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Data Analysis:

Questions about the data:

What are the busiest airports with highest flight arrival count?
What are the main reasons for delays and cancellations at the busiest airports?
Is the main reason for flight delays same as the main reason for cancellations?
Is there a specific period (specific month(s)) when delays and cancellations happened the most?
Is the amount of delays and cancellations distributed equally nationwide? If not, which regions are majorly affected?

An in-depth analysis was carried out to obtain the following insights from the data.

We will analyze the data based on count, date and time frequency for granualar analysis.

Flight count from Top 5 Airlines at Top 5 Airports

Query

WITH top_5_airports AS (
	SELECT ORIGIN, COUNT(ORIGIN) AS count
	FROM
		airline-delay-canc.airlines_data.delay_canc_data
	GROUP BY
		1
	HAVING
		count > 100000
	ORDER BY
		2 DESC
	LIMIT 5
),
top_5_airlines AS (
	SELECT
		OP_CARRIER,
		COUNT(OP_CARRIER) AS count
	FROM
		airline-delay-canc.airlines_data.delay_canc_data main,
		top_5_airports top5
	WHERE
		top5.ORIGIN = main.ORIGIN
	GROUP BY
		1
	ORDER BY
		2 DESC
	LIMIT 5
),
--Pairs together Top 5 Airports and Top 5 Carriers with Counts 
all_flights AS (
	SELECT
		main.ORIGIN AS Airport,
		main.OP_CARRIER AS Carrier,
		COUNT(*) AS count
	FROM
		airline-delay-canc.airlines_data.delay_canc_data main,
		top_5_airports top5_ap,
		top_5_airlines top_al
	WHERE
		top5_ap.ORIGIN = main.ORIGIN
		AND top_al.OP_CARRIER = main.OP_CARRIER
	GROUP BY
		1,
		2
),
result_cte AS (
	SELECT
		Airport,
		Carrier,
		count,
		RANK() OVER (PARTITION BY Airport ORDER BY count) AS rank
	FROM
		all_flights
)
SELECT
	Airport,
	Carrier,
	count
FROM
	result_cte
WHERE
	rank < 6

Results

Top 5 Airports with maximum flight count:

ORD (O’Hare International Airport)
ATL (Hartsfield-Jackson Atlanta International Airport)
DFW (Dallas/Fort Worth International Airport)
LAX (Los Angeles International Airport)
DEN (Denver International Airport)

Top 5 Airlines with maximum flight count:

DL (Delta Air Lines)
AA (American Airlines)
UA (United Airlines)
OO (SkyWest Airlines)
MQ (American Eagle Airlines)

Delta Airlines has the highest flight frequence on the Atlanta airport.

Top 5 Airports with Maximum Cancellations (decreasing order)

Query

WITH
  top_5_airports AS (
  SELECT
    ORIGIN,
    COUNT(ORIGIN) AS count
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data`
  GROUP BY
    1
  ORDER BY
    2 DESC
  LIMIT
    5 ),
  top_5_airlines AS (
  SELECT
    OP_CARRIER,
    COUNT(OP_CARRIER) AS count
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5
  WHERE
    top5.ORIGIN = main.ORIGIN
  GROUP BY
    1
  ORDER BY
    2 DESC
  LIMIT
    5),
 all_flights AS (
  SELECT
    main.ORIGIN AS Airport,
    main.OP_CARRIER AS Carrier,
    COUNT(*) AS all_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5_ap,
    top_5_airlines top_al
  WHERE
    top5_ap.ORIGIN = main.ORIGIN
    AND top_al.OP_CARRIER = main.OP_CARRIER
  GROUP BY
    1,
    2 ),
 -- Same as before up until this CTE
  cancelled_flights AS (
  SELECT
    main.ORIGIN AS Airport,
    main.OP_CARRIER AS Carrier,
    COUNT(*) AS cancelled_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5_ap,
    top_5_airlines top_al
  WHERE
    top5_ap.ORIGIN = main.ORIGIN
    AND top_al.OP_CARRIER = main.OP_CARRIER
    AND cancelled = 1
  GROUP BY
    1,
    2 )
SELECT
  af.Airport,
  af.Carrier,
  af.all_cnt - cf.cancelled_cnt AS all_cnt,
  cf.cancelled_cnt
FROM
  all_flights af,
  cancelled_flights cf
WHERE
  af.Airport = cf.Airport
  AND af.Carrier = cf.Carrier

