�ݺ�ߣshows by User: gregak

�ݺ�ߣshows by User: gregak / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: gregak / Thu, 14 Jun 2018 06:09:45 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: gregak If you're not measuring advertising effectiveness through RCTs, you're doing it wrong /gregak/if-youre-not-measuring-advertising-effectiveness-through-rcts-youre-doing-it-wrong-102417409 incrementality-180614060946
My talk given at Incrementality Meetup in San Francisco]]>
My talk given at Incrementality Meetup in San Francisco]]> Thu, 14 Jun 2018 06:09:45 GMT /gregak/if-youre-not-measuring-advertising-effectiveness-through-rcts-youre-doing-it-wrong-102417409 gregak@slideshare.net(gregak) If you're not measuring advertising effectiveness through RCTs, you're doing it wrong gregak My talk given at Incrementality Meetup in San Francisco <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/incrementality-180614060946-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> My talk given at Incrementality Meetup in San Francisco

If you're not measuring advertising effectiveness through RCTs, you're doing it wrong from Grega Kespret

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0 How we evolved data pipeline at Celtra and what we learned along the way /slideshow/how-we-evolved-data-pipeline-at-celtra-and-what-we-learned-along-the-way/93890885 howweevolveddatapipelineatceltraandwhatwelearnedalongtheway-180415101624
Presented at Data Science Meetup on 4/12/2018. In this talk, Grega Kespret (head of analytics group) will present Celtra’s data analytics pipeline and how it evolved through the years - sometimes forward, sometimes backward. On this journey, we became early adopter of different technologies: BigQuery, Vertica (pre-join projections), Spark (version 0.5), Databricks (beta users) and Snowflake (one of the first users). As the business grew and the product evolved, volume and complexity of data increased ten-fold, as has the number of users generating insights from this data. How come BigQuery did not scale? Why was choosing Vertica a mistake for our use case, and what have we learned from it? What requirements did we have for the analytics database, why did we have to abandon MySQL, and why we finally chose Snowflake? This talk will be heavily opinionated and will describe our experience and learnings - what worked for us and what didn't.]]>
Presented at Data Science Meetup on 4/12/2018. In this talk, Grega Kespret (head of analytics group) will present Celtra’s data analytics pipeline and how it evolved through the years - sometimes forward, sometimes backward. On this journey, we became early adopter of different technologies: BigQuery, Vertica (pre-join projections), Spark (version 0.5), Databricks (beta users) and Snowflake (one of the first users). As the business grew and the product evolved, volume and complexity of data increased ten-fold, as has the number of users generating insights from this data. How come BigQuery did not scale? Why was choosing Vertica a mistake for our use case, and what have we learned from it? What requirements did we have for the analytics database, why did we have to abandon MySQL, and why we finally chose Snowflake? This talk will be heavily opinionated and will describe our experience and learnings - what worked for us and what didn't.]]> Sun, 15 Apr 2018 10:16:24 GMT /slideshow/how-we-evolved-data-pipeline-at-celtra-and-what-we-learned-along-the-way/93890885 gregak@slideshare.net(gregak) How we evolved data pipeline at Celtra and what we learned along the way gregak Presented at Data Science Meetup on 4/12/2018. In this talk, Grega Kespret (head of analytics group) will present Celtra’s data analytics pipeline and how it evolved through the years - sometimes forward, sometimes backward. On this journey, we became early adopter of different technologies: BigQuery, Vertica (pre-join projections), Spark (version 0.5), Databricks (beta users) and Snowflake (one of the first users). As the business grew and the product evolved, volume and complexity of data increased ten-fold, as has the number of users generating insights from this data. How come BigQuery did not scale? Why was choosing Vertica a mistake for our use case, and what have we learned from it? What requirements did we have for the analytics database, why did we have to abandon MySQL, and why we finally chose Snowflake? This talk will be heavily opinionated and will describe our experience and learnings - what worked for us and what didn't. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howweevolveddatapipelineatceltraandwhatwelearnedalongtheway-180415101624-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Presented at Data Science Meetup on 4/12/2018. In this talk, Grega Kespret (head of analytics group) will present Celtra’s data analytics pipeline and how it evolved through the years - sometimes forward, sometimes backward. On this journey, we became early adopter of different technologies: BigQuery, Vertica (pre-join projections), Spark (version 0.5), Databricks (beta users) and Snowflake (one of the first users). As the business grew and the product evolved, volume and complexity of data increased ten-fold, as has the number of users generating insights from this data. How come BigQuery did not scale? Why was choosing Vertica a mistake for our use case, and what have we learned from it? What requirements did we have for the analytics database, why did we have to abandon MySQL, and why we finally chose Snowflake? This talk will be heavily opinionated and will describe our experience and learnings - what worked for us and what didn't.

How we evolved data pipeline at Celtra and what we learned along the way from Grega Kespret

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0 Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks /slideshow/selfserve-analytics-journey-at-celtra-snowflake-spark-and-databricks-60358487/60358487 whyceltraadoptedsnowflakeintoitsadeventsparkpipelinev1-160401222230
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both. Topics include: - Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline - How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution - How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency - How Snowflake + Spark enables production and ad hoc analytics on a single repository of data]]>
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both. Topics include: - Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline - How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution - How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency - How Snowflake + Spark enables production and ad hoc analytics on a single repository of data]]> Fri, 01 Apr 2016 22:22:30 GMT /slideshow/selfserve-analytics-journey-at-celtra-snowflake-spark-and-databricks-60358487/60358487 gregak@slideshare.net(gregak) Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks gregak Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both. Topics include: - Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline - How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution - How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency - How Snowflake + Spark enables production and ad hoc analytics on a single repository of data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whyceltraadoptedsnowflakeintoitsadeventsparkpipelinev1-160401222230-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both. Topics include: - Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline - How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution - How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency - How Snowflake + Spark enables production and ad hoc analytics on a single repository of data

Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks from Grega Kespret

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0 How Celtra Optimizes its Advertising Platform�with Databricks /slideshow/how-celtra-optimizes-its-advertising-platformwith-databricks/56186951 howceltraoptimizesitsadvertisingplatformwithdatabricks-151216014836
Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.]]>
Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.]]> Wed, 16 Dec 2015 01:48:36 GMT /slideshow/how-celtra-optimizes-its-advertising-platformwith-databricks/56186951 gregak@slideshare.net(gregak) How Celtra Optimizes its Advertising Platform�with Databricks gregak Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howceltraoptimizesitsadvertisingplatformwithdatabricks-151216014836-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.

How Celtra Optimizes its Advertising Platform with Databricks from Grega Kespret

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0 https://cdn.slidesharecdn.com/profile-photo-gregak-48x48.jpg?cb=1706464477 gregakespret.com https://cdn.slidesharecdn.com/ss_thumbnails/incrementality-180614060946-thumbnail.jpg?width=320&height=320&fit=bounds gregak/if-youre-not-measuring-advertising-effectiveness-through-rcts-youre-doing-it-wrong-102417409 If you're not measurin... https://cdn.slidesharecdn.com/ss_thumbnails/howweevolveddatapipelineatceltraandwhatwelearnedalongtheway-180415101624-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-we-evolved-data-pipeline-at-celtra-and-what-we-learned-along-the-way/93890885 How we evolved data pi... https://cdn.slidesharecdn.com/ss_thumbnails/whyceltraadoptedsnowflakeintoitsadeventsparkpipelinev1-160401222230-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/selfserve-analytics-journey-at-celtra-snowflake-spark-and-databricks-60358487/60358487 Self-serve analytics j...