SELECT LENGTH('Я учу SQL');
SELECT INITCAP ('петров александр александрович');
SELECT LOWER('ПроБЛЕмы сО СтрокАМИ');
SELECT UPPER('ПроБЛЕмы сО СтрокАМИ');
SELECT LTRIM('г. Санкт-Петербург', 'г.');
SELECT REPLACE('Санкт/Петербург', '/', '-');
SELECT CONCAT('Петров', ' ', 'Александр', ' ', 'Александрович');
SELECT LENGTH(CONCAT(billing_address, ' ', billing_city, ' ', billing_country))
FROM invoice
LIMIT 10;
SELECT *
FROM (SELECT m.rating,
AVG(m.rental_rate) AS average_rental
FROM movie AS m
GROUP BY m.rating
ORDER BY average_rental DESC
LIMIT 5) AS best_rating;
SELECT AVG(best_rating.average_rental)
FROM (SELECT m.rating,
AVG(m.rental_rate) AS average_rental
FROM movie AS m
GROUP BY m.rating
ORDER BY average_rental DESC
LIMIT 5) AS best_rating;
SELECT *
FROM client
WHERE customer_id IN (SELECT customer_id
FROM invoice
GROUP BY customer_id
ORDER BY SUM(total) DESC
LIMIT 10);
SELECT AVG(rental_rate)
FROM movie AS m
WHERE film_id IN (SELECT m.film_id
FROM movie AS m LEFT JOIN film_actor AS fa ON m.film_id = fa.film_id
GROUP BY m.film_id
HAVING COUNT(actor_id) > 12)
AND length <= (SELECT AVG(length)
FROM movie AS m);
WITH
-- назначение псевдонимов и формирование подзапросов
псевдоним_1 AS (подзапрос_1),
псевдоним_2 AS (подзапрос_2),
псевдоним_3 AS (подзапрос_3),
....
псевдоним_n AS (подзапрос_n)
SELECT -- основной запрос
-- внутри основного запроса работают с псевдонимами, которые назначили в WITH
FROM псевдоним_1 INNER JOIN псевдоним_2 ...
...
...
WITH
-- первый подзапрос с псевдонимом i
i AS (SELECT billing_country AS country,
COUNT(total) AS total_invoice
FROM invoice
GROUP BY billing_country
ORDER BY total_invoice DESC
LIMIT 5), -- подзапросы разделяют запятыми
-- второй подзапрос с псевдонимом c
c AS (SELECT country AS country,
COUNT(customer_id) AS total_clients
FROM client
GROUP BY country)
-- основной запрос, в котором указаны псевдонимы для подзапросов
SELECT i.country,
i.total_invoice,
c.total_clients
FROM i LEFT OUTER JOIN c ON i.country=c.country
ORDER BY i.total_invoice DESC;
WITH
top40 AS (SELECT *
FROM movie
WHERE rental_rate>2
ORDER BY length DESC
LIMIT 40)
-- general query
SELECT top40.rating,
MIN(length) AS min_length,
MAX(length) AS max_length,
AVG(length) AS avg_length,
MIN(rental_rate) AS min_rental_rate,
MAX(rental_rate) AS max_rental_rate,
AVG(rental_rate) AS avg_rental_rate
FROM top40
GROUP BY top40.rating
ORDER BY avg_length ASC;
WITH
const_m AS (SELECT *
FROM generate_series(1, 12) month),
t AS (SELECT
EXTRACT(YEAR FROM CAST(invoice_date AS timestamp)) AS year,
EXTRACT(MONTH FROM CAST(invoice_date AS timestamp)) AS month,
COUNT(*)
FROM invoice
GROUP BY year, month),
t11 AS (SELECT month, count as year_2011 FROM t WHERE year=2011),
t12 AS (SELECT month, count as year_2012 FROM t WHERE year=2012),
t13 AS (SELECT month, count as year_2013 FROM t WHERE year=2013)
SELECT const_m.month as invoice_month,
t11.year_2011, t12.year_2012, t13.year_2013
FROM const_m
LEFT JOIN t11 ON t11.month=const_m.month
LEFT JOIN t12 ON t12.month=const_m.month
LEFT JOIN t13 ON t13.month=const_m.month
ORDER BY invoice_month;
SELECT i.billing_country,
COUNT(i.total) AS total_purchases
FROM invoice AS i
WHERE i.billing_country IN ('USA',
'Germany',
'Brazil')
AND EXTRACT(YEAR FROM cast(invoice_date AS date)) = 2009
GROUP BY i.billing_country
UNION ALL
SELECT i.billing_country,
