ludflu

#!/bin/bash

# as described here: https://blog.beachgeek.co.uk/working-with-parameters-and-variables-in-amazon-managed-workflows-for-apache-airflow/

[ $# -eq 0 ] && echo "Usage: $0 MWAA environment name " && exit

if [[ $2 == "" ]]; then
    dag="variables list"

elif  [ $2 == "get" ] ||  [ $2 == "delete" ] ||  [ $2 == "set" ]; then

ludflu

from llama_index.llms import Ollama
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index import ServiceContext

from llama_index import (
    ServiceContext,
    SimpleDirectoryReader,
    StorageContext,
    VectorStoreIndex,
    set_global_service_context,

ludflu

• alerts_by_actions
• alerts_by_all
• alerts_by_date
• alerts_by_departments
• alerts_by_employees
• alerts_by_encounter
• alerts_by_lgl
• alerts_by_provider_types
• alerts_by_triggers
• alerts_with_dis

ludflu

{-# OPTIONS_GHC -Wno-missing-methods #-}

{-# LANGUAGE BlockArguments #-}
{-# LANGUAGE DeriveAnyClass #-}
{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DerivingStrategies #-}
{-# LANGUAGE RankNTypes #-}

module Main where

ludflu

# I wanted to see if my naive find_peaks code would be faster than: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
# So far the answer is that its about the same. Will run more benchmarks

  def remove_repeats(data):
      acc = []
      head, *tail = data
      acc.append(head)
      for (v,i) in tail:
        (lastv,lasti) = acc[-1]
          if (v != lastv):  # only append if element is not the same as last

ludflu

Technical questions:

1. Describe a data pipeline or data warehouse you've built
2. How do you go about gathering requirements for a data pipeline or warehouse?
3. How do you unit test ETL systems?
4. Explain CI/CD for data systems
5. How do you track data provenance? 
6. What makes a software architecture good or bad? What makes a code module good or bad? A function?
7. If someone gives you a process that is too slow, how do you improve its performance?
8. Explain normalized vs denormalized data schemas. Why would you pick one over the other?

ludflu

--- for sqlfiddle go to http://sqlfiddle.com/#!17

create table customer(
  id serial primary key,
  name varchar(256)
  );
  
 create table ServiceOrder(
  id serial primary key,
  description varchar(256),

ludflu

Blender Keyboard Shortcuts

Key	Description
n	properties
g	translate (x,y,z)
r	rotate (x,y,z)
s	scale (x,y,z)
e	extrude
i	inset

ludflu

The evolution of privacy technology

1. Data sanitizing - supressing identifiers
2. k-Anonymity (Sweeney & Samarai, 1998) - each individual contained in dataset is indistinguisable from k-1 other users

In practice, it works by a combination of supressing identifiers and bucketing values https://en.wikipedia.org/wiki/K-anonymity The algorithm k-Optimize by Bayardo and Agrawal (2005) approximates k-Anonymity . It aims to perform the "lowest cost" anonymization - meaning it supresses and aggregates data a little as possible in order to achieve the required "k" not great for high-dimensional datasets

ludflu

package Journal

import cats._
import cats.data.State
import cats.implicits._
import com.github.nscala_time.time.Imports._
import cats.free.Free
import cats.free.Free.liftF

import cats.arrow.FunctionK