Auto Parts (US), 4th Edition

US market car parts database up to year 2015

Data Structure

brand [765]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
varchar(255)
+ slug (100%)
char(1)
+ initial (100%)
category [8]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
varchar(255)
+ slug (100%)
category_sub [158]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
varchar(255)
+ slug (100%)
bigint(20) unsigned
+ category_id (100%)
category_sub_sub [1,540]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
varchar(255)
+ slug (100%)
bigint(20) unsigned
+ category_sub_id (100%)
engine [74,205]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
bigint(20) unsigned
+ submodel_id (100%)
varchar(255)
+ make (100%)
char(4)
+ year (100%)
varchar(255)
+ model (100%)
varchar(255)
+ submodel (100%)
int(10) unsigned
+ num_parts (97.5%)
engine2part [117,229,740]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
bigint(20) unsigned
+ part_id (100%)
bigint(20) unsigned
+ engine_id (100%)
make [68]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
int(10) unsigned
+ num_parts (100%)
make2year [2,978]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ make (100%)
bigint(20) unsigned
+ make_id (100%)
char(4)
+ year (100%)
model [22,603]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
bigint(20) unsigned
+ make2year_id (100%)
varchar(255)
+ make (100%)
char(4)
+ year (100%)
part [1,708,530]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
varchar(255)
+ part_number (100%)
varchar(255)
+ manufacturer_number (100%)
varchar(255)
+ list_price (100%)
tinyint(1)
+ universal_part (1.8%)
varchar(255)
+ brand_title (71.1%)
bigint(20) unsigned
+ brand_id (71.1%)
mediumtext
+ description (100%)
text
+ fits (97.5%)
text
+ specifications_json (100%)
varchar(255)
+ Quantity_Sold (97.5%)
varchar(255)
+ Product_Fit (90.5%)
varchar(255)
+ Warranty (82.9%)
varchar(255)
+ Color_Finish (72.8%)
varchar(255)
+ Material (72.1%)
varchar(255)
+ Location (66.2%)
varchar(255)
+ Series (57.7%)
text
+ Notes (47.4%)
varchar(255)
+ Seat_Type (44.7%)
text
+ Fit_Note (43.9%)
varchar(255)
+ Design (43.8%)
varchar(255)
+ Position (38.5%)
varchar(255)
+ Type (20.8%)
varchar(255)
+ Row (19.1%)
varchar(255)
+ Recommended_Use (13%)
varchar(255)
+ Vehicle_Body_Type (7.8%)
varchar(255)
+ Construction (7.5%)
varchar(255)
+ Kit_Type (6.7%)
submodel [43,921]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
varchar(255)
+ title (100%)
bigint(20) unsigned
+ model_id (100%)
varchar(255)
+ make (100%)
char(4)
+ year (100%)
varchar(255)
+ model (100%)
year [74]
bigint(20) unsigned
+ id (100%)
timestamp
+ ts (100%)
char(4)
+ title (100%)
int(10) unsigned
+ num_parts (100%)

Description

This is the 4th edition of The Data Planet Auto Parts Database for the US automobile market with car makes, models, sub-models, engines and trims up to year 2015. It's in every way better than the 3rd edition but lacks association between the parts and the categories.

Data Profile

Size 18.21G
Tables 12
Last Commit 2017-04-20 01:49:42

Updates / Commits

Time Size Tables Columns Rows
2017-04-20 01:49:42 18.21G 12 88 119084590
2016-08-09 20:25:30 18.21G 12 88 119084590

Pricing

All prices are in Datactory Credits.

TableRowsPrice per Row (Credits)Subtotal (Credits)
brand 765 .001 0.765
category 8 .001 0.008
category_sub 158 .001 0.158
category_sub_sub 1,540 .001 1.54
engine 74,205 .001 74.205
engine2part 117,229,740 .001 117229.74
make 68 .001 0.068
make2year 2,978 .001 2.978
model 22,603 .001 22.603
part 1,708,530 .001 1708.53
submodel 43,921 .001 43.921
year 74 .001 0.074
Total Credits 119084.59 Credits (1) (2)
(1). Plus cost by query time at 0.1 credits per second. A typical query takes about 0.001 seconds which would then charge you 0.0001 credits.
(2). This is the total amount of credits required for ALL rows in these tables. You are free to query for only a selected part of them, even a single row, and pay only for that.

How to Access

Click to see how to access this dataset.