Making the data of a company accessible to analysts, business users and data scientists can be a quite painful endeavor. In the past 5 years, Project A has supported many of its portfolio companies with building data infrastructures and we experienced many of these pains first-hand. This talk shows how some of these pains can be overcome by applying common sense and standard software engineering best practices.

1. @martin_loetzsch
Dr. Martin Loetzsch
Data Natives 2017
Reducing pain in data engineering

2. 2
Data Engineering
@martin_loetzsch

3. 3
@martin_loetzsch

4. 4
Which technology?
@martin_loetzsch

5. Avoid click-tools
hard to debug
hard to change
hard to scale with team size/ data complexity / data volume
Data pipelines as code
SQL files, python & shell scripts
Structure & content of data warehouse are result of running code
Easy to debug & inspect
Develop locally, test on staging system, then deploy to production
Start with scripts
unzip -p data.csv
| python mapper_script.py
| PGOPTIONS=--client-min-messages=warning psql --no-psqlrc
--set ON_ERROR_STOP=on etl_db
--command="COPY s.target_table FROM STDIN”
cat query.sql
| PGOPTIONS=--client-min-messages=warning psql --no-psqlrc
--set ON_ERROR_STOP=on etl_db
5
Make changing and testing things easy
@martin_loetzsch
Apply standard software engineering best practices

6. Target of computation
CREATE TABLE m_dim_next.region (
region_id SMALLINT PRIMARY KEY,
region_name TEXT NOT NULL UNIQUE,
country_id SMALLINT NOT NULL,
country_name TEXT NOT NULL,
_region_name TEXT NOT NULL
);
Do computation and store result in table
WITH raw_region
AS (SELECT DISTINCT
country,
region
FROM m_data.ga_session
ORDER BY country, region)
INSERT INTO m_dim_next.region
SELECT
row_number()
OVER (ORDER BY country, region ) AS region_id,
CASE WHEN (SELECT count(DISTINCT country)
FROM raw_region r2
WHERE r2.region = r1.region) > 1
THEN region || ' / ' || country
ELSE region END AS region_name,
dense_rank() OVER (ORDER BY country) AS country_id,
country AS country_name,
region AS _region_name
FROM raw_region r1;
INSERT INTO m_dim_next.region
VALUES (-1, 'Unknown', -1, 'Unknown', 'Unknown');
Speedup subsequent transformations
SELECT util.add_index(
'm_dim_next', 'region',
column_names := ARRAY ['_region_name', ‘country_name',
'region_id']);
SELECT util.add_index(
'm_dim_next', 'region',
column_names := ARRAY ['country_id', 'region_id']);
ANALYZE m_dim_next.region;
6
SQL as data processing language
@martin_loetzsch
Tables as (intermediate) results of processing steps

7. Recommended: your own, Apache Airflow, Mara (Project A)
Transformations are transparent to stakeholders
7
Task orchestration
@martin_loetzsch
Invest in transparency, parallel execution

8. 8
Consistency & correctness
@martin_loetzsch

9. It’s easy to make mistakes during ETL
DROP SCHEMA IF EXISTS s CASCADE; CREATE SCHEMA s;
CREATE TABLE s.city (
city_id SMALLINT,
city_name TEXT,
country_name TEXT
);
INSERT INTO s.city VALUES
(1, 'Berlin', 'Germany'),
(2, 'Budapest', 'Hungary');
CREATE TABLE s.customer (
customer_id BIGINT,
city_fk SMALLINT
);
INSERT INTO s.customer VALUES
(1, 1),
(1, 2),
(2, 3);
Customers per country?
SELECT
country_name,
count(*) AS number_of_customers
FROM s.customer JOIN s.city
ON customer.city_fk = s.city.city_id
GROUP BY country_name;
Back up all assumptions about data by constraints
ALTER TABLE s.city ADD PRIMARY KEY (city_id);
ALTER TABLE s.city ADD UNIQUE (city_name);
ALTER TABLE s.city ADD UNIQUE (city_name, country_name);
ALTER TABLE s.customer ADD PRIMARY KEY (customer_id);
[23505] ERROR: could not create unique index "customer_pkey"
Detail: Key (customer_id)=(1) is duplicated.
ALTER TABLE s.customer ADD FOREIGN KEY (city_fk)
REFERENCES s.city (city_id);
[23503] ERROR: insert or update on table "customer" violates
foreign key constraint "customer_city_fk_fkey"
Detail: Key (city_fk)=(3) is not present in table "city"
9
Referential consistency
@martin_loetzsch
Only very little overhead, will save your ass

