On-Demand Webinars
Access On-Demand Open Data Lake Webinars
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1:16:34
Qubole Virtual Masterclass: Data Analytics & Machine Learning for Financial Services
On-demand recording from the Qubole Virtual Masterclass hosted on 09.24.2020.
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41:26
Four Ways To Optimize Cloud Data Lake Cost
Conducting ad-hoc analytics, streaming analytics and machine learning workloads in the cloud offers unique cost, performance, speed, time to value, and accessibility advantages. However, data in the c
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21:06
Maximizing Spot Utilization & Minimizing Job Loss - On-Demand Webinar
Join Sandeep Dabade, Lead Solution Architect at Qubole, to explore how to avoid reliability issues, delays, troubleshooting problems, and cost overruns for your AWS Spot jobs. Sandeep is joined by Dh
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2:09:57
Virtual Workshop: Streaming Analytics with Hive ACID
Capturing data and making it available within an organization quickly will be a differentiator for companies in the modern data era. For instance, a customer could be interacting with a bank's website
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41:53
Winning Open Data Lake Architectures - Ad-hoc Analytics, Streaming Analytics & Machine Learning
Get closer to winning open data lake architectures, fast forward to the use case that you'd like to explore: Ad-hoc Analytics - 07:50 - 18:27 Streaming Analytics - 21:20 - 30:54 Machine Learning - 34
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59:48
The Open Data Lake Talks Optimizing Costs in A Changing World
As organizations grapple with the sudden economic turmoil created by the pandemic, there is a critical need to balance cost savings with the need to drive innovation. Join Justin Wainwright, Systems
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49:14
Right Tool for the Job: Running Apache Spark at Scale in the Cloud
Apache Spark is powerful open source engine used for processing complex, memory-intensive workloads. However, running Apache Spark in the cloud can be complex and challenging. Qubole has re-engineered
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57:57
Right Tool for the Job: Using Qubole Presto for Interactive and Ad-Hoc Queries
Presto is the go-to query engine of Qubole customers for interactive and reporting use cases due to its excellent performance and ability to join unstructured and structured data in seconds. Many Qubo
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41:51
Best Practices: How To Build Scalable Data Pipelines for Machine Learning
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
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46:30
Key Differences Between On-Prem and Cloud Data Platforms
Cloud service models have become the new norm for enterprise deployments in almost every category — and big data is no exception. The separation of storage and compute in the cloud afford unparalleled
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56:58
Enterprise-Scale Big Data Analytics on Google Cloud Platform
As companies scale their data infrastructure on Google Cloud, they need a self-service data platform with integrated tools that enables easier, more collaborative processing of big data workloads. Jo
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35:14
Qubole On-Demand: Discover our Open Data Lake Platform
Watch this on-demand demo to learn how the most data-driven organizations are able to significantly enhance TCO, performance and optimization of their cloud data lakes with Qubole. This session is fo
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59:18
Data Lake and Data Warehouse: Collision or Synergies? With John Riewerts, VP Engineering, Acxiom
Data warehouses support reporting and analytics on historical data while data lakes support newer use cases that leverage data for machine learning, predictions, and real-time analysis. The question i
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59:33
Inside the Brain of Cloud Data Platform Leaders: Ep. 1: Addressing GDPR & CCPA
In this episode, we’re evaluating the SaaS/PaaS data platform from a CCPA and GDPR perspective. We’ll hear from subject experts: - Drew Daniels, CISO, Qubole - Akil Murali, Director, PM Data Governan
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56:52
Data Lake And Data Warehouse: Collision or Synergies? Featuring Pharmeasy & Swiggy
The data that organizations collect and process must be stored in a way that allows them to leverage it: to report on the past, to understand the present, and to predict the future. Data warehouses su
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