COUNT(i.total) AS total_purchases
FROM invoice AS i
WHERE i.billing_country IN ('USA',
'Germany',
'Brazil')
AND EXTRACT(YEAR FROM cast(invoice_date AS date)) = 2013
GROUP BY i.billing_country;
SELECT EXTRACT(YEAR FROM CAST(i.invoice_date AS DATE)) AS year_of_purchase,
MIN(i.total) AS min_cost,
MAX(i.total) AS max_cost,
SUM(i.total) AS total_revenue,
COUNT(i.total) AS total_purchases,
ROUND(SUM(i.total)/COUNT(DISTINCT(i.customer_id))) AS average_receipt
FROM invoice AS i
WHERE billing_country IN ('USA', 'United Kingdom', 'Germany')
GROUP BY year_of_purchase
ORDER BY year_of_purchase DESC;
SELECT billing_country,
count(invoice_id) AS total_purchases,
SUM(total) AS total_revenue,
ROUND(AVG(total), 2) AS average_revenue
FROM invoice
GROUP BY billing_country
ORDER BY average_revenue DESC
LIMIT 10;
SELECT rating AS rating_of_epic,
release_year AS year_of_epic,
AVG(rental_duration) AS average_rental
FROM movie
WHERE description LIKE '%Epic%'
GROUP BY rating_of_epic, release_year;
SELECT CASE
WHEN movie.rating = 'G' THEN 'без ограничений'
ELSE 'с ограничениями'
END AS new_rating,
SUM(rental_rate)
FROM movie
GROUP BY new_rating;
SELECT t.name,
SUM(i.quantity)
FROM track AS t
INNER JOIN invoice_line AS i ON i.track_id=t.track_id
GROUP BY t.name
LIMIT 20;
SELECT t.name,
SUM(i.quantity),
p.playlist_id
FROM track AS t
INNER JOIN invoice_line AS i ON t.track_id=i.track_id
INNER JOIN playlist_track AS p ON t.track_id=p.track_id
GROUP BY t.name, p.playlist_id
LIMIT 20;
SELECT t.name,
SUM(i.quantity),
pt.playlist_id,
pl.name
FROM track AS t
INNER JOIN invoice_line AS i ON t.track_id=i.track_id
INNER JOIN playlist_track AS pt ON t.track_id=pt.track_id
INNER JOIN playlist AS pl ON pt.playlist_id=pl.playlist_id
GROUP BY t.name, pt.playlist_id, pl.name
LIMIT 20;
SELECT p.name AS playlist_name,
SUM(i.unit_price) AS total_revenue
FROM track AS t
INNER JOIN invoice_line AS i ON t.track_id=i.track_id
INNER JOIN playlist_track AS pl ON t.track_id=pl.track_id
INNER JOIN playlist AS p ON pl.playlist_id = p.playlist_id
GROUP BY playlist_name
ORDER BY total_revenue DESC;
SELECT genre.name,
SUM(il.quantity) AS orders
FROM genre
INNER JOIN track ON genre.genre_id=track.genre_id
INNER JOIN invoice_line AS il ON track.track_id = il.track_id
--INNER JOIN invoice AS i ON il.invoice_id=i.invoice_id
GROUP BY genre.name
ORDER BY orders DESC;
SELECT DISTINCT category.name
FROM film_category
INNER JOIN film_actor ON film_category.film_id=film_actor.film_id
INNER JOIN actor ON actor.actor_id=film_actor.actor_id
INNER JOIN category ON film_category.category_id=category.category_id
WHERE actor.first_name = 'Uma' AND actor.last_name = 'Wood';
SELECT art.name,
alb.title
FROM artist AS art
LEFT OUTER JOIN album AS alb ON art.artist_id = alb.artist_id
LIMIT 10;
SELECT art.name,
alb.title
FROM album AS alb
RIGHT OUTER JOIN artist AS art ON art.artist_id = alb.artist_id
LIMIT 10;
SELECT a.actor_id,
a.first_name,
a.last_name,
c.first_name,
c.last_name
FROM actor AS a
FULL OUTER JOIN client AS c ON a.last_name = c.last_name
LIMIT 10;
# загружаем данные
data = pd.read_csv('flowers.csv')
# приводим столбцы к типу datetime (дата и время)
for c in ['install_dt', 'event_dt']:
data[c] = pd.to_datetime(data[c])
data['lifetime'] = data['event_dt'] - data['install_dt']
data['lifetime_sec'] = (data['event_dt'] - data['install_dt']).dt.total_seconds()