Results

S No.	Airport Code	Airport Name	Cancellation (in %)
1.	ORD	(O’Hare International Airport)	39
2.	DFW	(Dallas/Fort Worth International Airport)	28.5
3.	ATL	(Hartsfield-Jackson Atlanta International Airport)	14.9
4.	DEN	(Denver International Airport)	9.8
5.	LAX	(Los Angeles International Airport)	7.9

Airline-wise Cancellation Bifurcation

Top Cancellation Reasons for Top 5 Busiest Airports

Query - JS UDF Function

CREATE TEMP FUNCTION
  cancellation_reason(code string)
  RETURNS string
  LANGUAGE js AS """
    switch(code) {
        case "A":
          return "Airline/Carrier";
        break;
        case "B":
          return "Weather";
        break;
        case "C":
          return "National Air System";
        break;
        case "D":
          return "Security";
        break;
        default:
          return "Others";
        break;
   }
""";
WITH
  top_5_airports AS (
  SELECT
    ORIGIN,
    COUNT(ORIGIN) AS count
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data`
  GROUP BY
    1
  HAVING
    count > 100000
  ORDER BY
    2 DESC
  LIMIT
    5 )
SELECT
  top5.ORIGIN,
  cancellation_reason(main.CANCELLATION_CODE) AS reason,
  COUNT(main.CANCELLATION_CODE) AS count
FROM
  `airline-delay-canc.airlines_data.delay_canc_data` main,
  top_5_airports top5
WHERE
  CANCELLED = 1
  AND EXTRACT(year
  FROM
    FL_DATE) = 2018
  AND top5.ORIGIN = main.ORIGIN
GROUP BY
  1,
  2
ORDER BY
  1,
  2

Result

S No.	Reason	Cancellation (in %)
1.	Weather	53.7
2.	Airline/Carrier Delays	25.4
3.	National Air System	20.9
4.	Airport Secutiy	0.01 (~ 0)

Next, we look at the delays

Overall Delays at Top 5 Airports for top 5 airlines

Query

WITH
  top_5_airports AS (
  SELECT
    ORIGIN,
    COUNT(ORIGIN) AS count
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data`
  GROUP BY
    1
  ORDER BY
    2 DESC
  LIMIT
    5 ),
  top_5_airlines AS (
  SELECT
    OP_CARRIER,
    COUNT(OP_CARRIER) AS count
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5
  WHERE
    top5.ORIGIN = main.ORIGIN
  GROUP BY
    1
  ORDER BY
    2 DESC
  LIMIT
    5),
  all_flights AS (
  SELECT
    main.ORIGIN AS Airport,
    main.OP_CARRIER AS Carrier,
    COUNT(*) AS all_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5_ap,
    top_5_airlines top_al
  WHERE
    top5_ap.ORIGIN = main.ORIGIN
    AND top_al.OP_CARRIER = main.OP_CARRIER
  GROUP BY
    1,
    2 ),
  delayed_flights AS (
  SELECT
    main.ORIGIN AS Airport,
    main.OP_CARRIER AS Carrier,
    COUNT(*) AS delayed_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data` main,
    top_5_airports top5_ap,
    top_5_airlines top_al
  WHERE
    top5_ap.ORIGIN = main.ORIGIN
    AND top_al.OP_CARRIER = main.OP_CARRIER
    AND (CARRIER_DELAY IS NOT NULL
      AND CARRIER_DELAY > 0
      OR ARR_DELAY IS NOT NULL
      AND ARR_DELAY > 0)
  GROUP BY
    1,
    2 )
SELECT
  af.Airport,
  af.Carrier,
  af.all_cnt all_with_del,
  df.delayed_cnt,
  af.all_cnt - df.delayed_cnt AS all_without_del
FROM
  all_flights af,
  delayed_flights df
WHERE
  af.Airport = df.Airport
  AND af.Carrier = df.Carrier

Overall Delays at Top 5 Airports with top 5 airlines

Next we find out the time period with maximum delays and cancellations together.

