Lars Albertsson, former software engineer at Spotify and Google and currently freelance data engineer via mapflat, maintains this list of data engineering resources. It includes many links to videos and courses about data pipelines, batch processing, Kafka, NoSQL, Clojure, Scala, Parquet, Luigi, Storm, Spark, Hadoop, Cassandra, and other tools I am not too familiar with. Looks like it could function as a great curated overview for starters.
MIT researchers have spent years developing the new drag-and-drop analytics tools they call Northstar.
Northstar is an interactive data science platform that rethinks how people interact with data. It empowers users without programming experience, background in statistics or machine learning expertise to explore and mine data through an intuitive user interface, and effortlessly build, analyze, and evaluate machine learning (ML) pipelines.
Northstar starts as a blank, white interface. Users upload datasets into the system, which appear in a “datasets” box on the left. Any data labels will automatically populate a separate “attributes” box below. There’s also an “operators” box that contains various algorithms, as well as the new AutoML tool. All data are stored and analyzed in the cloud.
You can read more about the tool’s functionalities in this MIT news article, which includes several promising GIFs:
Moreover, on the Northstar website you can find this longer video explaining the tool in detail.
While Northstar looks insanely cool and promising, I do worry about putting such power in the hands of people who may not have much experience with statistics and/or machine learning. We all know how easily errors and bias may slip into data-driven processes, so I am curious to see how these next-gen kind of tools will be deployed and used.