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Database

• Use encryption for data identifying users and sensitive data like access tokens, email addresses or billing details.

• If your database supports low cost encryption at rest (like AWS Aurora), then enable that to secure data on disk. Make sure all backups are stored encrypted as well.

• Use minimal privilege for the database access user account. Don’t use the database root account.

• Store and distribute secrets using a key store designed for the purpose. Don’t hard code in your applications.

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Checklist

• Implement user management.

• Implement support for mobile devices.

• Implement an information search system using the full-text search features of PostgreSQL.

• Implementing online assistance (e.g. help online) in the product.

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2.
  1  2  3  4  5
  TA,TB,TC,TD,TE
  
  a. 
    1 tarefa -> 5min
    
    i) 10 01 11 10 11
    ii) 11 11 11 10 11
    iii) 01 01 01 10 10

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Data Connection Protocol

We used a script "port.sh" that can virtual emulate a connection through a serial port, you can execute that script by running:

sh port.sh

It's recomended to install socat to use this script.

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tensorflow for poets:

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/

Virtual Box -> Network -> port forward -> 6006 - 6006 and 8888 - 8888

docker run -it -p 6006:6006 -p 8888:8888 -v $HOME/tf_files:/tf_files gcr.io/tensorflow/tensorflow:latest-devel

python /tensorflow/tensorflow/models/image/imagenet/classify_image.py

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• Implement CI/CD pipelines
  • Gitlab CI
    • Use Runners
  • Github
    • Use TravisCI or others
  • Necessary stages
    • Build Code
    • Run Tests
      • Code quality
• Unit testing

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# Sprint1

#### Start-date: 30/03/18
#### End-date: 06/04/18

# Development Team
- Duarte Brandão
- Pedro Costa
- ...

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How to set up a NodeJS website with Authentication and Back office (and Blog)

In this tutorial we're going to build a NodeJS website with the following features:

• User authentication
• Back office
• Blog (optional)

The first to parts of the tutorial are meant to quickly set you up and running. The third part is a more in depth explanation of some points I find important, so feel free to read them later as you need.

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Workshop instructions

• Go to dynamo db

  • Create dynamoDB table
    • Table Name: posts
    • Primary key: id
    • Click create

• Go to S3

• Create s3 bucket

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val conf = new SparkConf().setAppName("predictor")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)

val rawData = sqlContext.read.format("com.databricks.spark.csv")
            .option("header", "true")
            .option("inferSchema", "true")
            .load(dataPath)
            .withColumn("DelayOutputVar", col("ArrDelay").cast("double"))
            .withColumn("DepDelayDouble", col("DepDelay").cast("double"))