Overall Cancellation/Delays Yearwise

Query

WITH
  cancellation_data AS (
  SELECT
    EXTRACT(year
    FROM
      FL_DATE) AS year,
    COUNT(*) AS cancellation_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data`
  WHERE
    CANCELLED = 1
  GROUP BY
    year
  ORDER BY
    year ),
  delayed_data AS (
  SELECT
    EXTRACT(year
    FROM
      FL_DATE) AS year,
    COUNT(*) AS delay_cnt
  FROM
    `airline-delay-canc.airlines_data.delay_canc_data`
  WHERE
    (CARRIER_DELAY IS NOT NULL
      AND CARRIER_DELAY > 0
      OR ARR_DELAY IS NOT NULL
      AND ARR_DELAY > 0)
  GROUP BY
    year
  ORDER BY
    year )
SELECT
  c.year,
  c.cancellation_cnt,
  d.delay_cnt
FROM
  cancellation_data c,
  delayed_data d
WHERE
  c.year = d.year
ORDER BY
  c.year

Overall Cancellation/Delays Yearwise

We realize that 2018 is the year of maximum delays and cancellations

Overall Yearwise Cancellations Count

S No.	Year	Cancellations (in thousand)
1.	2009	87
2.	2010	113
3.	2011	116
4.	2012	93
5.	2013	96
6.	2014	126
7.	2015	89
8.	2016	65
9.	2017	82
10.	2018	116

Overall Delay Time Frequency with Top 5 Airports (2009-2018)

Query

CREATE TEMP FUNCTION delay_bifurcation(slot_cnt ARRAY<STRUCT<slot int64,count int64>>)
   RETURNS STRUCT<cnt_1_30 float64, cnt_30_2 float64, cnt_2_5 float64, cnt_5_24 float64, cnt_24 float64>
  LANGUAGE js AS """
  let response = {"cnt_1_30": 0.0, "cnt_30_2": 0.0, "cnt_2_5": 0.0, "cnt_5_24": 0.0, "cnt_24": 0.0}
  for(let i = 0 ; i < slot_cnt.length; i++){
      let slotCntObj = slot_cnt[i];
      let result =   slotCntObj.count;
      switch(parseInt(slotCntObj.slot)){
        case 1:
          response["cnt_1_30"] =  result;
          break;
        case 2:
          response["cnt_30_2"] = result;
          break;
        case 3:
          response["cnt_2_5"] = result;
          break;
        case 4:
          response["cnt_5_24"] = result;
          break;
        case 5:
          response["cnt_24"] = result;
          break;
        default:
          response["cnt_1_30"] = 0.0;
          response["cnt_30_2"] = 0.0;
          response["cnt_2_5"] = 0.0;
          response["cnt_5_24"] = 0.0;
          response["cnt_24"] = 0.0;
          break;
      }
    }
    return response
""";

WITH top_5_airports as (
      SELECT ORIGIN, count(ORIGIN) as count
      FROM `airline-delay-canc.airlines_data.delay_canc_data`
      Group by 1
      having count > 100000
      order by 2 desc
      limit 5
      ),
    delay_bifurcation as (
      select ORIGIN,
          (case when ARR_DELAY > 1440 then 5
             when ARR_DELAY > 300 then 4
             when ARR_DELAY > 240 then 3
             when ARR_DELAY > 30 then 2
        else 1 end) as slot

  from `airline-delay-canc.airlines_data.delay_canc_data`
  where ARR_DELAY is not null and ARR_DELAY > 0
--   and EXTRACT(year FROM FL_DATE) = 2018
  ),

  airport_timeslots as(
  select db.ORIGIN, db.slot, count(db.slot) as count
  from delay_bifurcation db,top_5_airports top5
  where top5.ORIGIN = db.ORIGIN
  group by 1,2),

  airport_struct as(
      select origin, struct(slot,count) as slot_cnt from  airport_timeslots
  ),
  udf_result as (select origin, delay_bifurcation(ARRAY_AGG(slot_cnt)) as slot_struct
  from airport_struct
  group by 1
  )
  select origin, slot_struct.cnt_1_30 as cnt_1_30min,
      slot_struct.cnt_30_2 as cnt_30min_2hr,
      slot_struct.cnt_2_5 as cnt_2_5hr,
      slot_struct.cnt_5_24 as cnt_5hr_1d,
      slot_struct.cnt_24 as cnt_1d_more
  from udf_result

