How to build a data warehouse - code.talks 2014
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The document discusses how to build a data warehouse for a startup company. It recommends integrating data from different sources like application databases, Excel files, and APIs into a central data warehouse. Dimensional modeling techniques are used to structure the data warehouse into fact and dimension tables for efficient querying and reporting. Several example schemas for real-life data warehouses are also presented.
![MDX = query language for multidimensional data
‣ Developed by Microsoft as part of Analysis Services
• http://en.wikipedia.org/wiki/MultiDimensional_eXpressions
‣
Each KPI is always computed in the same way
!
!
‣
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SELECT !
TopCount([Product].[Product].Members, 2,!
[Measures].[Revenue])!
ON COLUMNS,!
[Measures].[Revenue]!
ON ROWS!
FROM [Pet sales]!
WHERE [Date].[Month].[Oct]
SELECT [Date].[Month].Members!
ON COLUMNS,!
CrossJoin({[Measures].[Sold items],!
[Measures].[# Orders], !
[Measures].[Revenue]},!
Descendants([Product].[All products]))!
ON ROWS!
FROM [Pet sales]
order_item
item_id
order_id
has_voucher
price
day_fk
product_fk
day
day_id
day_name
month_id
month_name
product
product_id
product_name](https://image.slidesharecdn.com/2014-10-10code-141010135653-conversion-gate01/85/How-to-build-a-data-warehouse-code-talks-2014-13-320.jpg)
![Mondrian schema II
‣ Measures as defined as aggregates on columns
Each KPI is always computed in the same way
!
!
!
!
‣ Mondrian = SQL query generator
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SELECT [Date].[Month].Members!
ON COLUMNS,!
[Measures].[Avg cart value]!
ON ROWS!
FROM [Pet sales]
SELECT!
"day"."month_id" AS "c0",!
count(DISTINCT "order_item"."order_id") AS "m0",!
sum("order_item"."price") AS "m1"!
FROM!
"dim"."day" AS "day",!
"dim"."order_item" AS "order_item"!
WHERE!
"order_item"."day_fk" = "day"."day_id"!
GROUP BY!
"day"."month_id"
order_item
item_id
order_id
has_voucher
price
day_fk
product_fk
day
day_id
day_name
month_id
month_name
product
product_id
<Cube name="Pet sales" defaultMeasure="# Orders”>! product_name
..!
<Measure name="# Orders" column="order_id" datatype="Integer" aggregator="distinct-count" formatString="Standard"/>!
!
<Measure name="Revenue" column="price" datatype="Integer" aggregator="sum" formatString="Currency"/>!
!
<Measure name="Sold items" column="item_id" datatype="Integer" aggregator="count" formatString="Standard"/>!
!
<CalculatedMember name="Avg cart value" dimension="Measures">!
<Formula>[Measures].[Revenue] / [Measures].[# Orders]</Formula>!
</CalculatedMember>!
</Cube>!
!
➞ ➞](https://image.slidesharecdn.com/2014-10-10code-141010135653-conversion-gate01/85/How-to-build-a-data-warehouse-code-talks-2014-16-320.jpg)
![XMLA = XML for Analysis = MDX via SOAP
‣ Industry standard originally proposed by Microsoft
• http://en.wikipedia.org/wiki/XML_for_Analysis
• Soap protocol to discover and query OLAP cubes
• Mondrian has an XMLA server
‣ Request
‣ Response
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<?xml version="1.0" encoding="UTF-8"?>!
<SOAP-ENV:Envelope xmlns:SOAP-ENV=“..”>!
<SOAP-ENV:Header/>!
<SOAP-ENV:Body>!
<Execute xmlns="urn:schemas-microsoft-com:xml-analysis">!
<Command>!
<Statement>!
<![CDATA[!
SELECT [Date].[Month].Members!
ON COLUMNS,!
[Measures].[Avg cart value]!
ON ROWS!
FROM [Pet sales]!
]]>!
</Statement>!
</Command>!
<Properties>!
<PropertyList>!
<Catalog>dwh</Catalog>!
<DataSourceInfo>Monsai</DataSourceInfo>!
<Format>Multidimensional</Format>!
<?xml version="1.0" encoding="UTF-8"?>!
<SOAP-ENV:Envelope xmlns:SOAP-ENV="..">!
<SOAP-ENV:Header ../>!
<SOAP-ENV:Body>!
<cxmla:ExecuteResponse xmlns:cxmla="urn:schemas-microsoft-<cxmla:return>!
<root>!
<OlapInfo ../>!
<Axes>!
<Axis name=“Axis0" ../>!
<Axis name="Axis1">!
<Tuples>!
<Tuple>!
<Member Hierarchy=“Measures" ..>!
</Tuple>!
</Tuples>!
</Axis>!
<Axis name=“SlicerAxis" ../>!
</Axes>!
<CellData>!
<Cell CellOrdinal="0">!
<Value xsi:type="xsd:double">26.666666666666668</<FmtValue>26,67 €</FmtValue>!
<FormatString>Standard</FormatString>!
</Cell>!
<Cell CellOrdinal="1">!
<Value xsi:type="xsd:double">40</Value>!
