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Abstract
Data Lake Management: Challenges and Opportunities Tuesday, August 27, 11:00 – 12:30 -- VLDB 2019, Los Angeles, California Abstract The ubiquity of data lakes has created fascinating new challenges for data management research. In this tutorial, we review the state-of-the-art in data management for data lakes. We consider how data lakes are introducing new problems including dataset discovery and
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS
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import cv2 import numpy as np from matplotlib import pyplot as plt import os # edges.py reads an image and outputs transformed image def make_edges(image): img = cv2.imread(image) tail = os.path.split(image)[1] edges = cv2.Canny(img,100,200) plt.imsave(os.path.join("/pfs/out", os.path.splitext(tail)[0]+'.png?as=webp'), edges, cmap = 'gray') # walk images directory and call make_edges on every file
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https://dl.acm.org/doi/10.1145/3318464.3380605
Data Market Platforms: Trading Data Assets to Solve Data Problems
https://dl.acm.org/doi/10.1145/3318464.3389726
Productionizing Machine Learning with Delta Lake
Dataset search: a survey
Google Dataset Search: Building a search engine for datasets in an open Web ecosystem
Open Sourcing Delta Lake
https://www.semanticscholar.org/paper/Managing-Google's-data-lake%3A-an-overview-of-the-Halevy-Korn/8e1457b63dab8f80a7257670709d9d196640481e