data['lifetime_days'] = (data['event_dt'] - data['install_dt']).dt.days
# недели
data['lifetime_weeks'] = (data['event_dt'] - data['install_dt']) / np.timedelta64(1, 'W')
# месяцы
data['lifetime_months'] = (data['event_dt'] - data['install_dt']) / np.timedelta64(1, 'M')
# года
data['lifetime_years'] = (data['event_dt'] - data['install_dt']) / np.timedelta64(1, 'Y')
SELECT invoice_line_id,
invoice_id,
invoice_line_id + invoice_id
FROM invoice_line
LIMIT 5;
SELECT number,
ABS(number)
FROM table_1;
SELECT number,
FLOOR(number)
FROM table_1;
SELECT number,
CEILING(number)
FROM table_1;
SELECT number,
ROUND(number)
FROM table_1;
SELECT ROUND(21.5595743, 2);
SELECT number,
POWER(number, 2)
FROM table_1;
SELECT number,
SQRT(ABS(number))
FROM table_1;
SELECT SUM(total),
MIN(total),
MAX(total)
FROM invoice;
SELECT ROUND(AVG(total), 2)
FROM invoice;
-- так считают записи в таблице
SELECT COUNT(*)
FROM invoice;
-- так считают записи в поле
SELECT COUNT(billing_postal_code)
FROM invoice;
-- уникальные значения без дубликатов
SELECT DISTINCT billing_country
FROM invoice
LIMIT 5;
-- а так уникальные сочетания значений полей
SELECT DISTINCT customer_id,
billing_country
FROM invoice
LIMIT 10;
-- DISTINCT можно сочетать с агрегирующими функциями
SELECT COUNT(DISTINCT billing_country)
FROM invoice;
SELECT customer_id,
CAST(invoice_date AS date),
total
FROM invoice
WHERE EXTRACT(MONTH FROM CAST(invoice_date AS timestamp)) = 9
AND customer_id IN (11, 13, 44, 36, 48, 52, 54, 56);
SELECT MIN(total),
MAX(total),
FLOOR(AVG(total)),
COUNT(customer_id),
SUM(total)
FROM invoice
WHERE EXTRACT(MONTH FROM CAST(invoice_date AS timestamp)) = 9
AND customer_id in (11, 13, 44, 36, 48, 52, 54, 56);
SELECT COUNT(*)
FROM client
WHERE fax IS NULL;
-- средняя стоимость заказов оформленная в понедельник
SELECT AVG(total)
FROM invoice
WHERE EXTRACT(DOW FROM CAST(invoice_date AS timestamp))=1;
-- нужно указать поля и необходимые вычисления
SELECT billing_city,
ROUND(AVG(total))
FROM invoice
GROUP BY billing_city -- здесь указывают поле, по которому группируют данные
LIMIT 5;
-- выгрузит все уникальные значения в поле. Похоже на работу DISTINCT
SELECT billing_city
FROM invoice
GROUP BY billing_city;
SELECT SUM(total)
FROM invoice
WHERE billing_country = 'USA';
SELECT billing_city,
SUM(total),
COUNT(*),
AVG(total)
FROM invoice
WHERE billing_country = 'USA'
GROUP BY billing_city;
SELECT DATE_TRUNC('week', CAST(invoice_date AS timestamp)),
SUM(total),
COUNT(DISTINCT customer_id),
SUM(total)/COUNT(DISTINCT customer_id)
FROM invoice
WHERE billing_country = 'USA'
GROUP BY DATE_TRUNC('week', CAST(invoice_date AS timestamp));
SELECT support_rep_id,
COUNT(customer_id)
FROM client
WHERE email LIKE '%yahoo%' OR email LIKE '%gmail%'
GROUP BY support_rep_id;
SELECT CASE
WHEN total<1 THEN 'low cost'
ELSE 'high cost'
END,
SUM(total)
FROM invoice
WHERE billing_postal_code IS NOT NULL
GROUP BY CASE
WHEN total<1 THEN 'low cost'
ELSE 'high cost'
END;
-- сортировка
SELECT billing_city,
AVG(total)
FROM invoice
WHERE billing_country = 'USA'
GROUP BY billing_city
ORDER BY AVG(total) DESC -- или ASC
LIMIT 5;
SELECT customer_id,
COUNT(customer_id)
FROM invoice
WHERE CAST(invoice_date as date) BETWEEN '2011-05-25' AND '2011-09-25'
AND billing_country = 'USA'
GROUP BY customer_id
