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Neo4j Northwind Traders Database Schema

Graph Databases: Loading Data with Neo4j

Graph databases are becoming more popular as a way of storing and analysing large connected datasets.

Neo4j is a popular Graph DBMS because of its powerful querying language: Cypher and its growing community and excellent supporting tools.

A new paradigm comes with a new set of challenges. In this case we are focused on the challenge of creating a data pipeline to load data into Neo4j, thinking about how we might design our schema and how we might query it.

Today, we’ll take you through an ETL of The Northwind Traders Sample Database and some of the things we can do with Cypher that makes it special and worth a look.

We’ll be using Kettle to orchestrate our ETL and making use of the Neo4j Output Plugin to help us interact with our Neo4j server. We’ll also load the same dataset to PosgreSQL, to see how the two technologies compare.


Environment Setup

The installation of Kettle, Neo4j Output Plugin and Neo4j server is outside the scope of this post but it is important to note that the connection is not stored in the transformations as they usually are. It’s stored in the metastore which is found in your home folder. For that reason, you’ll have to create a new connection before you can load data into Neo4j.

Create connection is available the Neo4j Menu of Kettle
Neo4j Connection dialog allows you to add a connection to your metastore

Once you can test your connection you are good to go, just make sure the connection name is local-hardcoded so that you don’t need to change any of the transformations provided at the end of the post.

To set up your PostgreSQL connection you’ll need to edit the conf/kettle.properties files with the correct details for your connection. This will change the connection for each of the transformations and the main job as we have parameterised the connection for your convenience. If you do not have a password you can remove everything after DATABASE_PASS from the file and it will work fine.

With that out of the way, let’s get started with Northwind.


Northwind Traders Database

The Northwind Traders database is a sample database that comes with Microsoft Access. The database contains sales data for a fictitious company called Northwind Traders.

As you can see it represents an OLTP focused on parts of orders.

We will be using a version of Northwind compiled by Neo4j as csv files, available on GitHub, which is more or less the same as the original and the image above.


Loading Data

The ETL we have created is relatively simple; there is a single job, RUN.kjb, that runs, in sequence, the transformations that load nodes and relationships into Neo4j from the csv files.

Similarly, there is a single job for loading into Postgres; one important difference is that for Postgres we must create our dimension and fact schemas beforehand using SQL create statements. In Neo4j this is not necessary because graph databases are schema-less.

We’ll be using the Neo4j Output step to create nodes and relationships and the Neo4j Cypher step for lookups.

The sequence of steps below shows a common pattern to ensure that poorly delimited and enclosed csv files produce the expected columns and rows:

Handling poorly delimited files in Kettle

Address Nodes

Addresses occur often within Northwind: suppliers, customers, shipment receivers and employees all have addresses. For that reason we have tried to create a generic way to link an address, an actor that allows for performant geographic queries.

Rather than have addresses and their components (country, region, city, postcode, street and building name) stored as a property for each customer, supplier, etc. each address is stored as its own node. Each address node is linked to a city node and each city to a region and so on; this creates a hierarchy, using composite keys to make sure the same region isn’t linked to two countries. The result of this is easier indexing which speeds up queries because searches on nodes are faster than searches on properties.

Producing nodes and relationships from a table of addresses in Kettle

After we have collected all the addresses from all the files, we remove duplicates and replace any null regions (some countries have only one region, which is null) with the country name. We create the region nodes and link them to their appropriate country node, do the same for cities -> regions and addresses -> city nodes. 

We now have a more efficient way to query locations and slice our data. 

Example address heirarchy in Neo4j Browser

Relational Addresses

In Postgres we need to do little to get addresses into our dimensions. If it wasn’t for other complications such as poorly delimited and enclosed csv files and replacing null regions it would be as simple as table input -> table output. This is because each address is stored in the same dimension as the other information for that actor, i.e. customer addresses are stored in the customer dimension.


