Name: Bringing Big Data to Enterprise BI with Starburst & ThoughtSpot
Start: 2022-03-22T19:00:00Z
End: 2022-03-22T19:00:28.000Z
Location: BrightTALK
Rating: 0

Starburst’s mission is to free our customers to see the invisible and achieve the impossible. Join us for high value content, insightful conversations, and the constant opportunity to learn. 

In today's landscape, digital transformation to provide seamless customer journeys is critical to long-term success. Retailers and consumer packaged goods (CPG) companies need to leverage data to drive insights, remain competitive, and build excellent, direct-to-consumer experiences. Analysts grapple with the sheer volume of data generated every second in modern retail, especially with E-commerce experiences expanding in the industry. With increasing pressure to innovate and scale, it can be challenging for organizations to digitally transform efficiently and effectively to meet customer demands. 

Join us to learn how retail and CPG organizations can place Starburst at the heart of their data strategy to revolutionize customer experiences and mitigate operational gaps.

In this webinar, we'll cover:

-Digital transformation revolutionizing the Retail CPG industry
-How innovators are rising to the challenge  
-How Starburst Data Lake Analytics Platform can support your organization’s customer data strategy for long-term success

Revolutionizing the Customer Journey in Retail & CPG

Watch on-demand for an engaging session filled with expert insights and live Q&A. Take the first step towards maximizing query performance with Starburst Enterprise’s managed statistics.

Maximizing query performance with managed statistics in Starburst Enterprise

According to the Sixth Annual Gartner Chief Data Officer Survey, CDOs who successfully increased data sharing led data and analytics (D&A) teams that were 1.7 times more effective at showing demonstrable, verifiable value to D&A stakeholders. 

Data products abstract away the complexity of data storage for consumers. For data engineers, however, it makes sense to take advantage of AWS tools and capabilities to optimize for speed and efficiency. Utilizing the power of Starburst Galaxy, you can operationalize an AWS data lake and manage it for the purpose of data analytics. Starburst Galaxy provides fast access and flexible data product management without adding the complexity of data movement.

Implementing a data lake house architecture with Starburst Galaxy on AWS capitalizes on the low-cost object storage of Amazon Simple Storage Service (Amazon S3) and the ability to load all types of data, while implementing the data warehousing principles of performance, reliability, and ease of use. A data lakehouse allows you to optimize your data architecture to meet specific organizational needs through the balance of cost-based optimizations and scalability, while also implementing a reporting structure to operationalize your analytics. At the same time, because Starburst can connect to and query multiple modern and legacy enterprise sources, it allows data lake users to only pay for what they use and minimize data duplication.


In this webinar, we'll cover:

-What are data products? 
-Imperatives for building great data products 
-Benefits for Data Producers and Consumers 
-Best practices for data products creation and usage on AWS
-A product demo

Extracting the Full Business Value of Data with Starburst Data Products on AWS

The move to the cloud and pay-as-you-go consumption models give IT leaders more flexibility to scale expenses upward or reduce them downward. But when you’re running an application in the cloud, that’s just part of the application’s total workload. Starburst Warp Speed sets a new benchmark in data lake analytics, empowering organizations to more quickly and efficiently derive greater insights from their data. 

In this webinar, Russell Christopher, Director of Product Strategy, and Guy Mast, Product Manager, will demonstrate a simplified environment wherein a single infrastructure significantly reduces costs and improves query response times with:

Speed query performance with smart indexing ensuring data is active and available for analysis, reducing query response times up to 7x.
Adapt to business requirements with elastic resource management that can automatically cache frequently accessed data to speed performance, and optimize for cost. 
Reduce operational costs with workload-level monitoring by detecting hot data and bottlenecks, saving customers up to 40%+ cloud compute cost reduction.

Warp speed - Setting a New Standard of Data Lake Analytics

As companies build their data analytics practice, they quickly outgrow running analytics off their operational store that powers their applications. Building a read replica only buys them time until they hit scalability limits with their growing internal and customer demand. This is where one hits the crossroads of going all in with a cloud data warehouse or choosing an open data lake house approach to future-proof them for scale, performance, and cost efficiency. In this workshop, Matt Fuller and Tom Nats lead you through how you can easily build and manage an open data lake house architecture using open-source technologies such as Trino and Apache Iceberg to support your growing analytics. Trino is an open source highly parallel and distributed query engine built from the ground up at Facebook for efficient, low-latency analytics. Iceberg is an open source, high performant table storage format that enables an engine like Trino to perform data warehousing SQL functionality such as UPDATE, DELETE, and MERGE commands on the data lake house. In addition, Matt and Tom will lead you through combing these technologies to perform near real-time analytics with streaming ingestion with database functionality on the lakehouse. This workshop will use the Starburst Galaxy SaaS product making it simple to leverage these technologies for your modern data lake house without having to worry about the operational aspects of running Trino and other software.