<FmtValue>40,00 €</FmtValue>!
<FormatString>Standard</FormatString>!
</Cell>!](https://image.slidesharecdn.com/2014-10-10code-141010135653-conversion-gate01/85/How-to-build-a-data-warehouse-code-talks-2014-21-320.jpg)
How to build a data warehouse - code.talks 2014
- 1. Martin Loetzsch How to Build a Data Warehouse? Project A Ventures, Berlin ! http://project-a.com http://twitter.com/martin-loetzsch
- 2. The “typical startup” ‣ Has data in • application database • Excel & csv files • external tools ‣ Excel based reporting chains • manual sql queries, CSVs • copy & paste from external data sources • difficult to debug and test • sometimes cranky ! ‣ Everybody pulls their own numbers. # Orders? ! ! ! ! ! ! ! ‣ Does not have “big data” ‣ Will not have “big data” in the relevant future 2 / 25 -- count rows! SELECT count(*) FROM orders;! ! -- count everything except test orders! SELECT count(*) FROM orders! WHERE is_test IS NULL;! ! -- count everything that was once paid! SELECT count(*) FROM orders! JOIN order_history ON order_fk = order_id! WHERE status_id = 17; If Excel works for your company, stick to it
- 3. Data driven growth requires integrated data ‣ Integrated data = Data Warehouse csv files Integrate data! ! ! ! ! ‣ Data in the Data Warehouse is • the single point of truth • cleaned up & validated • easy to access • embedded in the organisation ‣ Connect data from different domains 3 / 25 application databases json files apis reporting marketing crm search pricing … DWH orders users products stocks prices emails clicks …
- 4. ‣ 1. Use a BI Solutions by one of the big vendors ! ! • classic agency business • takes forever in startup time • usually too expensive ! ‣ 2. Use a cloud based DWH solution ! ! • covers only 80% of your business questions • usually not possible to extend ‣ 3. Build your own, it’s easy! ! ! • with technology that existed in the 1990s • simple ETL scripts running inside Postgresql • open source Pentaho Mondrian as query processor • own lightweight reporting frontend • integrated in own shop system ‣ Keep it simple & pragmatic ‣ Don’t use big data technologies if you don’t have big data How to build a Data Warehouse? Invest in own BI infrastructure 4 / 25
- 5. Basis of any Data Warehouse: fact tables ‣ Works with Excel, SQL frontends, Elasticsearch, Mondrian & other BI front ends ! ! ! ‣ KPIs: aggregations on single columns ‣ All time orders? ! ‣ Revenue October 1st? ! ‣ Sales by product? ! ! ‣ Allowed query operations • Aggregations (count, distinct-count, sum, avg) • Filtering • Grouping 5 / 25 item id order id has voucher price day product 1 1 20 09-30 Cat 2 1 10 09-30 Dog 3 2 2 20 09-30 Cat 4 3 30 09-30 Cow 5 4 4 10 10-01 Dog 6 4 4 30 10-01 Cow # Sold items: count(item_id) # Orders: distinct-count(order_id) # Orders with vouchers: distinct-count(has_voucher) Revenue: sum(price) Avg product price: avg(price) SELECT count(distinct order_id) FROM order_item; SELECT sum(price) FROM order_item WHERE day = ’10-01'; SELECT count(item_id) FROM order_item GROUP BY product;
- 6. Dimensional modelling ‣ Move redundant categorial data to “dimension” tables order_item item_id order_id has_voucher price day_fk product_fk day day_id day_name month_id month_name product product_id product_name Key challenge: finding good keys 6 / 25 item id order id has voucher price day fk product fk 1 1 20 930 1 2 1 10 930 2 3 2 2 20 930 1 4 3 30 930 3 5 4 4 10 1001 2 6 4 4 30 1001 3 day id day name month id month name 930 09-30 9 Sep 1001 10-01 10 Oct product id product name 1 Cat 2 Dog 3 Cow
- 7. Real life schemas I ‣ https://www.contorion.de/ early stage project order_item_status order_item_status_id order_item_status_sort_id order_item_status_name order_item_status_mapping order_item_status_fk order_item_status_partition_fk sales_event sales_event_id order_item_fk order_item_current_status_fk order_item_status_partition_fk order_timestamp event_timestamp hours_since_order hours_since_last_event hours_to_next_event estimated_net_revenue order_item_status_partition order_item_status_partition_id order_item_status_perspective_id order_item_status_perspective_name order_item_status_process_id order_item_status_process_name order_item_status_group_id order_item_status_group_name order_item order_item_id order_fk merchant_fk product_fk category_fk category_tree_fk order_process_fk order_item_status_fk processed_order_item_id net_shipping_revenue tax_amount_shipping gross_voucher_value net_voucher_value gross_revenue_before_voucher net_item_value gross_item_value tax_amount_before_voucher tax_amount_voucher gross_shipping_revenue gross_shipping_revenue_before_voucher net_purchase_cost gross_purchase_cost net_revenue_returned net_revenue_canceled net_payment_cost net_return_cost_and_loss_and_fraud net_shipping_and_fulfillment_cost net_marketing_expenses address zip_code first_name last_name city country_fk gender account_disabled company phone cell_phone country country_id country_name order order_id increment_id order_type_fk is_first_order_id is_follow_up_order_id is_second_order_id is_second_or_subsequent_order_id customer_fk returning_customer_fk order_rank_fk items_per_order_fk payment_method_fk payment_provider_fk zip_code_fk order_rank_1st_fk order_rank order_rank_id order_rank_name