Date Nodes and Date Dimension

Creating date nodes in Kettle

Creating date nodes is identical to any date dimension that you have created in the past except that you are creating nodes instead of rows. The result is a node with many properties that allow you to query in a variety of ways without having to do on the fly date operations:

Date node properties in Neo4j Browser

In PostgreSQL, we populate our date dimension with the same fields as in Neo4j but each date is a row not a node.


Order Nodes and Relationships 

In our graph, order nodes are the most connected node; you could compare this node to a fact table that contains no additive fields (also known as a factless fact table)

Joining orders.csv and order-details.csv in Kettle

We start by joining order-details to orders so that we can create all the nodes and relationships we want in one go.

Date and shipment receiver node lookups in Kettle

Next, we do some lookups on previously created nodes so that we can link the order nodes to date nodes and shipment receivers.

Creating order nodes and the relationship between products before grouping in Kettle

Calculate totals from unitPrice, discount and quantity; this reduces query time because values are precalculated. Create our order nodes and their relationships to products (remembering that the CONTAINS_PRODUCT relationship uses part orders coming from order-details.csv

Define the CONTAINS_PRODUCT relationship and its properties in Kettle

All the additive fields apart from freight are stored in our CONTAINS_PRODUCT relationship between Order and Product nodes. This is the most logical location to store these properties unless we wanted to create a Part Order which would only increase traversal and reduce performance of queries.

Creating relationships between orders and many other nodes in Kettle

Finally, we create all the relationships between our order nodes and the other nodes we created before using a sequence of Neo4j Output steps.

Define the SHIPPED_TO relationship and its properties in Kettle

It’s worth noting that we store our freight costs, another additive field, in the relationship between the Order and ShipmentReceiver: SHIPPED_TO. This allows us to maintain additivity without introducing complications surrounding the freight field as you will see later on.

The schema we have created looks like this:

“call db.schema()” in Neo4j Browser

Part Fact Orders

In PostgreSQL, we still join orders to order details to get part orders; however, we must isolate a single freight value for each order so that freight is additive. Some of the options here are:

  • Split the freight evenly between the products contained in an order.
    • This is misleading as packaging and shipping costs are usually dependant on size and/or weight of the package so we want to avoid splitting evenly.
  • Split the freight proportionately between the products contained in an order.
    • This is the ideal scenario but it is not possible because we do not have weight or size information for the products, unfortunately.
  • Store freight with only a single part order.
    • This is the compromise we chose as it maintains the additivity of the freight field and is less misleading.

As you can see, all options mean we cannot do analysis of freight costs per product. 

We accomplish this using a changing sequence and a javascript calculation.

Remove freight from all but the first row of an order in Kettle

Relational data warehouses depend heavily on surrogate keys to join facts to dimensions. For each dimension we have created a sequence for this purpose. 

When we create the fact table we lookup these sequences so we can add them to the fact table. This is a distinct difference between Neo4j and relational databases as Neo4j manages its own keys to identify which relationships are connected to a given node.

Dimension lookups in Kettle

Finally, before loading to the table we create a sequence to be the primary key for the fact table.

Add a primary key sequence before table output in Kettle

Querying

Let’s look at how we can query our newly created databases.

Value of sales for each year from customers in the USA

In Cypher:

MATCH (p:Product)<-[r]-(o:Order)--(:Customer)--()--()--()--(c:Country)
WHERE toLower(c.country) = "usa"
WITH o AS order, c.country AS country, r AS rel
MATCH (order)-[:ORDERED_ON_DATE]->(d)
RETURN 
  country, d.calendarYear AS year
, count(DISTINCT order) AS number_of_orders
, apoc.number.format(sum(rel.netAmount), '$#,##0.00', 'en') AS 
  value_in_dollars
ORDER BY year ASC

In PostgreSQL:

SELECT
  customers.country AS country
, dates.calendar_year AS year
, count(DISTINCT orders.order_nk) AS number_of_orders
, cast(sum(orders.net_amount) AS money) AS value_in_dollars
FROM
  public.fact_part_orders AS orders
, public.dim_customers AS customers
, public.dim_date AS dates
WHERE orders.customer_id = customers.customer_id
AND   orders.order_date_id = dates.date_id
AND   lower(customers.country) = 'usa'
GROUP BY country, year
order BY year ASC;

We can refactor our schema to include direct relationships between orders and cities, orders and regions, orders and countries, giving us a quicker way to retrieve the same results. After you do this the matching pattern changes from (p:Product)<-[r]-(o:Order)--(:Customer)--()--()--()--(c:Country) to (p:Product)<-[r]-(o:Order)--(c:Country) and the performance boost would be significant as there are less hops to traverse and fewer searches to complete. 