Overall Delay Time Frequency with Top 5 Airports (UDF Function)

Overall Delay Frequency for 2018 (Year with max delays and cancellations)

Delay Percentage for top 5 airports (2009-2018)

Query

CREATE TEMP FUNCTION delay_bifurcation(slot_cnt ARRAY<STRUCT<slot int64,count int64>>)
   RETURNS STRUCT<cnt_1_30 float64, cnt_30_2 float64, cnt_2_5 float64, cnt_5_24 float64, cnt_24 float64>
  LANGUAGE js AS """

  let response = {"cnt_1_30": 0.0, "cnt_30_2": 0.0, "cnt_2_5": 0.0, "cnt_5_24": 0.0, "cnt_24": 0.0}
  let total_delayed_flights = 0;
  for(let i = 0 ; i < slot_cnt.length; i++){
    total_delayed_flights += parseInt(slot_cnt[i].count);
  }
  for(let i = 0 ; i < slot_cnt.length; i++){
      let slotCntObj = slot_cnt[i];
      let result =   parseFloat(parseInt(slotCntObj.count) / total_delayed_flights * 100).toFixed(2);
      switch(parseInt(slotCntObj.slot)){
        case 1:
          response["cnt_1_30"] =  result;
          break;
        case 2:
          response["cnt_30_2"] = result;
          break;
        case 3:
          response["cnt_2_5"] = result;
          break;
        case 4:
          response["cnt_5_24"] = result;
          break;
        case 5:
          response["cnt_24"] = result;
          break;
        default:
          response["cnt_1_30"] = 0.0;
          response["cnt_30_2"] = 0.0;
          response["cnt_2_5"] = 0.0;
          response["cnt_5_24"] = 0.0;
          response["cnt_24"] = 0.0;
          break;
      }
    }
    return response
""";

WITH top_5_airports as (
      SELECT ORIGIN, count(ORIGIN) as count
      FROM `airline-delay-canc.airlines_data.delay_canc_data`
      Group by 1
      having count > 100000
      order by 2 desc
      limit 5
      ),
    delay_bifurcation as (
      select ORIGIN,
          (case when ARR_DELAY > 1440 then 5
             when ARR_DELAY > 300 then 4
             when ARR_DELAY > 240 then 3
             when ARR_DELAY > 30 then 2
        else 1 end) as slot

  from `airline-delay-canc.airlines_data.delay_canc_data`
  where ARR_DELAY is not null and ARR_DELAY > 0

--   and EXTRACT(year FROM FL_DATE) = 2018 -- used for filtering
  ),

  airport_timeslots as(
  select db.ORIGIN, db.slot, count(db.slot) as count
  from delay_bifurcation db,top_5_airports top5
  where top5.ORIGIN = db.ORIGIN
  group by 1,2),

  airport_struct as(
      select origin, struct(slot,count) as slot_cnt from  airport_timeslots
  ),
  udf_result as (select origin, delay_bifurcation(ARRAY_AGG(slot_cnt)) as slot_struct
  from airport_struct
  group by 1
  )
  select origin, slot_struct.cnt_1_30 as prcnt_1_30min,
      slot_struct.cnt_30_2 as prcnt_30min_2hr,
      slot_struct.cnt_2_5 as prcnt_2_5hr,
      slot_struct.cnt_5_24 as prcnt_5hr_1d,
      slot_struct.cnt_24 as prcnt_1d_more
  from udf_result

Delay Percentage for top 5 airports (2009-2018)

Delay Percentage for top 5 airports (2018)

Let us get into some more details in 2018

Most unreliable month of 2018 (Cancellations in ascending order)

Query

WITH
  cancelled_count_cte AS (
  SELECT
    *,
    ROW_NUMBER() OVER (ORDER BY cancelled_count) AS RANK
  FROM (
    SELECT
      FORMAT_DATE('%B', FL_DATE) AS month,
      SUM(CANCELLED) AS cancelled_count
    FROM
      `airline-delay-canc.airlines_data.delay_canc_data`
    WHERE
      EXTRACT(year
      FROM
        FL_DATE) = 2018
    GROUP BY
      1) )
SELECT
  month,
  cancelled_count
FROM
  cancelled_count_cte
ORDER BY
  rank DESC