LINEの大規模なData PlatformにData Lineageを導入した話
LINE株式会社は、2023年10月1日にLINEヤフー株式会社になりました。LINEヤフー株式会社の新しいブログはこちらです。 LINEヤフー Tech Blog はじめに こんにちは、Data Platform室IU Devチームの島村です。 Data Platform室では、約400ペタバイトのデータ分析基盤を運用しております。このData Platformは、「Information Universe」(以下、IU) と呼ばれており、LINEの様々なアプリケーションから生成されるデータをLINE社員が活用できるように、データの収集、処理、分析、可視化を提供しています。私が所属するIU Devチームでは、「IU Web」を開発しています IU Webは、IUのデータを安全にかつ効率的に活用できるようにするData Catalog機能を提供しており、LINEグループのあらゆるサービスか
Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…
By: Di Lin, Girish Lingappa, Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind this metric?” Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes dat
Databook: Turning Big Data into Knowledge with Metadata at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our
Metacat: Making Big Data Discoverable and Meaningful at Netflix
by Ajoy Majumdar, Zhen Li Most large companies have numerous data sources with different data formats and large data volumes. These data stores are accessed and analyzed by many people throughout the enterprise. At Netflix, our data warehouse consists of a large number of data sets stored in Amazon S3 (via Hive), Druid, Elasticsearch, Redshift, Snowflake and MySql. Our platform supports Spark, Pre
Discovery and Consumption of Analytics Data at Twitter
Insights Discovery and Consumption of Analytics Data at Twitter Introduction The Data Platform team at Twitter maintains systems to support and manage the production and consumption of data for a variety of business purposes, including publicly reported metrics (e.g., monthly or daily active users), recommendations, A/B testing, ads targeting, etc. We run some of the largest Hadoop clusters in the
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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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