Products most likely to be bought together

In Cypher:

MATCH 
  p=(original:Product)--(:Order)--(related:Product)
WHERE
  toLower(original.productName) = "teatime chocolate
  biscuits"
RETURN
  DISTINCT original.productName AS product
, related.productName AS most_likely_to_be_bought_with
, count(p) AS popularity 
ORDER BY
  popularity DESC
, most_likely_to_be_bought_with DESC
LIMIT 5

In PostgreSQL:

SELECT
  original.product_name AS product
, related.product_name AS most_likely_to_be_bought_with
, count(r_orders.order_nk) AS popularity
FROM
  public.dim_products AS original
, public.dim_products AS related
, public.fact_orders AS o_orders
, public.fact_orders AS r_orders
WHERE 
    original.product_id = o_orders.product_id
AND o_orders.order_nk =  r_orders.order_nk
AND r_orders.product_id = related.product_id
AND lower(original.product_name) = 'teatime chocolate biscuits'
AND lower(related.product_name) <> 'teatime chocolate biscuits'
GROUP BY
  original.product_name
, related.product_name
ORDER BY
  popularity DESC
, most_likely_to_be_bought_with DESC
LIMIT 5;

Isn’t that a mouthful.

This type of query has become common in online shopping; the shop will recommend products based on what you are looking at or what you have in your cart.

As you can see in Cypher the Products most likely to be bought together query is more compact. Importantly, this makes querying far less error prone; accidentally running a cross-join because you forgot a join condition can go unnoticed and be very costly.

In SQL, fewer joins will lead to the best performance, especially when your fact table has several billions of rows (or you’re joining the fact table to itself like we are here). Neo4j does not have the concept of joins because there are no tables. Graph queries are easier to write, read and modify which is why recommendation queries work well in graph databases.


Improvement Strategies 

This schema is a good start and allows us to think about how to use Neo4j to analyse our data. In loading the data into Neo4j, we have come up with new ideas that have not been implemented as of yet.

Firstly, aggregation nodes could be a useful way to query old data quickly by storing pre-calculated values for later; these nodes play the same role as aggregation tables do in a relational database. In a schema-less model, we can add new nodes easily without building new tables making aggregation a valuable strategy.

The most simple version of this is to calculate the total value of an order and store it in that order, this should improve query time.

We can create these nodes on several aggregation levels, e.g. Yearly Sales, Monthly Sales, Daily Sales, etc.

There is also a possibility of creating geographic aggregation nodes, e.g. USA Sales, London Sales etc.

Separating date nodes into year, month and day nodes is another strategy; this should allow performant querying for specific years and months as a search through all properties of date nodes is not necessary.

Finally, creating a LinkedList between nodes of the same type may prove to be valuable. For example, (:Year)->[:NEXT_YEAR]->(:Year) allows you to compare one years sales to the previous years sales; the same can be done for previous and next month or previous and next day. Thus we can make use of reduced hop traversal to improve query performance when interested in sequences of dates. This is quite difficult to implement in a relational model as each comparison to a previous period and future period will require an additional column on the date dimension.


Conclusion

  • Cypher queries are less error-prone because its more difficult to miss join conditions when SQL-style FROM and JOIN are expressed through a single pattern.
  • Kettle has a nice plugin to visualise and perform your output to Neo4j.
  • We can optimise our graph for a number of different queries without impacting overall performance.
  • The flexible, schema-less nature means changes can be made without refactoring the whole ETL.
  • Graphs produce efficient recommendation queries.
  • There are many improvements yet to explore.

