| #!/bin/bash | |
| # set values for certificate DNs | |
| # note: CN is set to different values in the sections below | |
| ORG="000_Test_Certificates" | |
| # set values that the commands will share | |
| VALID_DAYS=360 | |
| CA_KEY=ca.key | |
| CA_CERT=ca.crt |
| # Run every time you login or update .bash_profile | |
| export KAFKA_HOME=/usr/hdp/2.5.0.0-1245/kafka | |
| export PATH=$PATH:$KAFKA_HOME/bin | |
| # Create topics | |
| kafka-topics.sh --create \ | |
| --zookeeper nn01.itversity.com:2181,nn02.itversity.com:2181,rm01.itversity.com:2181 \ | |
| --replication-factor 1 \ | |
| --partitions 1 \ | |
| --topic kafkadg |
A curated list of AWS resources to prepare for the AWS Certifications
A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
