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Why we lost Uber as a user
On 07/26/2016 01:53 PM, Josh Berkus wrote: > The write amplification issue, and its correllary in VACUUM, certainly > continues …
On Uber’s Choice of Databases
Uber provides bad advice about PostreSQL vs. MySQL
Log Structured Merge Trees
A detailed look at the interesting LSM file organisation seen in BigTable, Cassandra and most recently MongoDB.
Introduction to Zipline: A Trading Library for Python
This blogpost introduces zipline package in python with its benefits, how to install it, and using it code the moving crossover strategy for financial trade
Generalized linear models, abridged
Generalized linear models (GLMs) are indispensable tools in the data science toolbox. They are applicable to many real-world problems involving continuous, yes/no, count and survival data (and more). The models themselves are intuitive and can be used for inference and prediction. A few very high quality free and open…
Sculpture of Housing Prices Ripping San Francisco Apart
This data sculpture depicts a map of housing prices in San Francisco. It's a map of the city, torn at the seams. The height of each area represents the average price per square foot for recent home sales. Where neighboring areas are close in value they are connected, but if neighboring areas are too far
SciPy 2016: “Data Science is Software” tutorial
Last week, Peter and I gave a tutorial at SciPy 2016. In keeping with our recent thinking about reproducibility and organization , the idea was to look at how some of the hard-won lessons and best practices from modern software engineering can be applied to data science work.
Unstructured Datatypes in Postgres – Hstore vs. JSON vs. JSONB
All about when to use unstructured data types in Postgres, such as Hstore, JSON, and JSONB. The tl;dr is that the right data type depends on your use case. Here we dig deep into the benefits, and why JSONB became so popular since it first got real and proper support in Postgres 9.4. The B in JSONB stands for better.
Jepsen: MongoDB
Previously in Jepsen , we discussed Redis . In this post, we’ll see MongoDB drop a phenomenal amount of data. See also: followup analyses of 2.6.7 and 3.4.0-rc3 .
Billions of Messages a Day – Yelp's Real-Time Data Pipeline
Billions of Messages a Day - Yelp's Real-time Data Pipeline Justin C., Software Engineer Jul 14, 2016 This post is part of a series covering Yelp's real-time streaming data infrastructure....
Lepton image compression: saving 22% losslessly from images at 15MB/s
Dash knows your context, your team, and your work, so your team can stay organized, easily find and share knowledge, and keep projects secure, all from one place.
Bayesian Machine Learning Explained
So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know …
Jepsen: VoltDB 6.3
In the last Jepsen post, we found that RethinkDB could lose data when a network partition occurred during cluster reconfiguration. In this analysis, we’ll show that although VoltDB 6.3 claims strict serializability, internal optimizations and bugs lead to stale reads, dirty reads, and even lost updates; fixes are now…
A beginners guide to thinking in SQL
In this tutorial we will learn about the concepts behind the SQL syntax. We will go through the basics of how to use the FROM and WHERE clauses, and move on to more advanced topics like joins, aggregations and subqueries.
Pokémon GO: The Data Behind America’s Latest Obsession
Similarweb's blog is a go-to resource for digital professionals and marketing experts offering a wide range of business tips, industry insights, trends and more.
Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE
In this post, I discuss an alternate solution; namely, running separate CPU and GPU clusters, and driving the end-to-end modeling process from Apache Spark.
Realtime Data Processing at Facebook
Recently there has been a lot of development in realtime data processing systems, including Twitter's Storm and Heron, Google's Millwheel, a...