order_rank_group_id order_rank_group_name customer customer_id increment_id customer_name email number_of_orders first_order_date last_order_date avg_days_between_orders number_of_orders_with_vouchers phone company gender_fk customer_type_fk customer_group_fk customer_industry_fk order_type order_type_id order_type_name order_date day_fk hour_of_day_fk day_of_week_fk order_fk order_date_perspective_fk customer_date day_fk customer_fk customer_date_perspective_fk order_date_perspective order_date_perspective_id order_date_perspective_name hour_of_day hour_of_day_id hour_of_day_name day_of_week day_of_week_id day day_of_week_name day_id day_reversed_id day_name year_id year_reversed_id iso_year_id iso_year_reversed_id quarter_id quarter_name month_id month_reversed_id month_name week_id week_reversed_id week_name customer_date_perspective customer_date_perspective_id customer_date_perspective_name gender gender_id gender_name customer_type customer_type_id customer_type_name customer_group customer_group_id customer_group_name sales_event_duration_perspective sales_event_duration_perspective_id sales_event_duration_perspective_name sales_event_duration sales_event_fk duration_fk sales_event_duration_perspective_fk duration duration_id days days_name weeks weeks_name months months_name quarters quarters_name years years_name sales_event_date sales_event_fk day_fk sales_event_date_perspective_fk sales_event_date_perspective sales_event_date_perspective_id sales_event_date_perspective_name product product_id sku ean sales_sku merchant_sku product_name category category_id category_parent_fk category_name newsletter_event newsletter_event_id day_fk campaign_fk customer_increment_fk sent bounce bounce_block bounce_soft bounce_hard bounce_reason_fk open first_open click first_click url_fk complaint subsequent_order_fk first_order gross_revenue_before_voucher gross_voucher_value net_voucher_value tax_amount_before_voucher net_purchase_cost url cost_per_campaign_and_day day_fk campaign_fk number_of_clicks imported_cost_mci imported_cost_api cost_of_clicks_directly_assigned cost_of_clicks_campaigns_without_clicks cost_of_clicks_unknown_campaign campaign campaign_id campaign_name subject sent_hour sent_date campaign campaign_id campaign_name level_3_id level_3_name level_2_id level_2_name channel_fk channel channel_id channel_name Star schema, galaxy schema, nth normal form? Doesn’t matter, do what’s fastest. 7 / 25
- 8. campaign_click_performance campaign_click_fk performance_attribution_model_fk attribution_path_segment_fk number_of_signups number_of_activations number_of_transactions gross_revenue campaign_click campaign_click_id visitor_id day_fk campaign_fk user_fk path_segment_fk path_position_fk reverse_path_position_fk step_fk next_step_fk step_reverse_fk next_step_reverse_fk number_of_clicks number_of_new_visitors number_of_daily_visitors number_of_monthly_visitors duration_fk time_to_end marketing_cost path_segment path_segment_id path_segment_name performance_attribution_model performance_attribution_model_id performance_attribution_model_name user_date day_fk user_fk user_time_perspective_fk cost_per_campaign_and_day day_fk campaign_fk number_of_clicks imported_cost_mci imported_cost_api cost_of_clicks_directly_assigned cost_of_clicks_campaigns_without_clicks cost_of_clicks_unknown_campaign campaign campaign_id campaign_name level_3_id level_3_name level_2_id level_2_name channel_fk corridor_fk conversion_path_transition conversion_path_transition_id conversion_path_transition_name channel_with_position_id channel_with_position_name reverse_path_position reverse_path_position_id reverse_path_position_name path_position path_position_id path_position_name user user_id number_of_users customer_id number_of_customers repeat_customer_id gender_fk age_fk user_city_fk user_state_fk user_country_fk most_freq_corridor_fk total_transaction_range_fk referral_source_fk transaction_frequency_fk has_sent_cash_id has_sent_airtime_id has_sent_cash_and_airtime_id sent_amount_money_transfer number_of_transactions number_of_transactions_with_voucher fees fx_gain sent_amount_airtime voucher_cost_money_transfer voucher_cost_airtime days_between_signup_and_first_transaction days_between_signup_and_second_transaction days_between_first_and_second_transaction days_between_second_and_third_transaction average_days_between_transactions days_since_last_transaction days_since_last_login day day_id day_name year_id iso_year_id quarter_id quarter_name month_id month_name week_id week_name day_of_week_id day_of_week_name day_of_month_id day_of_month_reversed_id number_of_days_in_month duration duration_id days days_name weeks weeks_name months months_name quarters quarters_name years years_name conversions conversions_name channel channel_id channel_name corridor corridor_id corridor_name sender_country_fk sender_country_name receiver_country_fk receiver_country_name campaign_cohort performance_attribution_model_fk user_fk campaign_fk channel_fk day_fk duration_fk number_of_transactions gross_revenue age age_id age_name age_group_id age_group_name user_city user_city_id user_city_name user_state_id user_state_name user_country_fk user_country_name gender gender_id gender_name referral_source referral_source_id