Knowledge Base: Working With Data Teams
You'll learn: How to make positive contributions when working with analytics and data science teams
Knowledge Base information is split up into 4 sections, organized by how it important it is to know each bit; peruse at your own leisure.
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The basics
Core concepts everyone should understand about data teams, including analytics, data science, and ML
What you need to know
- At SaaS companies, product analytics is a big part of what data teams do.
- You can read an overview of different parts of the data stack here.
What you should know
- The basic language of data teams is SQL, and it's very learnable.
- A new slew of tools build around cloud data warehouses are called The Modern Data Stack but it's mostly a marketing gimmick.
Where data comes from
Understanding data sources and collection across the business
What you need to know
- Data for analytics comes from across your business: your user and app data, and third party tools like Stripe and Salesforce.
- Relational databases are the ABCs of backends: they're where you store the data your app needs.
- You can go more in depth on production databases.
What you should know
- NoSQL databases are another popular way to store data, with less structure and more flexibility.
Where data is stored
Understanding data warehouses and storage solutions
What you need to know
- These days, most teams store their analytics data in a cloud-based data warehouse.
- You can go more in depth on cloud data warehouses.
What you should know
- A popular but less organized storage format is called a Data Lake.
How data gets moved around
Understanding ETL processes and data transformation
What you need to know
- Transforming data usually gets called ETL, short for extract, transform, and load.
- You can go more in depth on ETL.
Tools and products
- dbt is an increasingly popular tool for transforming and organizing your warehouse data.
- Kafka is a powerful tool built at LinkedIn for streaming event data in real time.
- Segment helps data teams collect analytics events and send them to the tools they need to be in.
- Databricks is a tool for running Spark jobs, basically ETL for big data.
How data gets used
Understanding data applications and machine learning
What you need to know
- Reverse ETL is the process of getting data from the warehouse to tools like Salesforce and Hubspot.
- Most data teams use a special type of code notebook to explore and analyze their data.
What you should know
- A language-based ML model named GPT-3 took the world by storm.
What's nice to know
- Product analytics tools are starting to use more data from the warehouse instead of clickstream events.
- Session Replay allows data teams to get a qualitative view into what their users are doing in the product.