Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.
Skafos is the machine learning deployment platform that provides data scientists with end-to-end support throughout the machine learning lifecycle. Skafos maximizes tool and framework interoperability and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously.
Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
- You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
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- This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics
- How did you get involved in the area of data engineering and data management?
- What is Snowplow Analytics and what problem were you trying to solve when you started the company?
- What is unique about customer event data from an ingestion and processing perspective?
- Challenges with properly matching up data between sources
- Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?
- What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly?
- Can you describe the overall architecture of the ingest pipeline that Snowplow provides?
- How has that architecture evolved from when you first started?
- What would you do differently if you were to start over today?
- Ensuring appropriate use of enrichment sources
- What have been some of the biggest challenges encountered while building and evolving Snowplow?
- What are some of the most interesting uses of your platform that you are aware of?
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- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Deloitte Consulting
- EMR (Elastic Map-Reduce)
- Business Intelligence
- Data Warehousing
- Google Analytics
- CRM (Customer Relationship Management)
- GDPR (General Data Protection Regulation)
- Google Cloud Pub-Sub
- IAB Bots And Spiders List
- Heap Analytics
- Snowplow Insights
- Google Cloud Platform