Next Steps

  • Compare performance on large, connected datasets between relational and graph databases.
  • Load and query databases built from the ground up for connected use cases: social media, map navigation, city planning.
  • Explore hybrid schemas (relational when needed, graph when appropriate) with a virtualisation layer.
  • Optimise Date nodes for different use cases.

Where to get the code

The Neo4j ETL can be downloaded here: loading_northwind_neo4j.zip

The PostgreSQL ETL can be downloaded here: loading_northwind_postgres.zip

Date Dimension Revisited: Bank Holidays

Everyone familiar with data warehousing knows about the date dimension. It’s one of the first steps in the creation of an ETL and exists in almost every data warehouse.

Despite how ubiquitous it is, many still fall for some common pitfalls such as:

  • Weeks shouldn’t be children of months
  • Missing or wrong week of year

These and other common pitfalls are described in a previous post: http://ubiquis.co.uk/dwh/date-dimensions/

Another commonly faced pitfall is that the date dimension often requires a field containing the relevant national public holidays to make it easy to correlate with an increase or decrease in sales. However it is not trivial to add these to your date dimension.

Here, we tackle this issue by creating an ETL that demonstrates the calculation of public holidays for France with particular focus on Easter and related holidays without neglecting fixed date holidays.

What’s a Holiday?

For the purpose of this exercise, we divide holidays into three categories: fixed date, variable date and Easter based date holidays. We consider fixed date holidays to be those that occur on the same date every year; variable dates are those that do not have a set date, like Thanksgiving, which occurs on the 4th Thursday in November; Easter based dates are observed a fixed number of days before or after Easter.

Fixed Date Holidays

A naive approach to generating fixed date holidays in PDI uses a Modified JavaScript value containing a condition to set the holiday field value if the day and month matches, e.g., 

is_holiday = 0;
holiday_description = "";
if (month_number == 12 && day_of_month == 25) {
  is_holiday = 1;
  holiday_description = "Christmas Day";
}

This is good if you only have a few fixed holidays but the code quickly becomes unwieldy as their numbers grow. Ideally this will be generalised into a single condition using variables for month_number and day_of_month.

Variable Date Holidays

Holidays defined as “the first Monday in May”, as is the case with May Day Bank Holiday in the UK, can be calculated in a similar way to fixed date holidays with an added condition week_of_month == 1.

Memorial Day, which occurs on the last Monday in May will require you to calculate if it is the last week of the month, in JavaScript it may look like this:

if (month_number == 5 && day_of_month >= 25 && day_of_week = 1){ 
  is_holiday = 1;
  holiday_description = "Memorial Day";
}

This only works for months with 31 days, 30 day months require day_of_month >= 24 instead; some tweaking would also be necessary in February, which doesn’t have a fixed number of days.

Easter based holidays

Some holidays rely on the day that Easter falls that year, eg. Good Friday, Easter Monday, Whit Sunday.

The complication with calculating these dates is that Easter is not a fixed date.

Easter falls on the Sunday following the full moon on or after the northern hemisphere spring (vernal) equinox. However, the vernal equinox and the full moon are not determined by astronomical observation. The vernal equinox is fixed to fall on 21 March.

Luckily there is a calculation for Easter Sunday called Computus (Latin for “computation”) The name has been used for this procedure since the early Middle Ages, as it was considered the most important computation of the age. We used the version found on wikipedia: https://en.wikipedia.org/wiki/Computus#Gauss’_Easter_algorithm

Solution

We have produced a generic solution to calculate fixed date and Easter based holidays for France. We have not included variable date holidays as there are none in the French calendar.

Additionally, to get a better understanding of how different techniques could be implemented and how they perform; we created a transformation using both a Modified JavaScript value and a User defined Java class in PDI.

