Tasman Analytics

Here's all you need to know to run dbt inside of Snowflake using the Snowpark service - and get these benefits without needing to orchestrate anything: * OAuth authentication for security * Snowpark Container Services for compute * Email notifications for errors * Snowflake Procedures and Tasks for scheduling * Event Tables for persistent logging * and much more. It's not the simplest of setups but we've written the guide to make this super easy! Check the link below and let us know in the comments what you think :) 👉 https://lnkd.in/eDUS4yVk #snowpark #snowflake #dataengineering #howto

Hi folks - our own Caro Boeri Dollberg and Anna Selway are hosting an office hours session with our friends at Amplitude today at 5pm BST / 6pm CEST. Join them to ask anything you want to know about platform integrations, event governance and using Amplitude as a warehouse CDP. Register free over here: https://lnkd.in/exmgsDQ5

Is your data team as mature as you want it to be? What does that even mean? Here are five checks: 1️⃣ Are you tracking key data sources? 2️⃣ Are you ensuring data quality? 3️⃣ Is your data joined up for seamless insights? 4️⃣ Is your data platform actually used by the business for day to day decisions? 5️⃣ And last - are you contineously benchmarking against industry best standards? If YES to all five, then you're doing great :) Anything I missed? Let me know in the comments!

Marketing content on Linkedin: 10x your performance. Analytics content on Linkedin: Please, just do it properly. We are still trying to convince people to just implement a basic setup, maybe spend a little bit of money. Most of the data content on linkedin is trying to explain that: - data is not as hard as it seems - data can deliver value - it is not that hard to work with - it is not even expensive And yet, in my interactions with founders and CXOs, I see that the message hasn't travelled far. It's the same situation from 10 years ago when i entered analytics, only with better tools that don't get used. Why is it that data teams and practitioners are still trying to prove our value? -- This isn't a rant. Just an observation after i spent some time browsing through data content and realized that we are all talking about the same stuff. Posts taken from some excellent data communicators: Thomas Varekamp Thomas in't Veld Justin Butlion Drew Mooney

Tasman will be at Big Data LDN 2024 this week, drop me a DM if you want to say 👋 . And if you haven't already, you can register free here! #BigDataLDN https://lnkd.in/es_BPBnB

We are hiring! We're looking for a senior analytics engineering in Amsterdam or London to join our team. Key responsibilities: 🤝 Partner with our clients to design and develop scalable domain-driven data models. ✍ Complete technical analyses of business requirements, accurately estimating complexity. 🏗 Build, maintain and troubleshoot complex SQL data models. ✅ Design and implement testing frameworks to assure the quality of the data being presented. 👨🏫 Mentor and develop other Analytics Engineers in Tasman to achieve their full potential. Know anyone? Let us know or share the job post below! https://lnkd.in/eTDmy9yg

Are you in the market for a data platform, but a bit lost between all the options? Many folks ask us about the difference between a traditional CDP and the new wave of Composable CDPs, and so we thought we'd make a little video talking through it. Common platforms and a good industry overview in the comment section :) (Do you want us to produce more content like this? Leave a like or a comment with a suggestion!)

You can now run an entire data stack in Snowflake. A few weeks back we discussed how this works and how you could run a dbt project in an ad-hoc way. Let's now make sure this is scalable and rigorously implemented. That means we need to think about CI/CD, error notification, run monitoring, and container tracking. These are all game changers that we are super stoked about. Marcello Victorino is walking us through the setup next week, but below you can already see a small preview. See the whole post here: https://lnkd.in/gpwZ-Cws

When setting up your event pipeline, should you go one-table-per-event, or for the event stream approach? Table-per-event: 💫 offers rigidity and manageability 💫 making it ideal for isolated, large-scale event evaluations 💫 and great for scenarios where compliance requires data to be stored separately. Event stream schema: 👉 leverages JSON objects to store properties 👉 especially important in columnar data warehouses, as provides unmatched flexibility and efficiency 👉 allows Analytics Engineering teams to derive value from data with less complexity and higher scalability. There is no one-size fits all, so being able to have the best of both worlds in the name of the game - and in our opinion, transforming event streams into table-per-event schemas is easy and efficient, whilst converting table-per-event schemas into streams is painful and challenging to maintain as tracking evolves. At Tasman we love working with RudderStack. And while its out of the box loaders elect for table-per-event, it is possible to create event streams using pipeline transformations, allowing teams to select the schema that is right for them. Read more in Tom Shelley's excellent post here: https://lnkd.in/e3Jf3Bmm

Why is no one using your dashboards? Data Activation is the number 1 downer for even great data teams. Why is that? What can you do about it? Let me know in the comments what you do in your data teams to ensure you keep iterating dashboards to get your stakeholders to use them properly!