referral_source_name total_transaction_range total_transaction_range_id total_transaction_range_name country country_id country_code country_name transaction_frequency transaction_frequency_id transaction_frequency_name transaction transaction_id number_of_transactions number_of_first_transactions number_of_second_transactions number_of_third_transactions number_of_subsequent_transactions number_of_transactions_with_voucher number_of_first_transactions_with_voucher number_of_money_transfer_transactions number_of_airtime_transactions number_of_on_hold_transactions number_of_pending_transactions number_of_paid_transactions is_repeat_customer_id transaction_status_fk cancellation_status_fk customer_fk user_fk sender_city_fk receiver_city_fk sender_currency_fk receiver_currency_fk correspondent_fk voucher_fk corridor_fk payment_method_fk receive_method_fk transaction_rank_fk bank_fk sent_amount_range_fk sent_amount_money_transfer sent_amount_airtime receive_amount_creation receive_amount_payout total_to_pay fx_gain fees voucher_cost_money_transfer voucher_cost_airtime fx_rate_gbp_to_sent_amount fx_rate_gbp_to_receive_amount_creation_date fx_rate_gbp_to_receive_amount_payout_date fx_rate_sent_to_receive bank bank_id bank_name transaction_rank transaction_rank_id transaction_rank_name transaction_rank_group_id transaction_rank_group_name cancellation_status cancellation_status_id cancellation_status_name correspondent correspondent_id correspondent_name transaction_city transaction_city_id transaction_city_name transaction_state_id transaction_state_name transaction_country_fk transaction_country_name transaction_country_code transaction_capital_latitude transaction_capital_longitude currency currency_id currency_code currency_name voucher voucher_id voucher_name voucher_type_id voucher_type_name voucher_percentage_id voucher_percentage_name voucher_receive_method_group_id voucher_receive_method_group_name voucher_start_date_fk voucher_end_date_fk voucher_duration_days_id voucher_duration_days_name voucher_duration_range_id voucher_duration_range_name payment_method payment_method_id payment_method_name payment_method_group_id payment_method_group_name Real life schemas II sent_amount_range sent_amount_range_id origin_currency_fk sent_amount_range_name range_lower_limit range_upper_limit receive_method receive_method_id receive_method_name receive_method_group_id receive_method_group_name receive_service_id transaction_status transaction_status_id transaction_status_name foreign_exchange_rate foreign_exchange_rate_id day_fk sender_currency_fk receiver_currency_fk foreign_exchange_rate foreign_exchange_rate_without_markup voucher_usage_fact day_fk voucher_fk voucher_duration_days_id voucher_duration_days_name voucher_duration_range_id voucher_duration_range_name voucher_start_date_fk voucher_end_date_fk voucher_is_money_transfer_id voucher_is_airtime_id voucher_is_valid_id voucher_is_used_id voucher_receive_method_id voucher_receive_method_name number_of_customers number_of_transactions number_of_first_transactions fees voucher_cost_money_transfer voucher_cost_airtime fx_gain sent_amount_money_transfer sent_amount_airtime transaction_event_date transaction_event_fk day_fk transaction_event_time_perspective_fk transaction_event transaction_event_id number_of_transaction_events transaction_fk previous_status_fk current_status_fk hours_since_transaction hours_since_last_event hours_to_next_event sent_amount_money_transfer sent_amount_airtime voucher_cost_money_transfer voucher_cost_airtime fx_gain fees transaction_event_time_perspective transaction_event_time_perspective_id transaction_event_time_perspective_name transaction_date day_fk transaction_fk transaction_time_perspective_fk transaction_duration duration_fk transaction_fk transaction_time_perspective_fk transaction_time_perspective transaction_time_perspective_id transaction_time_perspective_name transaction_event_duration transaction_event_fk duration_fk transaction_event_duration_perspective_fk 8 / 25 ‣ https://www.worldremit.com/ finished soon* project * A Data Warehouse is never finished
- 9. order_item order_item_id processed_order_item_id is_original_id is_print_id processed_product_id order_fk product_fk order_item_status_fk price_range_fk order_process_fk option_fk fulfillment_provider_fk refund_reason_fk gross_revenue_item net_item_price net_item_price_first_order vat_amount net_shipping_revenue net_shipping_revenue_first_order duties_amount gross_revenue_item_option net_option_price net_option_price_first_order net_payment_cost net_option_cost net_printing_cost net_voucher_amount_saatchi_share net_voucher_amount_artist_share net_voucher_amount_saatchi_share_first_order net_voucher_amount_artist_share_first_order artist_commission artist_royalties estimated_net_revenue_after_vouchers origin_country_iso2 origin_latitude origin_longitude destination_latitude destination_longitude artwork artwork_id artist_fk showdown_fk artwork_category_fk artwork_subject_fk artwork_is_curated_fk artwork_is_user_collection_fk artwork_is_admin_collection_fk artwork_related_fk artwork_sale_category_fk artwork_for_sale_as_print_fk artwork_for_sale_as_original_fk date_uploaded_fk artwork_in_showdown_fk artwork_in_weekly_roundup_fk