Systems | Development | Analytics | API | Testing

June 2020

Ritual Improves Retention With a Modern Data Stack

A brittle ETL pipeline, a mix of different code languages and degrading warehouse performance inhibited customer retention analysis. With a modern data stack, Ritual has a 95% reduction in data pipeline issues, a 75% reduction in query times, and a threefold increase in data team velocity. By empowering the business with data, the business has seen a sustained improvement in retention.

Aceable Switches From Alooma to Fivetran, Eliminates ETL Maintenance

After Alooma announced it was sunsetting its services for Redshift customers, Aceable moved to Fivetran for data integration. In one week, the business integrated all of its sources, including MongoDB — a project that was never completed with Alooma. With Fivetran, Aceable eliminates the need for back-end maintenance and adds Jira to its stack to track project progress across the entire org.

Speed Up Development With Powered by Fivetran

Powered by Fivetran (PBF) provides a simple framework for developers to go beyond internal analytics projects to build data pipelines into their applications within the Fivetran platform. With no engineering overhead, you can easily access hundreds of customer accounts across countless Fivetran-supported data sources, including advertising platforms, CRM systems, databases, web events and more.

Databases Demystified Lesson Distributed Databases Part 3

In this episode of Michael Kaminksy's Databases Demystified, he explores: What does “consensus” mean and why is it important? Learn about the two-generals problem and what it teaches us about reaching consensus Learn about the main consensus algorithms in distributed databases: Raft & Paxos

Databases Demystified Lesson 1 Introduction to Databases and SQL

In the first episode of Databases Demystified with Michael Kaminsky, we give a high-level overview of the most important concepts in databases. We start with a brief history of databases going from the invention of relational databases through present day and we talk about the differences between analytical and transactional databases, distributed and single-node databases, and in-memory vs on-disk databases We finish up talking briefly about SQL and what makes it special.

Databases Demystified Lesson 4: Transactions Part 1

In this episode of Michael Kaminksy's Databases Demystified, we learn all about what a transaction is, and what ACID means. Learn why database constraints are important, and what the commands "begin" "commit" and "rollback" mean. We talk about atomicity, consistency, isolation, and durability and why transactions are so important.

Databases Demystified Lesson 6: Distributed Databases Part 1

Welcome to episode 6 of Michael Kaminsky's Databases Demystified. In this lesson, we introduce a fascinating and incredibly important topic: distributed databases. We discuss "nodes" and "clusters" and we cover the two major paradigms in distributed databases: big-compute databases and high-availability databases.

Databases Demystified: Lesson 7: Distributed Databases Part 2

Episode 7 of Michael Kaminsky's Databases Demystified. Learn about new issues we face in distributed databases and all about the CAP theorem. We'll talk about leader and follower nodes, what happens when distributed databases lose connection with a node, and what CAP stands for: consistency, availability, and partition tolerance.

Databases Demystified Lesson 3: Row vs Column Store

In Michael Kaminsky's third episode, we learn about the differences in row store vs column store database. This is a very important concept for understanding the difference between analytical and transactional databases, and we talk about the tradeoffs between using row and column stores for saving the data. Michael gets into the weeds and talks about disk blocks and the different types of queries that work well for row and column stores.