Remove Aggregator Remove Metrics Remove SQL
article thumbnail

Lessons Learned: Sharding for startups

Startup Lessons Learned

If youre of the type of organization that doesnt use db access objects directly, but simply passes SQL statements through a central API, you can try this: consider the entity identifier as part of the statement itself. Nearly every SQL query I see has a join. The actual directory itself is straightforward. E, How is WoW sharded?

article thumbnail

Scaling lessons learned at Dropbox, part 1

eranki.tumblr.com

App-specific metrics. Another thing that became increasingly useful was to have thousands of custom stats aggregated over thousands of servers graphed. We eventually ended up converting everything to SQLAlchemy’s lowest-level language for constructing SQL (one step away from raw SQL).

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

A Quick Primer on B2B Conversion Optimization

ConversionXL

What Are Your Goals, and What Are Your Metrics? What this means depends on the company, but it can take into account different metrics like pathing, time on site, video engagement, company size, role, etc. So the process in B2B looks a bit like this (simplified): Visitor → MQL → SQL → customer.

B2B 48
article thumbnail

Google Analytics vs. Google Analytics 360 (Based on a Decade of Implementations)

ConversionXL

For example, while the data aggregation process in Google Analytics seems like a “normal” feature, it might be a hurdle if your business needs to process data at the hit level instead of by sessions or campaigns. Custom dimension and metrics. With Google Analytics 360, you can have 200 custom dimensions and 200 custom metrics.

article thumbnail

Takeaways from our first V1 Data Team Hangout

Version One Ventures

Let’s think about the typical (and simplified) data flow in a company: raw data is aggregated, normalized or processed and then stored in a data warehouse. As a company grows bigger, dashboards sprout up all over the organization with KPIs and metrics that mean different things to different people. What’s the problem with this?

article thumbnail

Empowering Analysis Ninjas? 12 Signs To Identify A Data Driven Culture

Occam's Razor

11: Close to zero aggregated analysis exists, everything's segmented. #10: They are generic mash-ups that tailor to almost no one's needs, and more often than not contain awful things like nine not-really-thought out metrics for one dimension in a report. All data in aggregate is crap. " Kisses. Angels singing!

Analytics 142
article thumbnail

Digital Dashboards: Strategic & Tactical: Best Practices, Tips, Examples

Occam's Razor

It also handy explanations of the metrics, with key context where necessary. These will sound like: Metric x is down because of our inability to take advantage of trend y and hence I recommend we do z. It provides a brief snapshot of the entire business. From 3rd grader attendance to new artworks on view to expenses to (hurray!)

Analytics 160