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Qubole

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  • Modern Data Engineering and The Rise of Apache Airflow
    Modern Data Engineering and The Rise of Apache Airflow Prateek Shrivastava, Principal Product Manager, Qubole Recorded: Sep 11 2018 48 mins
    Storage and compute are cheaper than ever. As a result, data engineering is undergoing a generational shift and is no longer defined by star-schema modeling techniques on data warehouses. Further, downstream operations are not just BI reporting and now include emerging use-cases such as data science. This means that modern day ETL tools should be dynamic, scalable, and extensible enough to handle complex business logic.

    Airflow provides that level of abstraction today’s Data Engineers need. The Qubole Data Platform provides single-click deployment of Apache Airflow, automates cluster and configuration management, and includes dashboards to visualize the Airflow Directed Acyclic Graphs (DAGs).

    In this webinar we will cover:
    - A brief Introduction to Apache Airflow and its optimal use cases
    - How to remove the complexity of spinning up and managing the Airflow cluster
    - How to Scale out horizontally with multi-node Airflow cluster
    - Real-world customer examples
  • Deep Learning with TensorFlow on Qubole
    Deep Learning with TensorFlow on Qubole Piero Cinquegrana, Sr. Data Science Product Manager, Qubole Recorded: Sep 4 2018 43 mins
    Deep learning works on large volumes of unstructured data such as human speech, text, and images to enable powerful use cases such as speech-to-text transcription, voice identification, image classification, facial or object recognition, analysis of sentiment or intent from text, and many more. In the last few years, TensorFlow has become a very popular deep learning framework for image recognition and speech detection use cases.

    All deep learning methods, including TensorFlow, require large volumes of data to train the model. Today, the most significant challenge in deep learning is the ever-increasing training time — as models get more complicated, the size of training data continues to increase. In order to address this challenge, cloud providers have launched instance types with many graphics processing units (GPUs) in a single node. However, using all of the GPUs in a single training job is not trivial. Qubole’s TensorFlow engine has been built to run on distributed Graphics Processing Units (GPUs) on Amazon Web Services.

    In this webinar we will:

    - Discuss how Qubole has achieved single-node, multi-GPU parallelization using native Tensorflow and Keras with Tensorflow as a backend.
    - Present results from our studies that show how training time varies with the number of GPUs in the cluster.
    - Run through a demo of a TensorFlow use case on Qubole.
  • The Power of Presto for Analytics and Business Intelligence (BI)
    The Power of Presto for Analytics and Business Intelligence (BI) Goden Yao, Principal Product Manager at Qubole Recorded: Aug 30 2018 49 mins
    Presto is a distributed ANSI SQL engine designed for running interactive analytics queries. Presto outshines other data processing engines when used for business intelligence (BI) or data discovery because of its ability to join terabytes of unstructured and structured data in seconds, or cache queries intermittently for a rapid response upon later runs. Presto can also be used in place of other well known interactive open source query engine such as Impala, Hive or traditional SQL data warehouses.

    Qubole Presto, a cloud-optimized version of open source Presto, allows for dynamic cluster sizing based on workload, and terminates idle clusters — ensuring high reliability while reducing compute costs. Qubole customers use Presto along with their favorite BI tools, including PowerBI, Looker, Tableau, or any ODBC- and JDBC-compliant BI tool, to explore data and run queries.

    In this webinar, you’ll learn:
    - Why Presto is better suited for ad-hoc queries than other engines like Apache Spark
    - How to jumpstart analysts across your organization to harness the power of your big data
    - How to generate interactive or ad hoc queries or scheduled reports using Qubole and Presto
    - Real-world examples of companies using Presto
  • Accelerate The Time To Value Of Apache Spark Applications With Qubole
    Accelerate The Time To Value Of Apache Spark Applications With Qubole Ashwin Chandra Putta, Sr. Product Manager at Qubole Recorded: Aug 28 2018 50 mins
    Apache Spark is powerful open source engine used for processing complex, memory-intensive workloads to create data pipelines or to build and train machine learning models. Running Spark on a cloud data activation platform enables rapid processing of petabyte size datasets.