artwork_is_visible artwork_is_in_curated artwork_is_in_user_collection artwork_is_in_admin_collection user_collections_per_artwork admin_collections_per_artwork url title styles artist_name artist_first_name artist_last_name option option_id option_name artwork_for_sale_as_original artwork_for_sale_as_original_id artwork_for_sale_as_original_name Real life schemas III artwork_category artwork_category_id artwork_category_name artwork_in_showdown artwork_in_showdown_id artwork_in_showdown_name artwork_for_sale_as_print artwork_for_sale_as_print_id artwork_for_sale_as_print_name artwork_in_weekly_roundup artwork_in_weekly_roundup_id artwork_in_weekly_roundup_name artwork_is_admin_collection artwork_is_admin_collection_id artwork_is_admin_collection_name artwork_is_curated artwork_is_curated_id artwork_is_curated_name artwork_is_user_collection artwork_is_user_collection_id artwork_is_artwork_related user_collection_name artwork_related_id artwork_related_name artwork_sale_category artwork_sale_category_id artwork_sale_category_name artwork_subject artwork_subject_id artwork_subject_name round round_id showdown_id showdown_round showdown_title_sort_id showdown_title user user_id user_type_fk user_status_fk user_city_fk artist_with_artwork_for_sale_id artist_with_artwork_uploaded_id user_name user_first_name user_last_name email number_of_weekly_roundup number_of_showdown number_of_artwork_comments number_of_collection_comments number_of_artworks_in_user_collections number_of_user_likes number_of_collection_favourites number_of_user_logins number_of_messages_sent number_of_uploads hours_to_first_upload number_of_bought_items number_of_originals_bought number_of_prints_bought number_of_orders_made net_item_price_bought net_item_revenue_bought gross_revenue_after_vouchers_bought net_revenue_after_vouchers_bought net_voucher_cost_bought number_of_sold_items number_of_originals_sold number_of_prints_sold number_of_orders_sold net_item_price_sold net_item_revenue_sold net_voucher_cost_sold product product_id sku artwork_fk product_category_fk substrate_fk product_category product_category_id product_category_name edition_type substrate substrate_id substrate_name collection_artwork_order_item collection_artwork_order_item_id collection_fk artwork_fk order_item_fk collection collection_id collection_name user_fk collection_type_fk collection_detailed_type_fk date_created_fk date_initiated_fk artwork_style_mapping artwork_fk artwork_style artwork_style_fk artwork_style_id artwork_style_name artwork_in_collection artwork_fk collection_fk sales_event_duration_perspective sales_event_duration_perspective_id sales_event_duration_perspective_name sales_time_perspective sales_time_perspective_id sales_time_perspective_name collection_artwork_order_item_date collection_artwork_order_item_fk day_fk collection_artwork_order_item_time_perspective_fk day day_id day_name year_id iso_year_id quarter_id quarter_name month_id month_name week_id week_name day_of_the_month number_of_days_in_month iso_date collection_artwork_order_item_time_perspective collection_artwork_order_item_time_perspective_id collection_artwork_order_item_time_perspective_name collection_detailed_type collection_detailed_type_id collection_detailed_type_name collection_type collection_type_id collection_type_name campaign_click_date campaign_click_fk day_fk online_marketing_time_perspective_fk campaign_click campaign_click_id campaign_fk search_phrase_fk referrer_fk user_fk number_of_clicks number_of_daily_visits number_of_monthly_visits number_of_new_visits number_of_daily_visitors number_of_monthly_visitors subsequent_registration_fk subsequent_confirmation_fk subsequent_first_order_fk subsequent_order_fk direct_cost cost_of_campaigns_without_clicks unmatched_cost visit_duration online_marketing_time_perspective online_marketing_time_perspective_id online_marketing_time_perspective_name email_event_date email_event_fk day_fk email_time_perspective_fk email_event email_event_id email_list_fk email_campaign_fk email_recipient_fk subscribe unsubscribe email_unsubscribe_reason_fk sent bounce_soft bounce_hard open first_open click first_click subsequent_order subsequent_first_order items net_item_price net_option_price net_shipping_revenue net_voucher_amount_saatchi_share net_voucher_amount_artist_share email_time_perspective email_time_perspective_id email_time_perspective_name transactional_mail number_of_mails_sent transactional_mail_type_fk day_fk transactional_mail_type transactional_mail_type_id transactional_mail_type_name sales_event_date sales_event_fk day_fk sales_event_date_perspective_fk sales_event sales_event_id order_item_fk order_item_current_status_fk order_item_status_partition_fk order_timestamp event_timestamp hours_since_order hours_since_last_event hours_to_next_event effected_net_revenue_after_vouchers estimated_net_revenue_after_vouchers sales_event_date_perspective sales_event_date_perspective_id sales_event_date_perspective_name order_date day_fk order_fk order_date_perspective_fk order order_id order_increment_id processed_order_id is_first_order_id is_second_order_id is_second_or_subsequent_order_id order_with_voucher_id user_fk returning_buyer_fk hour_of_day_fk voucher_fk payment_method_fk payment_provider_fk