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SF Data Weekly - Most Clicked Posts of 2018

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2018 has been an amazing year, and our newsletter had a x 10 increase in subscribers! As a thank you
 
January 2 · Issue #98 · View online
SF Data Weekly
2018 has been an amazing year, and our newsletter had a x 10 increase in subscribers!
As a thank you to all our dedicated readers, we’ve prepared a round up of the most clicked posts of the year. Enjoy reading!

Introductory
A Beginner’s Guide to Data Engineering — Part I – Robert Chang
Building The Analytics Team At Wish – Wish Engineering And Data Science – Medium
Scaling Decision-Making Across Teams within LinkedIn Engineering | LinkedIn Engineering
Rules for Crash Course Data Engineering – Data Syndrome
Laying the Foundation for a Data Team | Monzo
Software Architecture: Architect Your Application with AWS
ETL vs ELT or Data Warehouse vs Data Lake – Xplenty Blog – Medium
The Surprising Link between a Company’s Growth and its Data Analytics Strategy
Batch and Streaming in the World of Data Science and Data Engineering
The State of Data Engineering
Data Pipelines
Functional Data Engineering — a modern paradigm for batch data processing
Thorough Introduction to Apache Kafka™ – Hacker Noon
Understanding Apache Airflow’s key concepts – Dustin Stansbury
Getting started with Elasticsearch in Python – Towards Data Science
Stream Processing Made Easy With Confluent Cloud and KSQL
Streaming in the Clouds: Where to Start | Confluent
How Pinterest runs Kafka at scale – Pinterest Engineering – Medium
Building data infrastructure in Coursera – Zhaojun Zhang
How Disney Built a Pipeline for Streaming Analytics
Processing streams of data with Apache Kafka and Spark: ingestion, processing, reaction, examples
Is Batch ETL Dead, and is Apache Kafka the Future of Data Processing?
How we built a data pipeline with Lambda Architecture using Spark/Spark Streaming
Overview of Mozilla's Data Pipeline - Firefox Data Documentation
Hands on: Building a Streaming Application with KSQL | Confluent
Democratizing Stream Processing with Apache Kafka® and KSQL - Part 2
Airflow 101: Start automating your batch workflows with ease
Building a Big Data Pipeline With Airflow, Spark and Zeppelin
Data pipelines, Luigi, Airflow: everything you need to know
Data Storage
Hadoop 3: Comparison with Hadoop 2 and Spark – ActiveWizards: machine learning company – Medium
Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift | Amazon Web Services
Doing Without Databases in the 21st Century – codeburst
Migrating Messenger storage to optimize performance - Facebook Code
Optimizing BigQuery: Cluster your tables – Google Cloud Platform - Community – Medium
Why SQL is beating NoSQL, and what this means for the future of data
Amazon Redshift Spectrum: Diving into the Data Lake! - intermix.io
Snowflake’s Cloud Data Warehouse — What I Learned and Why I’m Rethinking the Data Warehouse
How to Get Started With AWS Spectrum in Minutes
Streaming Messages from Kafka into Redshift in near Real-Time
How to architect the perfect Data Warehouse – Lewis Gavin – Medium
Top 14 Performance Tuning Techniques for Amazon Redshift - intermix.io
Time Series Data and MongoDB: Part 2 – Schema Design Best Practices | MongoDB
How We Reduced Our Amazon Redshift Cost by 28% - intermix.io
The SQL vs NoSQL Difference: MySQL vs MongoDB – Xplenty Blog – Medium
Improving Amazon Redshift Performance: Our Data Warehouse Story [Udemy]
Data Analysis
Give meaning to 100 billion analytics events a day – Teads Engineering – Medium
Get Started with PySpark and Jupyter Notebook in 3 Minutes
Google’s AutoML will change how businesses use Machine Learning
Twitter meets TensorFlow
What is TensorFlow? An Intro to The Most Popular Machine Learning Framework - intermix.io
Productionizing ML with workflows at Twitter
Reinforcement Learning: A Deep Dive | Toptal
Learn ML Algorithms by coding: Decision Trees – Lethal Brains
Using Apache Spark for large-scale language model training - Facebook Code
Tutorial: Connecting Tableau to your data warehouse for analytics
Using Deep Learning at Scale in Twitter’s Timelines
Machine Learning @ Teads (part 2) – Teads Engineering
10 Myths of Enterprise Python | PayPal Engineering Blog
Get Started with Deep Learning Using the AWS Deep Learning AMI | AWS Machine Learning Blog
A/B Testing: The Definitive Guide to Improving Your Product
Apache Spark Introduction for Beginners
Gradient Boosting Libraries — A Comparison - intermix.io
Deploying a Keras Deep Learning Model as a Web Application in Python
Data Visualization
Visualizations on Apache Kafka® Made Easy with KSQL
Creating a Data Visualization GraphQL Server with a Loosely Coupled Schema
Interactive Data Visualization with D3.js – Towards Data Science
An overview of every Data Visualization course on the internet
4 More Quick and Easy Data Visualizations in Python with Code
The Gender Balance of The New York Times Best Seller List
Exploring Movie Data with Interactive Visualizations
Data-driven Products
Food Discovery with Uber Eats: Building a Query Understanding Engine | Uber Engineering Blog
Post-Retirement Calculator: Will My Money Survive Early Retirement? Visualizing Longevity Risk - Engaging Data
City Health Dashboard
3 Phases of Building a Data Product — Juice Analytics
Exclusive: Fitbit's 150 billion hours of heart data reveal secrets about health
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