The following is the general algorithm we wrote for the purpose of generating a field containing the holidays for our date dimension: 

  1. On the first day of the year
    1. Store all of the fixed date holiday descriptions and their dates in a dedicated object for later reuse.
    2. calculate the date that Easter will fall on and store it for later use.
    3. As we are interested in Easter Monday, rather than Easter Sunday, we add a day to the previously calculated date and store it in our object.
  2. If the holiday is in our object, set the flag is_bank_holiday to 1, bank_holiday_desc_en to the name of that holiday in English and bank_holiday_desc_fr to the name of that holiday in French.
  3. Output the three new fields: is_bank_holiday, bank_holiday_desc_en, bank_holiday_desc_fr

The Result

The algorithm stated above has been introduced to our standard date dimension to give a transformation that looks like:

The effect of calculating holidays as we have is shown in the table snippet below (some columns have been omitted and others have been renamed to make it easier to see the result).

From the table, we can see that Good Friday has been successfully calculated as two days before Easter Sunday and Easter Monday occurs the day after Easter, as expected. 

The fixed date holidays: Labour Day and Victory Day 1945 are also present and correct.

Common Pitfalls

In Java and JavaScript (and almost all other languages) month numbers do not go from 1 to 12 like the output of Gauss’s Easter algorithm does. Instead the months run from 0 to 11, where 0 is January and 11 is December. You’ll need to add or subtract 1 to convert from or to Java / Javascript dates.

What Next

If two holidays coincide, only the last occurrence will be written to the stream. One solution to this would be to concatenate the strings of holidays that occur on the same day.

If you want to include holidays for multiple countries you’ll need an additional column for each country to avoid confusing days marked as holidays in France when analysing sales data from Germany, for example.

If you need many countries, you’re probably better off by having a holidays dimension that accounts for all countries and has a key on the fact table.

In some countries, like the UK, if a bank holiday is on a weekend, a ‘substitute’ weekday becomes a bank holiday, normally the following Monday. Accounting for this would be valuable in the UK and USA.

Where Can I Get the Transformation?

Our implementation of a date dimension with Easter based holidays and fixed date holidays is available to download as a PDI transformation here: dim_date_holidays.ktr

Happy Pi Day!

Today is Pi Day, March 14 or 3.14 (using a rather awkward M.dd format).

So this morning, the following dialog occurred within our team:

– Happy Pi Day!
– You know what we should do? A PDI Monte Carlo simulator to calculate Pi…
– Uuuuh, nice. Yeah, I think I can do that.
– … without scripting steps.
– Challenge accepted!

Well, happy Pi Day everyone, and behold our awesome (though of limited use) Monte Carlo simulator to calculate an approximation of Pi. It uses only the following steps:

  • Generate rows
  • Generate random value
  • Calculator (x2)
  • Add sequence (x2)
  • Filter rows (x2)
  • Write to log

It runs “forever” and gives updated results on the logs for each 1.27 Million rows (approximately).

Remark: forever means until you reach 1b rows and the sequence counters roll over.

Oh, you may want to run it with Minimal Logging only.

Happy Pi Day!

Download here

Using PDI and C-tools to display real time scores analysis for UEFA Euro 2016

So, the Euro 2016 started this past Friday. We’ll have matches almost everyday until July 10th.

And here at Ubiquis we decided to start a little project around the Euro. The basic idea is: suppose England gets to half time level with Slovakia. Given both teams’ past history, what’s the most likely scenario? A win for England? A draw? A win for Slovakia? We got score change information from all Euro finals matches since 1960 from Wikipedia and parsed it with PDI. And using a real time scores API called xmlscores.com, built a couple queries that answer one basic question: “out of all matches in which team A was leading/trailing by X goals or more, at the same point in time, how many ended up in wins, losses and draws?”.

So, for example, when yesterday Germany was leading Ukraine by 1 goal at Half time, the dashboard would give us some idea of how often Germany managed to keep a lead and win the match, or how often Ukraine managed to turn the tables around when losing by 1 goal or more and ended up drawing or winning the match.

The dataset is quite small and the data model very simple, but it serves to show how Pentaho C-tools and PDI can be leveraged to create a real time information system, fed by external data sources, in a reliable maner.

Here’s the link: Euro 2016 Analysis dashboard (no longer available).

