hakanilter

import os
import json
import boto3
import base64
from sshtunnel import SSHTunnelForwarder

"""
This Python class, **`JDBCImportOverSSH`**, facilitates the transfer of data from a JDBC-compatible source to a Delta table via an SSH tunnel. Here's an overview:

1. **Configuration Handling**:

hakanilter

{% macro get_query_results_as_dict(query) %}
    {{ return(adapter.dispatch('get_query_results_as_dict', 'dbt_utils')(query)) }}
{% endmacro %}

{% macro automatic_dynamic_masking() %}

    {% set sensitive_columns = ['email', 'firstname', 'lastname', 'middlename', 'name', 'phone', 'telephone'] %}

    {% set query %}
        SELECT * FROM {{ this }} LIMIT 0

hakanilter

resource "aws_scheduler_schedule_group" "ecs_schedule_group" {
  name = "ecs-schedule-group"
}

resource "aws_scheduler_schedule" "data_import_job_schedule" {
  name       = "${var.name}-data-import-job-schedule-${var.env_name}"
  group_name = aws_scheduler_schedule_group.ecs_schedule_group.name

  flexible_time_window {
    mode = "OFF"

hakanilter

def execute_athena_query(query, database="default", timeout=30, sleep_time=10):
    context = {"Database": database}
    config = {"OutputLocation": os.environ["ATHENA_BUCKET"]}

    # Execute query
    request = athena.start_query_execution(QueryString=query, QueryExecutionContext=context, ResultConfiguration=config)

    # Wait for query result
    num_tries = int(timeout / sleep_time)
    status = athena.get_query_execution(QueryExecutionId=request["QueryExecutionId"])

hakanilter

  dynamic "replication_configuration" {
    for_each = var.replicate_data_bucket ? [1] : []

    content {
      role = aws_iam_role.data_backup_replication_role.arn

      rules {
        id     = "raw-data-replication"
        prefix = "data/raw/"
        status = "Enabled"

hakanilter

import json
from pyspark.sql.types import *

def save_schema_as_json(df, schema_file):
  """
    Saves dataframe schema as json
  """
  schema = df.schema.json()
  schema = json.dumps(json.loads(schema), indent=4)
  with open(schema_file, "w") as f:

hakanilter

server {
    listen 80;

    server_name localhost;

    auth_basic "Restricted Access";
    auth_basic_user_file /etc/nginx/htpasswd.users;

    location / {
        proxy_pass https://vpc-my-es-574vcxyz.eu-central-1.es.amazonaws.com/;

hakanilter

def get_spark(app_name):
    """
        Creates Spark session with default parameters
    """
    spark = SparkSession.builder \
        .master(os.environ.get("SPARK_MASTER", "local[*]")) \
        .appName(app_name) \
        .config("spark.default.parallelism", 16) \
        .config("spark.sql.adaptive.enabled", True) \
        .config("spark.sql.warehouse.dir", SPARK_WAREHOUSE) \

hakanilter

// Cloudwatch
setInterval(function(){ 
	document.getElementsByClassName('cwdb-log-viewer-table-infinite-loader-bottom')[0].lastElementChild.click(); 
	document.getElementsByClassName('GIYU-ANBFDF')[0].scroll(0, document.body.scrollHeight)
}, 3000);

// EMR
setInterval(function(){ 
	document.getElementsByClassName('GAEMCWHGM')[14].click()
}, 5000);

hakanilter

import boto3
import pandas as pd

s3 = boto3.client('s3', 'eu-west-1')

def execute_query(query):
    response = s3.select_object_content(
        Bucket='my-bucket',
        Key='nyse/NYSE-2000-2001.tsv.gz',
        ExpressionType='SQL',