Building an Open Data Lake House Using Trino and Apache Iceberg

Building a modern lakehouse has become easier with new technological advances bringing database-type functionality to the data lake. In this session, we’ll discuss the history of data lakes, the importance of the advances in open table formats and query engines in the last few years, and where we predict the future will lead us in providing self-serve analytics to enterprises of all sizes. 

Topics covered: 
How do we define a data lakehouse
How to start a data lakehouse strategy 
Implementing a data lakehouse

Building a Modern Lakehouse

As data architectures evolve there is a big question to answer: How do we best evolve people, too? Is it more pragmatic to adopt a “Modern Data Stack” which is iterative and requires less people and process change? Is it better to take a more holistic approach, which Data Mesh proposes, pushing people & technology forward at once? What is the best next step toward companies becoming data-driven?


This fireside chat, moderated by Justin Borgman, Starburst Co-Founder & CEO, will bring the stars of the data world to the table to discuss the hard and soft side of data: People and technology.

Data disrupted: A fireside chat

In a landmark research study by Boston Consulting Group, the macro trends shaping the data & analytics imperative are explored. As analytics use cases proliferate, and more data is created, how will companies illuminate dark data and make it easy to consume? Given today's economic uncertainty, how can companies solve for faster data consumption while finding a path to analytics cost control?

Join Pranay Ahlawat, Partner and Associate Director, Enterprise Software & Cloud at BCG as he presents their latest findings on how companies can derive the most value from an organization's single most important asset: Data. Pranay will be joined by Steven Huels, Senior Director, AI Product Management and Strategy at Red Hat, and Adrian Estala, Field Chief Data Officer at Starburst.

The future of data: A BCG study

What is Data Mesh? Join Starburst for an introduction into this modern approach to managing analytics at scale. This is the first installment of a series on Data Mesh. Coined by Zhamak Dehghani, Principal Technology Consultant at ThoughtWorks, Data Mesh embraces decentralization over-centralization, meaning it allows companies to become more efficient in accessing and exploiting data as a core architectural approach. Data Mesh addresses the flaws in monolithic data warehouses models. 
We’ll cover:

The foundations of Data Mesh, what it is and how it works
How to rethink organizational, architectural, and technological assumptions to get the best out of your data team and your data. 
Why Starburst and Trino are essential to your Data Mesh 
Starburst is the analytics engine for Data Mesh. If you are moving towards adopting a Data Mesh architecture we want to be there to help.

Data Mesh 101: What It Is & Why You Need It

Winning the War for AI Talent

The Essentials to Data Science Success within Enterprises

The Human Data Democracy

Leveraging Big Data Analytics & Visualization for transformational growth

Using automated visualizations to ensure confidence in your data

The Art of Storytelling through Engaging Visuals

Building Commercial Products using BI and Visual Analytics

Data Fabric for Analytics & Cloud : A Telco Realty

How to Unlock the Value of Data in Your Business

Modernizing Business Intelligence Creates Path to Data Leadership

Data Visualization & Analytics for Business Intelligence

Seeing the Forest and the Trees: Data Discovery in Billion Row datasets

Trends in Big Data and Analytics in 2022

Let’s face it. For all the incredible innovations in modern BI, we still face limitations when it comes to live connections to large enterprise sources. Starburst provides the single point of live access to all enterprise data, wherever it resides, through Trino’s ANSI-SQL MPP engine. With big data access, and used in combination with ThoughtSpot, users can query and analyze billions of rows of data across many sources at speeds thought unimaginable. Starburst and ThoughtSpot give business analysts the tools to ask new questions with easy expanded access to new data sources.

This session with Tom Nats, Director of Customer Solutions at Starburst, along with Sean Zinsmeister, VP of Product Marketing at ThoughtSpot will share how ThoughtSpot, used in conjunction with Starburst, opens new avenues by incorporating new data sources and performing high-speed queries at scale.

Bringing Big Data to Enterprise BI with Starburst & ThoughtSpot

Big Data

Big Data Analytics

Big Data Management

Enterprise Datacenter

Data Analytics

Data Science

Innovation

Data

Enterprise Applications

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Bringing Big Data to Enterprise BI with Starburst & ThoughtSpot

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