shelf. Data mesh is a sociotechnical approach to share, access, and manage analytical data in complex and large-scale environments — within or across organizations. The technical aspect is more architecture than tool or platform, with almost a religious mantra of, “Data mesh is not about technology.” But the number one question data architecture and data engineering teams have about data mesh is, what are the strategies I use to implement it?
Tune into this webinar to learn from guest speaker Michele Goetz, VP of Analytics at Forrester
Organizations have a wide variety of analytics needs ranging from dashboards and visualization to artificial intelligence and machine learning (AI/ML). The faster an organization can perform analytics, the more efficient and effective an organization’s business operations will be.
This webcast features research and best practices presented by David Menninger, SVP and Research Director of Data and Analytics Research at Ventana Research.
In this webcast, we’ll dive into solid Eckerson Group research on how companies across industries are getting their arms around data in multiple clouds and on-prem systems. ThinkData Works is an example of a successful technology company at the center of this important trend. We invite you to learn how ThinkData Works is helping customers pull in new sources and manage external data at scale to reduce risk, boost efficiency, and drive innovation.
Most organizations are moving their analytical data platforms – whether based on data warehouses, data lakes, or both — into the cloud. But how do you choose the right platform to fit your organizational realities, your technology strategy and direction, and important product requirements? What are the compromises in choosing a platform that is only available as a cloud service or only available in one cloud? And what are the capabilities you should look for beyond support for business intelligence and analytics, particularly when it comes to supporting machine learning and data science?
Join Doug Henschen, VP and principal analyst at Constellation Research, and author of “What to Consider When Choosing a Cloud-Centric Analytical Data Platform,” for this informative web event on March 10 at 8 am PT/11 am ET. He’ll be joined by Paige Roberts, Open Source Relations Manager at Vertica, and by Bert Corderman, Senior Manager of Engineering at The Trade Desk.
In this webinar with Mike Leone, Senior Analyst from ESG Global, you’ll learn how customers are increasingly turning to solutions that use object storage as a centralized data repository. Object storage can enable organizations to separate compute and storage and deliver desired levels of elasticity to ensure cost-effective support of all data-centric workloads. And most importantly, object storage can easily be deployed and leveraged, both on-premises and in the cloud, to support an organization’s desire to stay flexible, agile, and hybrid. Whether the requirements call for an ACID-compliant data warehouse or a cost-effective data lake, object store has a place in modern IT infrastructure.
Both Apache Spark and massively parallel processing (MPP) databases are designed for the demands of analytical workloads. Each has strengths related to the full data science workflow, from consolidating data from many siloes, to deploying and managing machine learning models. Understanding the power of each technology, and the cost and performance trade-offs between them can help you optimize your analytics architecture to get the best of both. Learn when using Spark accelerates data processing, and when it spreads far beyond what you want to maintain. Learn when an MPP database can provide blazing fast analytics, and when it can fail to meet your needs. Most of all, learn how these two powerful technologies can combine to create a perfect balance of power, cost, and performance.
June 23rd, 2021
Vertica recently commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study*, which uncovered a three-year customer ROI of 385%, a net present value of $10.1 million and a payback period of less than six months for Vertica’s Unified Analytics Platform.
Join Vertica and our guest from Forrester to explore the framework to evaluate the financial impact of Vertica’s Unified Analytics Platform, covering the benefits, costs, risk, and flexibility that affect the investment decision. This webinar will help you understand the ROI your organization could expect from an investment in Vertica.
June 16th, 2021
As telecommunications operators roll out 5G services across the globe, data analytics will be a critical component for their success. Delivery of 5G and IoT solutions requires near real-time and predictive insights that leverage massive volumes of data from an increasing number of connected devices to automate operational processes.
Vertica and Justin van der Lande, Principal Telecoms Analyst for Data, AI and Development Platforms for Analysys Mason, explore how some of the largest telecoms operators are preparing for the 5G era with analytics.
They will also assess some of the decisions operators will face with regards to analytics moving forward. Key topics include: addressing flexibility in using multi-cloud and hybrid environments, designing for interoperability and openness, operationalizing Machine Learning, and unifying disparate data sources for BI/reporting and predictive analytics.
May 26th, 2021
The promise of unified analytics is to bring scalable, real-time analytics and machine learning to the data wherever it resides. By combining the broadest set of analytical capabilities — such as geospatial, time series, pattern matching, and end-to-end machine learning — with an intuitive visualization environment, unified analytics opens up a new world of possibilities in addressing all analytical use cases. Join industry visionaries and leaders Colin Mahony, CEO of Vertica, and Roman Stanek, CEO of GoodData, in a fireside chat style webcast as they discuss how the future of unified analytics, delivered as Data as a Service (DaaS), can fulfill every organization’s dream of becoming truly data-driven.
March 15th, 2021
About this webinar
Configuration, management, tuning and other tasks can take away from valuable time spent on business analytics. If a platform leads to coding workarounds, non-intuitive implementations and other problems, it can make a big impact on long-term resource usage and cost. A lot of enterprise analytics platform evaluations focus on query price-performance to the exclusion of other features that can have a huge impact on business value, and can cause major headaches if you don’t take them into consideration.
In this webinar, we’ll go beyond price-performance, and focus on everything else needed to modernize your data warehouse.
February 18th, 2021
About this webinar
Today, there’s a lot of buzz about cloud-native databases and how essential their capabilities are when moving data analytics workloads to the cloud. On-premises-only databases are rare, while cloud-only databases are common. What does “cloud-native” actually mean? Does it simply mean “cloud-only?” What’s the real difference, and is it important?
In your planning to move analytical workloads to the cloud, are there specific reasons to prefer a database that can exist both on clouds and on-premises? Or, are there specific reasons to prefer a database built only for the cloud?
In this webinar from our Thought Leadership Series, we’ll deconstruct the propaganda around cloud-native databases and discuss their place in your design of analytical systems.
Attend this webinar to learn how to deal with concerns like GDPR and other privacy regulations, vendor lock-in, pricing predictability, and more in your analytical database design.
November 17th, 2020
About this webinar
The goal of data analytics, whether business intelligence or advanced analytics like machine learning has always been to guide organizations with solid data, rather than feelings. While every company strives to be data-driven, this requires making analytics accessible to more people. What could be more accessible than asking your data a question in your own language? Tune in to learn about natural language processing, the challenges and benefits of this exciting technology, and how it can democratize data analytics, and bring business results to the next level.