We have been building platforms and workflows to store, process, and analyze data since the earliest days of computing. Over that time there have been countless architectures, patterns, and "best practices" to make that task manageable. With the growing popularity of cloud services a new pattern has emerged and been dubbed the "Modern Data Stack". In this episode members of the GoDataDriven team, Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan, explain the combinations of services that comprise this architecture, share their experiences working with clients to employ the stack, and the benefits of bringing engineers and business users together with data.
RudderStack is the smart customer data pipeline. It takes the toil out of building data pipelines that connect your whole customer data stack. Its easy-to-use SDKs and source integrations, Cloud Extract integrations, transformations, and expansive library of destination and warehouse integrations makes building customer data pipelines for both event streaming and cloud-to-warehouse ELT simple. RudderStack’s warehouse-first approach and Warehouse Actions functionality makes your customer data stack smarter by enabling analysis and modeling in your data warehouse to trigger enrichment and activation in all of your customer tools. Start building smarter customer data pipelines today with RudderStack. Visit dataengineeringpodcast.com/rudder to learn more and sign-up for our no credit card required, no time limit free tier.
Census is the operational analytics platform that syncs your cloud warehouse with all the SaaS applications used by your Sales, Marketing & Success teams. If you need to get your company data into Salesforce, Marketo, Hubspot, Intercom, Zendesk, and other tools, Census is the easiest way to do so. Just write SQL (or plug in your dbt models), set up the sync frequencies, and voila, your data is now available to be used by all of your teams. No need to worry about incremental sync, backfilling, API quota management, API versioning, monitoring, and maintaining custom scripts. Just SQL. Start your free 14-day trial now.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy!
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.
- We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
- Your host is Tobias Macey and today I’m interviewing Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan about their experiences with managed services in the modern data stack in their work as consultants at GoDataDriven
- How did you get involved in the area of data management?
- Can you start by giving your definition of the modern data stack?
- What are the key characteristics of a tool or platform that make it a candidate for the "modern" stack?
- How does the modern data stack shift the responsibilities and capabilities of data professionals and consumers?
- What are some difficulties that you face when working with customers to migrate to these new architectures?
- What are some of the limitations of the components or paradigms of the modern stack?
- What are some strategies that you have devised for addressing those limitations?
- What are some edge cases that you have run up against with specific vendors that you have had to work around?
- What are the "gotchas" that you don’t run up against until you’ve deployed a service and started using it at scale and over time?
- How does data governance get applied across the various services and systems of the modern stack?
- One of the core promises of cloud-based and managed services for data is the ability for data analysts and consumers to self-serve. What kinds of training have you found to be necessary/useful for those end-users?
- What is the role of data engineers in the context of the "modern" stack?
- What are the most interesting, innovative, or unexpected manifestations of the modern data stack that you have seen?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to implement a modern data stack?
- When is the modern data stack the wrong choice?
- What new architectures or tools are you keeping an eye on for future client work?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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