    Qubole runs the biggest Spark clusters in the cloud and supports a broad variety of use cases from ETL and machine learning to analytics. Qubole supports a performance-enhanced and cloud-optimized version of the open source framework Apache Spark. Qubole brings all of the cost and performance optimization features of Qubole’s cloud native data platform to Spark workloads.

    Qubole improves the performance of Spark workloads with enhancements such as fast storage, distributed caching, advanced indexing, metadata caching, job isolation on multi-tenant clusters. Qubole has open sourced SparkLens, a Spark profiler that provides insights into Spark application that help users optimize their Spark workloads.

    In this webinar, you’ll learn:

    - Why Spark is essential for big data, machine learning, and artificial intelligence
    - How a cloud-native platform allows you to scale Spark across your organization, enable all data users, and successfully deploy AI and ML at scale
    - How Spark runs on Qubole in a live demo
    - Real-world examples of companies using Spark on Qubole
  • Introduction to Qubole: A Data Platform Built To Scale
    Introduction to Qubole: A Data Platform Built To Scale Mohit Bhatnagar, SVP of Product at Qubole Recorded: Aug 23 2018 57 mins
    Many companies today struggle to balance their users’ demands for data with the cost of scaling their data operations. As the volume, variety, and velocity of data grows, data teams are getting overwhelmed and traditional infrastructure is being pushed to the brink.

    In this webinar, Qubole SVP of Product Mohit Bhatnagar will share how Qubole’s cloud-native platform helps companies scale their data operations, activate petabytes of data, and reach administrator-to-user ratios as high as 1:200 (compared to ratios of 1:20 with other platforms).

    He’ll also share how Qubole customers like Lyft, Under Armour and Turner use our cloud-native platform and multiple open source engines to run their big data workloads more efficiently and cost-effectively, as well as how the cloud helps them rapidly scale operations while simultaneously reduce their overall big data costs.

    In this webinar you’ll learn:

    - How to handle a broad set of needs and data sources
    - The importance of a cloud-native architecture for scaling big data operations
    - How and when to leverage multiple engines like Apache Spark, Presto and Airflow
    - The importance of a multi-layered approach to security
  • Speed to Value: How To Justify Your Big Data Investments
    Speed to Value: How To Justify Your Big Data Investments Amit Duvedi, VP of Business Value Engineering, Qubole Recorded: Aug 15 2018 55 mins
    Every investment in big data, whether people or technology, should be measured by how quickly it generates value for the business. While big data uses cases may vary, the need to prioritize investments, control costs and measure impact is universal.

    Like most CTOs, CIOs, VPs or Directors overseeing big data projects, you’re likely somewhere in between putting out fires and demonstrating how your big data projects are driving growth. If your focus, for example, is improving your users’ experience you need to be able to demonstrate a clear ROI in the form of higher customer retention or lifetime value.

    However, in addition to driving growth, you’re also responsible for managing costs. Here’s the rub-- if you’re successful in driving growth, your big data costs will only go up. That’s the consequence of successful big data use cases. How then, when you have success, do you limit and manage rising cloud costs?

    In this webinar, you’ll learn:

    - How to measure business value from big data use cases
    - Typical bottlenecks that delay time to value and ways to address them
    ​- Strategies for managing rising cloud and people costs
    - How best-in-class companies are generating value from big data use cases while also managing their costs
  • Moving Big Data To The Cloud? Here’s Why You Need A Cloud-Native Data Platform
    Moving Big Data To The Cloud? Here’s Why You Need A Cloud-Native Data Platform Matheen Raza and Kevin Blaisdell from Qubole Recorded: Jul 27 2018 29 mins
    Cloud service models have become the new norm for enterprise deployments in almost every category — and big data is no exception. As the volume, variety, and velocity of data increase exponentially, the cloud offers a more efficient and cost-effective option for managing the unpredictable and bursty workloads associated with big data compared to traditional on-premises data centers.

    Organizations looking to scale their big data projects and implement a data-driven business culture can do so with greater ease on the cloud. However, adopting a cloud deployment model requires a cloud-first re-architecture and a platform approach rather than a simple lift and shift of data applications and pipelines.