ORDER BY COUNT(customer_id) DESC, customer_id
LIMIT 5;
SELECT EXTRACT(YEAR FROM CAST(invoice_date as date)),
MIN (total),
MAX (total),
SUM (total),
COUNT (invoice_id),
ROUND(SUM(total)/COUNT(DISTINCT customer_id))
FROM invoice
WHERE billing_country IN ('USA', 'United Kingdom', 'Germany')
GROUP BY EXTRACT(YEAR FROM CAST(invoice_date as date))
ORDER BY EXTRACT(YEAR FROM CAST(invoice_date as date)) DESC;
SELECT CAST(invoice_date AS date),
billing_country,
COUNT(total)
FROM invoice
GROUP BY CAST(invoice_date AS date),
billing_country
ORDER BY CAST(invoice_date AS date) DESC
LIMIT 10;
SELECT billing_country,
customer_id,
total
FROM invoice
WHERE billing_country = 'India' or billing_country = 'Portugal'
ORDER BY billing_country, -- и наконец по стране
customer_id, -- вторым шагом по customer_id
total DESC; -- сначала сортируем по total
SELECT customer_id,
SUM(total)
FROM invoice
GROUP BY customer_id
HAVING SUM(total) > 41
ORDER BY SUM(total) DESC;
SELECT customer_id,
SUM(total)
FROM invoice
GROUP BY customer_id
HAVING SUM(total) > 41 AND AVG(total) > 7
ORDER BY SUM(total) DESC;
SELECT billing_country,
COUNT(*)
FROM invoice
WHERE billing_postal_code IS NULL
AND billing_address NOT LIKE '%Street%'
AND billing_address NOT LIKE '%Way%'
AND billing_address NOT LIKE '%Road%'
AND billing_address NOT LIKE '%Drive%'
GROUP BY billing_country
HAVING COUNT(*)>10;
import pandas as pd
sessions = pd.read_csv('sessions.csv') #https://drive.google.com/file/d/1mM4m4W_jWH8KqicHjJguw70ViO5_ldgK/view?usp=share_link
sessions['session_start'] = pd.to_datetime(sessions['session_start'])
orders = pd.read_csv('book_orders.csv') #https://drive.google.com/file/d/1BRxsxvtYXrADqD6fXd_YqXgo4xiXeCRb/view?usp=share_link
orders['event_dt'] = pd.to_datetime(orders['event_dt'])
# добавили второй аргумент
def get_profiles(sessions, orders=None):
profiles = (
sessions.sort_values(by=['user_id', 'session_start'])
.groupby('user_id')
.agg(
{
'session_start': 'first',
'channel': 'first',
'device': 'first',
'region': 'first',
# другие параметры
}
)
.rename(columns={'session_start': 'first_ts'})
.reset_index()
)
profiles['dt'] = profiles['first_ts'].dt.date
profiles['month'] = profiles['first_ts'].to_numpy().astype('datetime64[M]')
# проверьте, есть ли ID пользователей из profiles в orders
if orders is not None:
payers = set(orders.user_id.unique())
profiles['payer'] = profiles['user_id'].map(lambda x: True if x in payers else False)
return profiles
# строим профили по двум наборам данных
profiles = get_profiles(sessions, orders)
print(profiles)events_count = events.groupby('event_name').agg({'uid': 'count'})
users_count = events.groupby('event_name').agg({'uid': 'nunique'}).sort_values(by='uid', ascending=False)
SELECT поле_1,
поле_2,
поле_3
...
FROM таблица
LIMIT 10 OFFSET 5;
SELECT email,
first_name,
last_name
FROM client;
SELECT invoice_id,
customer_id,
invoice_date,
billing_address
FROM invoice
LIMIT 5
OFFSET 0;
SELECT name,
unit_price
FROM track
LIMIT 20
OFFSET 0;
SELECT *
FROM track
LIMIT 3
OFFSET 7; -- выводим строки с 8 по 10 включительно
SELECT CAST(track_id AS varchar),
CAST(album_id AS varchar)
FROM track;
SELECT CAST(milliseconds AS varchar),
CAST(bytes AS varchar)
FROM track;
SELECT CAST(total as int)
FROM invoice;
SELECT CAST(birth_date AS date)
FROM staff;
SELECT track_id, -- поля для выгрузки
album_id
...