shipping_city_fk order_source_fk order_date_perspective order_date_perspective_id order_date_perspective_name sales_event_duration sales_event_fk duration_fk sales_event_duration_perspective_fk duration duration_id days days_name weeks weeks_name months months_name quarters quarters_name five_day_period five_day_period_name order_duration order_fk duration_fk sales_time_perspective_fk fulfillment_provider fulfillment_provider_id fulfillment_provider_name order_item_status order_item_status_id order_item_status_sort_id order_item_status_name order_process order_process_id order_process_name checkout_type_id checkout_type fulfillment_type_id fulfillment_type price_range price_range_id price_range_name refund_reason refund_reason_id refund_reason_name refund_code_id hour_of_day hour_of_day_id hour_of_day_name order_source order_source_id order_source_name payment_method payment_method_id payment_method_name payment_provider payment_provider_id payment_provider_name shipping_city shipping_city_id shipping_city_name shipping_country_id shipping_country_name voucher voucher_id voucher_name order_item_status_partition order_item_status_partition_id order_item_status_perspective_id order_item_status_perspective_name order_item_status_group_id order_item_status_group_name order_item_refunds order_item_refunds_id order_item_fk refund_code_id refund_code refund_desc refund_amount refund_date refund_comment order_item_status_mapping order_item_status_fk order_item_status_partition_fk email_campaign email_campaign_id email_campaign_name email_list_fk email_unsubscribe_reason email_unsubscribe_reason_id email_unsubscribe_reason_name email_recipient email_recipient_id email email_recipient_location_fk email_list email_list_id email_list_name email_recipient_location email_recipient_location_id country_id country_name region_id region_name latitude longitude user_city user_city_id user_city user_country_id user_country user_status user_status_id user_status_name user_type user_type_id user_type_name user_event_date_registration user_event_fk day_fk user_event_time_perspective_fk user_event user_event_id user_fk user_type_fk user_event_date registration_date weekly_roundup showdown artwork_comment collection_comment artwork user_likes collection_favourite user_login message_sent artwork_upload artwork_for_sale_as_print artwork_for_sale_as_original artwork_for_sale_as_both_print_and_original artwork_for_sale_as_either_print_or_original signup verified_signup user_order time_since_signup time_since_last_order user_event_date_event user_event_fk day_fk user_event_time_perspective_fk referrer referrer_id referrer_name referrer_type_name campaign campaign_id campaign_name campaign_code channel_id channel_name is_brand_id is_brand_name partner_or_adwords_account_id partner_or_adwords_account_name publication_or_adwords_campaign_id publication_or_adwords_campaign_name wmc_or_adwords_adgroup_id wmc_or_adwords_adgroup_name search_phrase search_phrase_id search_phrase_name search_phrase_type_name user_date user_fk day_fk sales_time_perspective_fk campaign_click_position campaign_click_fk conversion_type_fk campaign_click_performance campaign_click_fk performance_attribution_model_fk conversion_type_fk number_of_registrations number_of_leads number_of_orders number_of_received_orders number_of_first_orders number_of_orders_with_voucher net_order_revenue performance_attribution_model performance_attribution_model_id performance_attribution_model_name 9 / 25 ‣ http://www.saatchiart.com/ exit August 2014
- 10. Data integration ‣ Visuals ETL tools • many data source connectors • hard to debug • slow to change Optimize for change speed! ‣ Start with simple sql queries & batch scripts cat create-tables.sql | psql dwh! ! cat load-order.sql ! | mysql --skip-column-names source_db ! | psql dwh --command="COPY tmp.order FROM STDIN ! ! ! ! ! ‣ Later build something more robust 10 / 25 WITH NULL AS 'NULL'"! ! cat /data/payment.csv ! | python payment_filter.py! | psql dwh --command="COPY tmp.payment FROM STDIN” ! ! cat transform-order.sql | psql dwh! !
- 11. Data integration in Yves & Zed 11 / 25 ‣ Jobs = processing steps with dependencies • parallel execution with cost based scheduler • robust, transparent, no black boxes ‣ Parallel jobs & incremental processing ‣ Extensive visualisations & monitoring tools
- 12. Plain text files ‣ Very git-friendly 12 / 25 <?xml version="1.0" encoding="UTF-8"?>! <process xmlns="http://project-a.com/dwh-process"! id=“operational-data" ..>! ! <initial-job id="initialize-schemas">! <description>Recreates schemas and writes configs</description>! <commands>! ..! </commands>! </initial-job>! ! <!-- Orders -->! <job id="load-order">! <description>Loads orders into tmp.order</description>! <commands>! <execute-sql-file file-name="orders/create-order-tmp-table.sql" echo-queries="true"/>! <load-from-mysql file-name="orders/load-order.sql"! target-table="tmp.order" database="app"! timezone="UTC"/>! <execute-sql>SELECT tmp.index_tmp_order();</execute-sql>! </commands>! </job>! ! <job id="cleanse-order">! <description>Deletes test orders and other invalid orders</description>! <dependencies>! <dependency job="cleanse-member"/>! <dependency job="load-order-item"/>! <dependency job="load-product"/>!
