How Cox Automotive Democratized Data with a Self-Service Data Exchange
Cox Automotive comprises more than 25 companies dealing with different aspects of the car ownership lifecycle, with data as the common language they all share. The challenge for Cox Automotive was to create an efficient engine for the timely and trustworthy ingest of data capability for an unknown but large number of data assets from practically any source. Working with StreamSets, they are populating a data lake to democratize data, allowing analysts easy access to data from other companies and producing new data assets unique to the industry.
In this webinar, Nathan Swetye from Cox Automotive will discuss how they:
-Took on the challenge of ingesting data at enterprise scale and the initial efficiency and data consistency struggles they faced.
-Created a self-service data exchange for their companies based on an architecture that decoupled data acquisition from ingestion.
-Reduced data availability from weeks to hours and developer time by 90%.
RecordedFeb 26 201962 mins
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StreamSets is proud to be hosting the first annual DataOps Summit in San Francisco, California at the Hilton San Francisco Financial District on September 3rd-5th. The summit will feature a full day of data operations training and two days of comprehensive content featuring major brands, high-scale use cases, ecosystem partners, and community heros. Keynote discussions will feature data luminaries from companies like Humana, Snowflake, GSK and Ericsson explaining how they built a discipline of DataOps and a culture that reaps the benefits. Take advantage of the opportunity to uplevel your skillset, hear from thought leaders and network with your peers.
According to research firm Gartner, at least 75% of large and global organizations will implement a multicloud-capable hybrid integration platform by 2021. As businesses continue to embrace digital transformation, moving toward self service analytics and cloud analytics executed on big data, a more agile solution for these workloads is needed. StreamSets applies DevOps practices to data management and data integration to reduce the cycle time of data analytics with a focus on automation, collaboration and monitoring. DataOps is essential for a data landscape marked by complexity of data architecture with accelerating change.
Join StreamSets Head of Product Management, Kirit Basu, and Head of Product Marketing, Clarke Patterson as they discuss how StreamSets customers are taking a DataOps approach to hybrid-cloud integration.
We'll explore how Fortune 500 customers are using StreamSets to streamline Apache Kafka and Data Lake projects using principles adopted from DevOps.
This session will cover:
- Pitfalls to avoid for any hybrid cloud project
- Key requirements to ensure continuous movement of data across any cloud
- How StreamSets customers are using the platform to streamline their approach to analytics and data lake projects, while driving real value from DataOps
Continuous Dataflows that Unleash Pervasive Intelligence
The StreamSets DataOps platform enables companies to build, execute, operate and protect batch and streaming dataflows. It is powered by StreamSets Data Collector, award-winning open source software with approximately 2,000,000 downloads to date from thousands of companies. The commercial StreamSets Control Hub is the platform's cloud-native control plane through which enterprises design, monitor and manage complex data movement that is executed by multiple Data Collectors. Unique Intelligent Pipeline technology automatically inspects the data in motion, detecting unexpected changes, errors and sensitive data in-stream.
Global 2000 customers use StreamSets for data lake ingestion, Apache Kafka enablement, cybersecurity, IoT, customer 360, GDPR compliance and more. In 2017, the company tripled its customer count and quadrupled revenues.
How Cox Automotive Democratized Data with a Self-Service Data ExchangeNathan Swetye - Sr. Manager of Platform Engineering - Cox Automotive[[ webcastStartDate * 1000 | amDateFormat: 'MMM D YYYY h:mm a' ]]62 mins