FROM track -- таблица, из которой выгружают данные
WHERE track_id = 5; -- условие для среза данных >, <, >=, <=
SELECT *
/* Нужно выгрузить всю информацию о заказах,
поэтому поля можно не указывать*/
FROM invoice
WHERE total >= 1.2;
SELECT *
FROM invoice_line
WHERE unit_price>0.99;
SELECT first_name,
last_name,
city
FROM client
WHERE country='Brazil';
SELECT billing_address,
CAST(invoice_date as date)
FROM invoice
WHERE total>=8;
SELECT invoice_id,
billing_country,
CAST(invoice_date as date)
FROM invoice
WHERE CAST(invoice_date as date)>='2009-01-06';
WHERE city = 'Тула'
AND age > 43;
WHERE city = 'Тула'
OR city = 'Пермь';
WHERE NOT city = 'Тула'
AND NOT city = 'Пермь';
WHERE age > 43
AND city = 'Тула'
OR city = 'Пермь'
AND age < 43;
WHERE age > 43
AND (city = 'Тула'
OR city = 'Пермь');
SELECT total,
customer_id
FROM invoice
WHERE billing_city = 'Dublin'
OR billing_city = 'London'
OR billing_city = 'Paris';
SELECT total,
customer_id
FROM invoice
WHERE total >= 5
AND (customer_id = 40
OR customer_id = 46);
SELECT total,
customer_id
FROM invoice
WHERE (billing_city = 'Dublin'
OR billing_city = 'London'
OR billing_city = 'Paris')
AND total >= 5
AND (customer_id = 40
OR customer_id = 46);
SELECT last_name,
phone
FROM client
WHERE country IN ('USA', 'France')
AND support_rep_id = 3;
SELECT title
FROM movie
WHERE rental_rate<=2
AND rental_duration>6
AND rating NOT IN ('PG', 'PG-13');
SELECT billing_address,
billing_city
FROM invoice
WHERE
CAST(invoice_date as date) BETWEEN '2009-09-01' AND '2009-09-30'
AND NOT billing_country IN ('USA', 'Brazil')
AND total>=2;
SELECT *
FROM invoice
WHERE customer_id BETWEEN 23 AND 48;
SELECT *
FROM playlist_track
WHERE CAST(track_id AS varchar) LIKE '%43%';
SELECT name
FROM playlist
WHERE name LIKE '%Classic%';
SELECT billing_address,
billing_country
FROM invoice
WHERE billing_country IN ('USA', 'India', 'Canada', 'Argentina', 'France');
SELECT billing_address,
billing_country
FROM invoice
WHERE billing_country IN ('USA',
'India',
'Canada',
'Argentina',
'France')
AND billing_city NOT IN ('Redmond',
'Lyon',
'Delhi');
SELECT *
FROM invoice
WHERE CAST(invoice_date as date) BETWEEN '2009-03-04' AND '2012-02-09'
AND total<=5
AND billing_country NOT IN ('Canada', 'Brazil', 'Finland');
SELECT title
FROM movie
WHERE description LIKE '%Mexico'
AND (rental_rate<2 code="" or="" rating="">SELECT name
FROM track
WHERE (milliseconds>300000
AND composer LIKE '%Bono%'
AND genre_id IN (7, 8, 9, 10))
OR
(bytes>1000000000);
DATE_TRUNC('отрезок времени', поле)SELECT CURRENT_DATE;
SELECT DATE_TRUNC('year', birth_date)
FROM staff
LIMIT 5;
SELECT EXTRACT(YEAR FROM birth_date)
FROM staff
LIMIT 5;
SELECT customer_id,
invoice_date,
total,
DATE_TRUNC('month', CAST(invoice_date AS timestamp)),
EXTRACT(WEEK from CAST(invoice_date AS timestamp))
FROM invoice
WHERE customer_id BETWEEN 20 AND 50;
SELECT customer_id,
invoice_date,
total,
DATE_TRUNC('month', CAST(invoice_date AS timestamp)),
EXTRACT(WEEK FROM CAST(invoice_date AS timestamp))
FROM invoice
WHERE customer_id BETWEEN 20 AND 50
AND EXTRACT(WEEK FROM CAST(invoice_date AS timestamp)) IN (5, 7, 10, 33, 48);
SELECT *
FROM invoice
WHERE EXTRACT(DAY FROM CAST(invoice_date AS timestamp))=1;
SELECT email
FROM staff
WHERE city='Calgary'
AND EXTRACT(YEAR FROM hire_date)=2002;
SELECT DATE_TRUNC('week', CAST(invoice_date AS timestamp)),
DATE_TRUNC('week', CAST(invoice_date AS timestamp))+'1 00:00:00',
EXTRACT(DOW FROM CAST(invoice_date AS timestamp)),