- 13. MDX = query language for multidimensional data ‣ Developed by Microsoft as part of Analysis Services • http://en.wikipedia.org/wiki/MultiDimensional_eXpressions ‣ Each KPI is always computed in the same way ! ! ‣ 13 / 25 SELECT ! TopCount([Product].[Product].Members, 2,! [Measures].[Revenue])! ON COLUMNS,! [Measures].[Revenue]! ON ROWS! FROM [Pet sales]! WHERE [Date].[Month].[Oct] SELECT [Date].[Month].Members! ON COLUMNS,! CrossJoin({[Measures].[Sold items],! [Measures].[# Orders], ! [Measures].[Revenue]},! Descendants([Product].[All products]))! ON ROWS! FROM [Pet sales] order_item item_id order_id has_voucher price day_fk product_fk day day_id day_name month_id month_name product product_id product_name
- 14. Mondrian = engine for executing MDX ‣ Open source analytics processor • http://mondrian.pentaho.com • http://en.wikipedia.org/wiki/Mondrian_OLAP_server • In Java • Eclipse Public License • Active community • https://github.com/pentaho/mondrian/ ! ‣ Part of Pentaho BI platform Open source business analytics William D. Back Nicholas Goodman Julian Hyde M A N N I N G 14 / 25 www.it-ebooks.info
- 15. Mondrian schema I ‣ The relation between fact tables and dimension tables is defined in a XML file 15 / 25 <Cube name="Pet sales" defaultMeasure="# Orders">! <Table schema="dim" name="order_item"/>! ! <Dimension name="Date" type="TimeDimension" foreignKey="day_fk">! <Hierarchy allMemberName="All dates" hasAll="true" primaryKey="day_id">! <Table schema="dim" name="day"/>! <Level name="Month" column="month_id" nameColumn="month_name"! type="Integer" levelType="TimeMonths" uniqueMembers="true"/>! <Level name="Day" column="day_id" nameColumn="day_name"! type="Integer" levelType="TimeDays" uniqueMembers="true"/>! </Hierarchy>! </Dimension>! ! <Dimension name="Product" foreignKey="product_fk">! <Hierarchy hasAll="true" allMemberName="All products" primaryKey="product_id">! <Table schema="dim" name="product"/>! <Level name="Product" column="product_id" nameColumn="product_name"! type="Integer" uniqueMembers="true"/>! </Hierarchy>! </Dimension>! ! ..! </Cube> order_item item_id order_id has_voucher price day_fk product_fk day day_id day_name month_id month_name product product_id product_name
- 16. Mondrian schema II ‣ Measures as defined as aggregates on columns Each KPI is always computed in the same way ! ! ! ! ‣ Mondrian = SQL query generator 16 / 25 SELECT [Date].[Month].Members! ON COLUMNS,! [Measures].[Avg cart value]! ON ROWS! FROM [Pet sales] SELECT! "day"."month_id" AS "c0",! count(DISTINCT "order_item"."order_id") AS "m0",! sum("order_item"."price") AS "m1"! FROM! "dim"."day" AS "day",! "dim"."order_item" AS "order_item"! WHERE! "order_item"."day_fk" = "day"."day_id"! GROUP BY! "day"."month_id" order_item item_id order_id has_voucher price day_fk product_fk day day_id day_name month_id month_name product product_id <Cube name="Pet sales" defaultMeasure="# Orders”>! product_name ..! <Measure name="# Orders" column="order_id" datatype="Integer" aggregator="distinct-count" formatString="Standard"/>! ! <Measure name="Revenue" column="price" datatype="Integer" aggregator="sum" formatString="Currency"/>! ! <Measure name="Sold items" column="item_id" datatype="Integer" aggregator="count" formatString="Standard"/>! ! <CalculatedMember name="Avg cart value" dimension="Measures">! <Formula>[Measures].[Revenue] / [Measures].[# Orders]</Formula>! </CalculatedMember>! </Cube>! ! ➞ ➞
- 17. Mondrian schema III ‣ Everything about KPIs & dimensions (business) and tables & columns (IT) in one file • consistent & explicit semantics • transparency is easy Always draw your Mondrian schema! 17 / 25
- 18. Ad-hoc queries with Saiku Analytics ‣ Drag & drop reporting tool on top of Mondrian • Open source (Apache 2.0) • Talks to Mondrian via MDX • http://meteorite.bi/saiku Try it out immediately, it’s amazing: http://demo.analytical-labs.com/ 18 / 25
- 19. Reports in Yves & Zed I ‣ Own lightweight reporting frontend • bootstrap/ Google charts • lacks many features • features are easy to implement Numbers are random! 19 / 25
- 20. Reports in Yves & Zed II ‣ Dashboard-like interactive reports • maintained by developers • each table / chart is an MDX query Numbers are random! 20 / 25