    A cloud-native data platform like Qubole helps organizations save on average 50 percent in total cost of ownership. Intelligent automation of cluster management tasks allows data teams to focus on business outcomes, thereby greatly improving SLAs and the end-user experience.

    ​Join experts from Qubole as they discuss how to activate your big data and get the most out of open source technologies on the cloud. In this webinar, you'll learn:

    - How big data projects benefit from a cloud-native data platform
    - How intelligent cluster management can help you save in total cost of ownership
    - About companies that successfully transitioned their big data to the cloud
    - How to evaluate cloud data platforms for your big data needs
  • O'Reilly Webcast: Creating a Data-driven Enterprise in Media
    O'Reilly Webcast: Creating a Data-driven Enterprise in Media Barbara Eckman from Comcast; Brad Linder from Sling TV; John Slocum from MediaMath; Utpal Bhatt from Qubole Recorded: Jul 24 2018 61 mins
    Every CEO aspires to create a data-driven culture that can activate hundreds or thousands of users and petabyte-scale data to continuously deliver true business value. This webcast panel discussion will explore the journey of three companies — Comcast, Sling TV, and MediaMath — that have chronicled their successes and challenges in a book by O’Reilly Media about creating a data-driven enterprise in media.

    The panelists discuss:

    - Their general technology strategy and choices
    - How data-driven insights are powering their businesses
    - Transforming the competitive dynamics of their industry through the power of data
  • AIRflow at Scale
    AIRflow at Scale Qubole Data Engineers Recorded: Jul 18 2018 44 mins
    This webinar covers how Qubole extended Apache AIRflow to manage the operational inefficiencies that arise managing data pipelines in a multi-tenant environment. Qubole also shares how to make data pipelines robust by adding data quality checks using CheckOperators.

    Key Takeaways:

    - Overview of major types of data pipelines
    - How Qubole manages deployments and upgrades of data pipelines in a multi-tenant environment
    - The Data quality issues that arise during data ingestion or transformation.
    - The approach that Qubole has adopted using Apache Airflow Check operators
    - The best practices in using Apache Airflow for data quality checks.
  • How To Increase Value from Machine Learning and Advanced Analytics on Azure
    How To Increase Value from Machine Learning and Advanced Analytics on Azure Nate Shea-han, Americas Global Black Belt, Data & AI at Microsoft and Shaun Van Staden, Solutions Architect at Qubole Recorded: Jun 20 2018 31 mins
    Becoming more competitive with big data today means having the right technology to uncover new insights from your data and make critical business decisions in real time. Qubole and Microsoft help companies activate their big data in the cloud to uncover insights that improve customer engagement, increase revenue, and lower costs.

    ​Join experts from Qubole and Microsoft as they discuss how to activate your big data and how to get the most out of open source technologies on the cloud. In this webinar, you'll learn:

    - How to modernize with data lakes and data warehouses on the cloud
    - Strategies for boosting business value out of Machine Learning and advanced analytics with Qubole on Azure
    - How to reduce costs, control risks, and improve data governance as you build your data pipelines
    - The importance of data security and privacy
    - Real world example​s​ of successful companies activating their big data

    Webinar Speakers:

    Nate Shea-han
    Americas Global Black Belt, Data & AI at Microsoft

    Nate Shea-han has been with Microsoft for 14 years and has spent the last 8 years focused on the helping Microsoft customers transform their business in the cloud on the Azure platform. Currently he has responsibilities across the United States, Canada and Latin America for Microsoft’s AI, big data, and analytics offerings. Nate has also worked extensively with Microsoft partner community.

    Shaun Van Staden
    Solutions Architect, Qubole

    Shaun Van Staden has 19 years of experience in enterprise software managing advanced analytics projects, as a developer, DBA, business analyst and now a solutions architect. As a solutions architect manager, Shaun is responsible for supporting business development and sales at Qubole and helping customers transform their use cases for the cloud. Prior to Qubole, Shaun worked as a solutions architect at NICE Systems and Merced Systems (acquired by NICE).

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