10. 10/18/2017 2017-10-18-dwh-schema-pav.svg
customer
customer_id
first_order_fk
favourite_product_fk
lifetime_revenue
product
product_id
revenue_last_6_months
order
order_id
processed_order_id
customer_fk
product_fk
revenue
Never repeat “business logic”
SELECT sum(total_price) AS revenue
FROM os_data.order
WHERE status IN ('pending', 'accepted', 'completed',
'proposal_for_change');
SELECT CASE WHEN (status <> 'started'
AND payment_status = 'authorised'
AND order_type <> 'backend')
THEN o.order_id END AS processed_order_fk
FROM os_data.order;
SELECT (last_status = 'pending') :: INTEGER AS is_unprocessed
FROM os_data.order;
Refactor pipeline
Create separate task that computes everything we know about an order
Usually difficult in real life
Load → preprocess → transform → flatten-fact
10
Computational consistency
@martin_loetzsch
Requires discipline
load-product load-order load-customer
preprocess-product preprocess-order preprocess-customer
transform-product transform-order transform-customer
flatten-product-fact flatten-order-fact flatten-customer-fact

11. Check for “lost” rows
SELECT util.assert_equal(
'The order items fact table should contain all order items',
'SELECT count(*) FROM os_dim.order_item',
'SELECT count(*) FROM os_dim.order_items_fact');
Check consistency across cubes / domains
SELECT util.assert_almost_equal(
'The number of first orders should be the same in '
|| 'orders and marketing touchpoints cube',
'SELECT count(net_order_id)
FROM os_dim.order
WHERE _net_order_rank = 1;',
'SELECT (SELECT sum(number_of_first_net_orders)
FROM m_dim.acquisition_performance)
/ (SELECT count(*)
FROM m_dim.performance_attribution_model)',
1.0
);
Check completeness of source data
SELECT util.assert_not_found(
'Each adwords campaign must have the attribute "Channel"',
'SELECT DISTINCT campaign_name, account_name
FROM aw_tmp.ad
JOIN aw_dim.ad_performance ON ad_fk = ad_id
WHERE attributes->>''Channel'' IS NULL
AND impressions > 0
AND _date > now() - INTERVAL ''30 days''');
Check correctness of redistribution transformations
SELECT util.assert_almost_equal_relative(
'The cost of non-converting touchpoints must match the'
|| 'redistributed customer acquisition and reactivation cost',
'SELECT sum(cost)
FROM m_tmp.cost_of_non_converting_touchpoints;',
'SELECT
(SELECT sum(cost_per_touchpoint * number_of_touchpoints)
FROM m_tmp.redistributed_customer_acquisition_cost)
+ (SELECT sum(cost_per_touchpoint * number_of_touchpoints)
FROM m_tmp.redistributed_customer_reactivation_cost);',
0.00001);
11
Data consistency
@martin_loetzsch
Makes changing things easy

12. Contribution margin 3a
SELECT order_item_id,
((((((COALESCE(item_net_price, 0)::REAL
+ COALESCE(net_shipping_revenue, 0)::REAL)
- ((COALESCE(item_net_purchase_price, 0)::REAL
+ COALESCE(alcohol_tax, 0)::REAL)
+ COALESCE(import_tax, 0)::REAL))
- (COALESCE(net_fulfillment_costs, 0)::REAL
+ COALESCE(net_payment_costs, 0)::REAL))
- COALESCE(net_return_costs, 0)::REAL)
- ((COALESCE(item_net_price, 0)::REAL
+ COALESCE(net_shipping_revenue, 0)::REAL)
- ((((COALESCE(item_net_price, 0)::REAL
+ COALESCE(item_tax_amount, 0)::REAL)
+ COALESCE(gross_shipping_revenue, 0)::REAL)
- COALESCE(voucher_gross_amount, 0)::REAL)
* (1 - ((COALESCE(item_tax_amount, 0)::REAL
+ (COALESCE(gross_shipping_revenue, 0)::REAL
- COALESCE(net_shipping_revenue, 0)::REAL))
/ NULLIF(((COALESCE(item_net_price, 0)::REAL
+ COALESCE(item_tax_amount, 0)::REAL)
+ COALESCE(gross_shipping_revenue,
0)::REAL), 0))))))
- COALESCE(goodie_cost_per_item, 0)::REAL) :: DOUBLE PRECISION
AS "Contribution margin 3a"
FROM dim.sales_fact;
Use schemas between reporting and database
Mondrian
LookerML
your own
Or: Pre-compute metrics in database
12
Semantic consistency
@martin_loetzsch
Changing the meaning of metrics across all dashboards needs to be easy

13. Focus on the complexity of data
rather than the complexity of technology
@martin_loetzsch
13

14. 14
We are open sourcing our BI infrastructure
@martin_loetzsch
ETL part released end of 2017

15. @martin_loetzsch
15
Meet us here at DN

16. 16
Refer us a data person, earn 200€
@martin_loetzsch
Also analysts, BI managers

17. Thank you
@martin_loetzsch
17