- 21. XMLA = XML for Analysis = MDX via SOAP ‣ Industry standard originally proposed by Microsoft • http://en.wikipedia.org/wiki/XML_for_Analysis • Soap protocol to discover and query OLAP cubes • Mondrian has an XMLA server ‣ Request ‣ Response 21 / 25 <?xml version="1.0" encoding="UTF-8"?>! <SOAP-ENV:Envelope xmlns:SOAP-ENV=“..”>! <SOAP-ENV:Header/>! <SOAP-ENV:Body>! <Execute xmlns="urn:schemas-microsoft-com:xml-analysis">! <Command>! <Statement>! <![CDATA[! SELECT [Date].[Month].Members! ON COLUMNS,! [Measures].[Avg cart value]! ON ROWS! FROM [Pet sales]! ]]>! </Statement>! </Command>! <Properties>! <PropertyList>! <Catalog>dwh</Catalog>! <DataSourceInfo>Monsai</DataSourceInfo>! <Format>Multidimensional</Format>! <?xml version="1.0" encoding="UTF-8"?>! <SOAP-ENV:Envelope xmlns:SOAP-ENV="..">! <SOAP-ENV:Header ../>! <SOAP-ENV:Body>! <cxmla:ExecuteResponse xmlns:cxmla="urn:schemas-microsoft-<cxmla:return>! <root>! <OlapInfo ../>! <Axes>! <Axis name=“Axis0" ../>! <Axis name="Axis1">! <Tuples>! <Tuple>! <Member Hierarchy=“Measures" ..>! </Tuple>! </Tuples>! </Axis>! <Axis name=“SlicerAxis" ../>! </Axes>! <CellData>! <Cell CellOrdinal="0">! <Value xsi:type="xsd:double">26.666666666666668</<FmtValue>26,67 €</FmtValue>! <FormatString>Standard</FormatString>! </Cell>! <Cell CellOrdinal="1">! <Value xsi:type="xsd:double">40</Value>! <FmtValue>40,00 €</FmtValue>! <FormatString>Standard</FormatString>! </Cell>!
- 22. Data Warehouse in Yves & Zed ! ! ! ! ! ! csv files ! ! ! ! ! data integration monsai reporting MDX results database mapping ! ‣ monsai = Mondrian XMLA Server + Saiku in a single war file, https://github.com/project-a/monsai 22 / 25 application databases json files apis SQL SQL DB results XMLA / MDX Mondrian XMLA response Mondrian schema
- 23. What kind of people do you need to hire for this? ‣ The “typical BI expert”: • studied something related to business and learnt VBA programming through Excel • relies on others to set up databases and tools ‣ Your ideal candidate • has studied computer science • masters the basic tools of software development and computer science • likes to learn new technologies • understands how databases work ‣ Good profile example: http://www.project-a.com/en/careers/jobs/?yid=332 Job opportunity Data Engineer / Data Scientist (m/f) at Projec... https://karriere.project-a.com/eng?yid=For our "A-Team" we are looking to fill the following position as soon as possible Data Engineer / Data Scientist (m/f) Your tasks: You will help our business intelligence team to build data driven applications for our ventures: data warehouses, recommendation engines and CRM systems (developed in-house, based on open-source technologies) You will integrate, transform and index data from various data sources, develop meaningful data representations and visualisations, and provide aggregated data for third-party systems You will advance our software architecture and tool set to growing challenges and data amounts (performance, scaling, data quality) You will work in an agile software development process in close collaboration with a product management team Your profile: You have a Master's degree in computer science or a comparable degree You have a genuine interest in data and algorithms and you are excited about solving difficult problems and strive for efficient and robust solutions You master at least these basic tools of computer science: object oriented programming in multiple languages, HTTP and current web technologies, the unix command line and basic server administration, version control systems, a basic understanding of the interplay between software and memory, hard discs and the CPU You have profound knowledge about the inner workings of database systems You are eager to delve into new technologies and programming languages (our current stack: Mac or Linux, PostgreSQL, Mondrian & MDX, PHP, Java, Python, Solr, ElasticSearch, R) You have a basic understanding of mathematics and machine learning Your chance: 23 You will join a highly professional and motivated team You will have the unique opportunity to witness the launch of a newly established company and you can contribute your own ideas to its development Search for computer scientists, not business intelligence experts / 25
- 24. Use a standard software engineering process! ‣ Product managers: what? • Collection of business requirements • KPI & report definitions • QA & analysis ! Any kind of Scrum / Kanban works, do it ‣ Developers: how? • Implementation, performance & stability • Schema & process design • Consistency checks Avg net revenue per buying member % Contribution margin 1 24 / 25 Net revenue Net voucher cost Avg net voucher cost per order Contribution margin 3a Tax shipping amount Tax amount Gross revenue Avg gross item price Gross price to gross retail price ratio Price to retail price ratio Avg gross order value % Gross voucher cost Gross invoiced amount Net invoiced amount Gross retail price Net price to net purchase price ratio Net price to net retail price ratio % Net discount Avg gross invoiced amount HGB net revenue margin Avg gross voucher cost per buying member Net item revenue Tax item amount Net purchase cost Net retail price Retail tax amount Gross voucher cost Net shipping revenue Gross shipping revenue
- 25. Thank you Data integration is easy if you keep things simple! http://www.project-a.com/ 25 / 25