Hopefully we’ll use the learnings from this project to expand its scope and reliability and use it for other sports competitions. Stay tuned.

Secret Santa Generator

It’s Christmas! And with Christmas comes Secret Santa! We replicated the names in a hat process which is normally used for this kind of thing: write all the names in pieces of paper and put them in a hat; each person takes one, if you drew your own name, you put it back and draw another. If the last person draws his own name, the whole process is repeated. We know it’s not the most efficient way to do it but is an interesting challenge to implement in PDI. You can find it here.

We start with a list of names and emails in a CSV file. PDI reads them, generates two sets of random numbers, builds the pairings and checks if in any pair there’s a repeated name. If so, it repeats the process again, until we have a valid list of pairs of names and then it emails everyone with the names of the recipient they were assigned.

Using loops in PDI is a powerful pattern but a dangerous one. One should always guarantee that the job will not loop indefinitely, eventually blowing up with an out of memory error. In our case this would happen if the list provided has only one name on it, but we trust the user not to misuse the program (besides, what’ the point of having a secret Santa if there’s only one Santa to assign, anyway?).

PDI: getting the previous value of a field, but only sometimes

In PDI we can easily retrieve the previous (or next) value of a field easily, by using the Transformation step Analytical Query. It allows us to fetch the value of a given field or fields N rows back or N rows forward. Quite useful for things like cumulative sums (although for that we could just use a Group by step), getting the previous attribute values of a SCD, etc to map a SCD of type II to a SCD of type III, etc.

However, this is a rather limited step:

  • The number of rows back or forth is fixed; it can be a variable, but can’t be dynamically changed;
  • We can only get a field’s value, not anything more complex, like operations on fields;
  • We can’t get values based on a condition (e.g., if customer number is null, take the previous customer number; otherwise, build it using some algorithm)

We were facing a problem where the answer seemed to be the Analytical Query step… only that it wasn’t. Here’s the problem: on an event tracking database we have a category_raw field; as a general rule, this is the value we want to use as the true category of the object, let’s call it category_new. However, if the category_raw field value is ‘X’, we should ignore it and instead use the previous category_new value. Sounds simple, right?

Here’s a sample of the data and the desired output:

category_raw;category_new
A;A
B;B
X;B
D;D
E;E

From here it seems quite obvious: we use the Analytical Query step, fetch the category_raw value of the previous row and with a simple condition we evaluate

category_raw == “X” ? prev_category_raw : category_raw

However, if we have various consecutive exceptions,

category_raw;category_new
A;A
X;A
X;A
D;D
E;E

This approach doesn’t work: the 3rd row would fetch “X”, the value of the previous row, not “A” which occurred 2 rows before.

We tried to trick the Analytical Query step into fetching the value of the same field it’s creating, but that doesn’t work either. The step has to read values from one field and write a new field, but they must be different.

In the end we decided to go with a simple Java Script step, not so much because it’s the only way to go (you can quite easily fetch the objects you need using a Java class, Java expression, etc.), but because it’s simple.

Here’s a Javascript code:

var category_new;
if( category_raw == “X”){
category_new = getVariable( “prev_category_new”, “”);
}else{
category_new = category_raw;
}
setVariable( “prev_category_new”, category_new, “s”);

We know we’re setting a variable and using it in the same transformation, which is something we were always told is wrong. And usually, it is. However, as setting and getting the variable both happen inside the same step, it’s actually harmless: the variable is set for row 1 before it’s called on row 2, and so on…

Here’s a sample of the result:

category_raw;category_new
A;A
B;B
X;B
X;B
X;B
F;F
X;F
H;H

Each row picks up the correct category_new value, taking into account the handling of the excepcional “X” values.

Reading/Writing files from a Kettle CDA datasource in Pentaho 5.x

Kettle datasources in CDA are the most versatile way of getting data from disparate systems into a dashboard or report. Both C-tools and PRD expose Kettle as a datasource, so we can call a transformation that queries several different systems (databases, web services, etc.), do some Kettle magic on the results and then ship the final resultset back to the server.

