Data Council Blog

Data Council Blog

Storing Cold Metadata, Snowflake Data Cloud, and More: Top 10 Links From Across the Web

Here's our January 2021 roundup of links from across the web that could be relevant to you:

1. Storing Cold Metadata with Alki (Dropbox)

Dropbox shared insights into Alki, the petabyte-scale metadata store it designed for infrequently accessed metadata (“cold data”). The post details how one-size-fits-all database Edgestore was reaching capacity limits, and why audit logs were a good candidate to be moved elsewhere than on costly SSDs. After considering off-the-shelf options, the team settled on building its own solution on top of AWS services: Alki; with DynamoDB as the hot store, and S3 as the cold store. Like HBase or Cassandra, Alki is based on log-structured merge-trees (LSM trees), but is better suited to handle hot-then-cold audit logs, as well as future use cases at Dropbox.

The Modern Data Stack, Metadata Architectures, and More: Top 10 Links From Across the Web

Here's our December 2020 roundup of links from across the web that could be relevant to you:

1. The Modern Data Stack (Fishtown Analytics)

This long-form post on the dbt blog is a must-read. Titled “The Modern Data Stack: Past, Present, and Future,” it answers the question that Tristan Handy has been asking himself for the past two years: “What happened to the massive innovation we saw from 2012-2016?” His carefully thought-out analysis covers the natural cycles of technological shifts, defines the phase we are in as a ‘deployment’ one, and points out high-impact opportunity areas for the next few years - which you might find particularly useful if you are considering launching a new product.

NLP Heroes, Pinot, Data Testing, and More: Top 10 Links From Across the Web

Here's our November 2020 roundup of good reads and podcast episodes that might be relevant for your career in data:

1. Heroes of NLP: Quoc Le (Deeplearning.ai)

NLP researcher Quoc Le was recently Andrew Ng’s guest as part of the ‘Heroes of NLP’ video series. Their discussion covered Le’s impressive journey, from growing up in Vietnam and developing his first basic chatbot in high school to becoming Google Brain’s first intern, and everything that followed. This includes the ‘Google Cat’ experiment, the Meena chatbot project, and work on Seq2Seq models. Check out the conversation here, and consider subscribing to the series to hear from other guests such as Chris Manning, Kathleen McKeown, and Oren Etzioni.

State of AI, Data Quality, and More: Top 10 Links From Across the Web

Here's our October 2020 roundup of good reads and podcast episodes that might be relevant to you as a data professional:

1. Multiplayer Editing: a Pragmatic Approach (Hex)

Data collaboration startup Hex published a great long read on its approach to live collaboration . Written by software engineer Mac Lockard, it takes a look at the respective pros and cons of Operational Transforms and Conflict-free Replicated Data Types (CRDTs), before explaining the solution that Hex adopted. Inspired by Figma's hybrid approach, it can also be described as "Atomic Operations (AO), as all edits to application state are broken down to their smallest atomic parts." "If the application you are building can rely on last-writer-wins semantics, Atomic Operations might provide a more pragmatic approach," the post concludes. This is a highly recommended read if you are pondering about a similar decision.  

Hot Data Tools pt. 2, End-to-End Data Scientists, and More: Top 10 Links From Across the Web

Here's our September 2020 roundup of good reads and podcast episodes that might be relevant to you as a data professional:

1. What Data Tools Don't Do (Data Council)

Our founder Pete Soderling co-authored a follow-on piece to his previous post with Great Expectations' core contributor Abe Gong and Partner at Amplify Partners Sarah Catanzaro, for which they had interviewed the makers of some of the hottest data tools. The focus is still the same: rather than what their data tools can do, we hear about what they don't do, as a way to better understand how they fit together. From ApertureData to Xplenty, this new installment covers 21 new tools, and you can read it here.

Large Datasets, Are Dashboards Dead, and More: Top 10 Links From Across the Web

Here's our August 2020 roundup of good reads and great podcast episodes for anyone working with data:

1. Processing Large Datasets with Python

AI engineer and author J.T. Wolohan was recently a guest of the Heroku’s Code[ish] podcast to discuss the contents of his book, “Mastering Large Datasets with Python.” Listen to the episode here or read the transcript for some practical advice on using Python to deal with massive datasets, especially in the context of machine learning.

Apache Airflow, Beyond Spreadsheets, and More: Top 10 Links From Across the Web

Here's our July 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. The State of Airflow

Software Engineering Daily recently invited Apache Airflow's creator Maxime Beauchemin and Astronomer engineers Vikram Koka and Ash Berlin-Taylor to discuss the state of Airflow. Listen to the podcast episode or read the transcript to hear their comments on Airflow's use cases, its purpose, the open source ecosystem, and more.

AGI, Dask, Feature Stores, and More: Top 10 Links From Across the Web

Here's our June 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. Self-Supervised Learning vs. AGI

"AGI does not exist — there is no such thing as general intelligence. We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence," Yann LeCun declared during an online session of the International Conference on Learning Representation (ICLR) 2020, which VentureBeat wrote about. Together with fellow Turing Award winner Yoshua Bengio, he advocated for pursuing humanlike AI through "self-supervised learning."

Emerging Data Roles: The Analytics Engineer

Analytics Engineer: this term has started showing up in blog posts and job listings. It all happened quickly; just a couple of years ago, it wasn't a thing our friends in the data ecosystem talked about. So how did it start trending, what is it exactly, and is it here to stay? We decided to take a closer look, and here's what we found out.

Open Source Highlight: Cube.js

Cube.js is an open source analytics framework meant to answer the "lack of tools for software engineers who are building production, customer-facing applications and need to embed analytics features into these applications," its co-founder and CEO Artyom Keydunov explained in a blog post

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