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AIRflow at Scale

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.
Recorded Jul 18 2018 44 mins
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Presented by
Qubole Data Engineers
Presentation preview: AIRflow at Scale

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    - 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
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    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.
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    - Discuss how Qubole has achieved single-node, multi-GPU parallelization using native Tensorflow and Keras with Tensorflow as a backend.
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    Goden Yao, Principal Product Manager at Qubole
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    Ashwin Chandra Putta, Sr. Product Manager at Qubole
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    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
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    Mohit Bhatnagar, SVP of Product at Qubole
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    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 Recorded: Aug 15 2018 55 mins
    Amit Duvedi, VP of Business Value Engineering, Qubole
    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 Recorded: Jul 27 2018 29 mins
    Matheen Raza and Kevin Blaisdell from Qubole
    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
Elemental to Big Data
At our core, we are a team of engineers who eat, sleep, and live big data. We believe that ubiquitous access to information is the key to unlocking a company's success. To achieve this, a big data platform must be agile, flexible, scalable, and proactive to anticipate a company's needs.

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  • Title: AIRflow at Scale
  • Live at: Jul 18 2018 9:10 pm
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