However… there’s no way out of the box to reference sub-transformations in Pentaho 5.x. Solution files are now hosted on Jackrabbit and not on the file system and ${Internal.Transformation.Filename.Directory} just doesn’t work. If you have a transformation calling a sub-transformation and upload both to the repository, when you run it you get an error message saying that it cannot find the file.

Although this usually isn’t a problem, as most Kettle transformations used by dashboards are relatively simple, for more complex tasks you may want to use mappings (sub-transformations), call a job or transformation executor or, and perhaps the best use case, use metadata injection. In these scenarios you _really_ need to be able to call ktr files from the BA server context, and as it happens you can’t.

Well, sort of. As it turns out, you can.

The problem: lets say you have your server installed in /opt/pentaho/biserver-ce (or biserver-ee, we at Ubiquis don’t discriminate), and your kettle file is in your repository in /public/myDashboard/files/kettle/myTransformation.ktr. You want to use Metadata injection on a transformation called myTemplate.ktr and so you reference it by ${Internal.Transformation.Filename.Directory}/myTemplate.ktr in your metadata injection step. When you run your datasource, your error message will say something like

Cannot read from file /opt/pentaho/biserver-ce/pentaho-solutions/public/myDashboard/files/kettle/myTemplate.ktr because it’s not a file.

So what seems to be happening is that Kettle is using your pentaho-solutions folder as the root folder and it just appends the JCR path to it. Which obviously fails, as there’s no physical counterpart in your filesystem for the JCR files.

But, if you use the Pentaho Repository Synchronizer plugin to copy the contents of JCR to the filesystem and vice-versa (which is perhaps the simplest way to have your solution files under GIT or SVN, at least until the Ivy GS plugin becomes fully functional), then the synchronisation process will create a folder repositorySynchronizer inside your pentaho-solutions folder and use it to create the copies of every JCR file.

So, after synchronising JCR with the file system, your template transformation can be found in /opt/pentaho/biserver-ce/pentaho-solutions/repositorySynchronizer/public/myDashboard/files/kettle.

And now, the funny bit: to get it working, just create a symbolic link to remove the extra repositorySynchronizer element from your path: go to your pentaho-solutions folder and just type ln -s repositorySynchronizer/public public.

Now the template transformation is found and you can reference it from a transformation called by a dashboard.

Credit for the discovery goes to Miguel Cunhal, our junior consultant, that had the tough task of getting our metadata injection transformation running inside a demo dashboard. But more on that in another post.

GEM, a Generic ETL Machine

It’s finally here. After a few months of work, a lot of bugs fixed and a last minute refactor of all database connections, we are proud to release the first version of GEM, Generic ETL Machine.

It’s an ETL framework based on Pentaho Data Integration, to automate most of the common tasks that take time to develop, are essential to ensure a proper maintainable ETL, but that are hard to explain when the project’s stakeholders want to see their data. Things like enabling a solid logging mechanism with email alerts; data lineage to allow us to trace the data from the reports and dashboards, to the cube, then the fact table, then the staging tables, then the source files or databases, complete with line numbers, date the record was extracted, etc; a way (still very archaic) to define the order in which several tasks should run; a way to quickly deploy from the development laptop to a server and switch environments, from dev to test, to prod; and all this, of course, configurable.

This is still the first version and a lot of work is left to do. For the time being there’s no user interface to display the status of each ETL run or the results, all rollbacks must be performed manually in the database and there’s no way to schedule different tasks at different intervals. That’s all on the roadmap, time permitting, together with increasing the number of available samples (CSV files and web services to name a couple), support for PDI clusters, output and input directly into Hadoop in its multiple shapes and forms.

But it’s usable as it is (well, sort of – we only support a couple databases on each stage for the time being) and it’s been allowing us to spend a significant larger amount of time dealing with what really matters when we have to develop an ETL from scratch: the data analysis and the design of the data model. Not enabling file or DB logging, or setting up the email step for the 500th time.

Clone it, fork it, commit to it, enjoy!

source code on Github