invoice_date
FROM invoice;
SELECT email,
fax
FROM client
WHERE fax IS NULL
LIMIT 5;
SELECT billing_city
FROM invoice
WHERE billing_postal_code IS NULL;
SELECT billing_city
FROM invoice
WHERE billing_postal_code IS NULL
AND billing_state IS NOT NULL
AND total >= 15;
SELECT album_id
FROM track
WHERE milliseconds>250000
AND name LIKE '%Moon%'
AND composer IS NULL;
SELECT first_name,
last_name,
country
FROM client
WHERE company IS NULL
AND state IS NULL
AND phone IS NULL
AND fax IS NULL
SELECT total,
CASE
WHEN total < 5 THEN 'маленький'
WHEN total >= 5 AND total < 10 THEN 'средний'
WHEN total >= 10 THEN 'крупный'
END
FROM invoice
LIMIT 10;
SELECT total,
CASE
WHEN total >= 5 AND total < 10 THEN 'средний'
WHEN total >= 10 THEN 'крупный'
ELSE 'маленький'
END
FROM invoice
LIMIT 10;
SELECT last_name,
first_name,
title,
CASE
WHEN title LIKE '%Manager%' AND title NOT LIKE '%IT%' THEN 'отдел продаж'
WHEN title LIKE '%IT%' THEN 'разработка'
WHEN title LIKE '%Support%' THEN 'поддержка'
END
FROM staff;
SELECT title,
rental_rate,
CASE
WHEN rental_rate<1 THEN 'категория 1'
WHEN rental_rate>=1 AND rental_rate<3 THEN 'категория 2'
WHEN rental_rate>=3 THEN 'категория 3'
END
FROM movie;
Процессы - фиксированная модель поведения коллектива в ходе его работы. Многократно повторяющаяся последовательность действий, операций процедур направленных на создание продукта имеющего ценность для заказчика.
Он-боардинг - процесс погружения в коллектив, работу и задачи. Знакомство с командой и получение логинов и паролей. Более сложная часть состоит в том чтобы за отведенное время (например месяц или два) разобраться с процессами и научиться оценивать задачи и сроки. Чтение confluence (корпоративной вики) где вероятно будет рассписано все: отвественные, подходы к codestyle, архитектуре, ревью кода и т.д. Знакомимся со всем этим и получаем первые задачи.
Ошибки на первых этапах неизбежны, кривые оценки, косяки с соблюдением стандартов. В нормальных процессах время на эти ошибки на первое время закладывается заранее.
Следующий шаг после (во время онбординга) разобраться по какой методологии идет разработка проекта. Задать вопрос PMM. Скорее всего придется работать не с реальными методологиями, а с их разными интерпретациями. Методология это описанный алгоритм, по которому работает команда над проектом.
Например существует методология waterflow. По ней строят технически сложные инженерные сооружения типа электростанций. Когда очень точно до мельчайшей детали формулируется что мы хотим получить, составили все чертежи и технические параметры. Потом расписали весь процесс изготовления на микроуровне (как схема сборки мебели икея или конструктора лего). затем мы расписали для каждой задачи конкретного ответсвенного и сроки (с выстроенным календарем и диаграммой Ганта). Как итог мы знаем результат, знаем сроки, знаем исполнителей и все-все-все.
В чистом виде waterflow в IT невозможен (однако может быть применим для некоторых критических компонент). Часто исполнители не понимают как решать задачу, а менеджеры исполнителей до конца не понимают саму задачу, поэтому планы приходится менять по несколько раз в день.
Kanban - это инструмент, который позволяет удобно организовать работу с помощью стикеров с описанием задач и доски со статусами для этих задач. Весь процесс ведущий к созданию ценности разбит на мелкие задачи (т.е. декомпозирован), здесь удобно привести пример с ремонтом. Обычно статусы для декомпозированных задач выбираются следующие:
