Learning Track: Working With Data Teams

Product analytics is how teams instrument and analyze data about their product usage.

A deep dive into all of the tools that data teams use to do their work.

If you’re not a data scientist but you have questions, you want to know SQL.

The new set of tools data teams use to get their jobs done.

Stripe sells payments infrastructure for internet businesses: primarily, they help you bill your customers, process payments, and work with your payment data.

How most developers store and analyze application data.

A deep dive into the databases that power our apps.

NoSQL is databases with no rules and no required structure.

A data warehouse is a special type of database designed for analytics instead of transactions.

A deep dive into data warehouses, what they do, and how different ones stack up.

A Data Lake is an unstructured place to put data.

Snowflake sells a powerful cloud data warehouse for analytics and data science teams.

Elasticsearch is a popular open source database for storing and searching unstructured data.

MongoDB is a highly popular unstructured, NoSQL document database for powering your applications.

The process of moving data around your internal systems to get it ready for analysis.

A deep dive on how (tools, methods, use cases) companies move data back and forth.

dbt (no capitals) is a tool for transforming and organizing data in your warehouse.

Apache Kafka is a framework for streaming real time data, and Confluent offers Kafka as a managed service.

Segment helps teams track their product and marketing data and send it to whichever tools it needs to go to.

What we should really be asking is “What does Databricks not do?”

How data gets from your warehouse to the tools that need it.

Data Science Notebooks help data teams explore data with code.

GPT-3 is a Machine Learning model that generates text.

Why product analytics tools like Mixpanel are focusing on the warehouse.

The magic behind the qualitative side of product analytics.