Ajilius loves Snowflake

Snowflake HeartAlong with Amazon Redshift and Azure SQL Data Warehouse, Ajilius does cloud data warehouse automation for Snowflake Elastic Data Warehouse.

We don’t just support Snowflake, in a short space of time it has become a favourite cloud data warehouse platform. We had a great time working with the Snowflake team during the development of their Python adapter.

Here are some of the things that we love about Snowflake.


Our Snowflake development and demonstration platform cost around AUD300 per month, on a lousy exchange rate. We pay a monthly storage cost, then pay for just the processing we need, when we need it.

For the smallest development machines Redshift may be slightly cheaper, but once you scale to multi-terabyte production workloads the advantage shifts to Snowflake unless you are prepared to commit to three-year reserved instances.

Given the rate of change in the cloud data warehouse market, we believe that long-term commitments are not in the interests of most customers, and Snowflake has a price/performance advantage.

Microsoft Azure SQL Data Warehouse is still in preview, and we can’t comment on comparative pricing at this time.


Scaling our Snowflake platform takes just seconds. In comparison, we’ve seen cluster resizing on Amazon take many minutes, and we’ve seen it take longer in customer sites.

Snowflake instances can scale from 1 to 128 8-core nodes. That is a huge amount of compute power, making Snowflake suitable for workloads of any size. At the lower end, we see Snowflake as an ideal platform for mid-market customers as its entry point and pricing model is so flexible.

We do a lot of Ajilius work without incurring any processing costs. This is because DDL operations are performed on the database, not the warehouse (see Features), and we don’t need to start a warehouse until we start actually loading, selecting or modifying data. The majority of our development and test work is done on a single node, with occasional scaling for performance tests.


I’ve never had better support from a data warehouse company, especially when we were not known to the vendor, and not spending huge amounts of money. From sales, to pre-sales, to support, and even right into engineering, we’ve had amazing engagement from every level of the company.

Snowflake people respond to emails, pick up the phone, and respond to support requests with speed. We’ve never waited more than a couple of hours for a response to an issue, and that response has always been highly relevant, never of the type “have you unplugged and plugged in the keyboard” variety.

The Snowflake team is knowledgeable, enthusiastic, and committed to success.


One intriguing feature of Snowflake is its avoidance of distribution keys, partitions, etc., in the database. This avoids one of the big design challenges present in both Redshift and Azure, where the wrong distribution method can really damage your performance. One day I’ll have a beer with Snowflake’s designers and figure out how this works, but for now, all I know is that it works well.

Better described as “quirky” is Snowflake’s terminology of database and warehouse. A “database” is a collection of schemas and data. A “warehouse” is the compute configuration that works on databases, to me they’d have been better off using a name like “server”. A powerful feature is the ability for multiple “warehouses” to act on a “database”, with different configuration settings. For example, an ETL warehouse might use very high scale to compress the ELT time, while a Browse warehouse might run for a long time at low scale for refreshing data used by BI tools like Tableau and Qlik Sense.

Another feature we love is the Snowflake administrative console, where we can not only administer databases, warehouses and users; but also review performance history and execute ad-hoc queries. The user interface for the console is a work of art, it is the first cloud-based data warehouse where I’ve not felt the need to find another administration tool.

What’s Missing?

Not much.

All the basic data types are there, all the basic SQL statements are there, you get JDBC, ODBC and Python interfaces, and the documentation is a work of art. There could be a few more examples in documentation for some of the more obscure features of the product, but it is being updated on a frequent basis.

Regarding data types, I’ve always been puzzled why data warehouse vendors avoid geospatial data. After all, map-based data is a major feature of the current generation of visualisation tools, but it is lacking from most cloud data warehouse platforms. I’d like to think Snowflake will get around to this feature soon.

If I was being picky, I’d also call out the absence of a TIME data type. We work around it by the use of date/time functions to extract time portions of timestamp fields into text fields, but a native TIME type would be helpful.

The only real pain we experience is that Snowflake is currently restricted to Amazon US data centers. That has no impact on warehouse performance, but our connection times and data transfer rates are a little slower than I’d like. We can co-locate Ajilius instances in their AWS data centre for fast Snowflake connections, but if your data is on-premise in Australia, you’re going to incur a penalty if you’re moving terabytes into Snowflake. I’m assured that data centers in other parts of the world are on their way.


Try it. You’ll like it.

Ajilius makes it easy to build Snowflake data warehouses from your on-premise and cloud data. Let us know if you’d like a deeper discussion of Snowflake and Ajilius.

One year of Ajilius

birthdayAjilius is now one year old.

Just over 12 months ago, we announced a new data warehouse automation platform, designed for a modern data warehouse workload.

We delivered all our objectives for complete Kimball support, on-premise and cloud, three click migration between databases, and full cross-platform portability.

We published a four-release roadmap for V1, and we’ve met its quarterly delivery schedules.

We’re not fully profitable, but we now have enough customers that we’re sustainable and growing.

This year we’re embarking on an ambitious roadmap for V2, with a focus on Data Quality, Data Profiling and Data Discovery. Again, we have defined a quarterly release schedule, and V2.1 will be delivered in March (we’re in beta already).

We’re also stepping up our marketing this year, with our first conference attendance being pgDayAsia, in Singapore, from March 17-19. We’ll be speaking on Data Warehousing and PostgreSQL, as well as using the occasion to showcase Ajilius 2.1.

Here’s to a great 2016 in data warehousing!

IBM DB2: Back to DSN

The devs told me that getting DB2 and Informix drivers to work took a bit of fiddling. That was the understatement of 2015. The driver setup experience is so bad that we can’t include it in the Ajilius installer.

On every platform we needed to manually copy files around, adjust environment variables, sometimes patch libraries, and often it just didn’t work. We tried the Python ibm_db adapter, we tried IBM’s ODBC / CLI adapter, and experienced nothing but pain.

As a result, we’re dumping the work we’ve done on native adapters, and reverting to the use of ODBC DSNs for data sourcing against DB2 and Informix.

To use DB2 as an Ajilius data source, have your IT department deploy an appropriate ODBC connection on your Ajilius server, then create a DSN to your data source. Select “ODBC DSN” to create your data source in Ajilius, then enter the DSN name in the Database field.

It is sad that IBM’s quality has sunk so far. I started my IT career on IBM System/34 computers well over 30 years ago, and at various times worked on System/38 and AS/400. I used one of the first DB2 mainframe installations in Australia, followed by the first OS2/EE implementation of DB2, before using DB2 on Windows as the core of a successful ISV product. Later, I ran a DBA team that included DB2 on mainframe, Linux and Windows in its portfolio.

You couldn’t call me a DB2 hater with that background, but the current connectivity options are rubbish.

DB2 is not a bad DBMS, but what good is a DBMS  without great connectivity? I’d struggle to recommend it to anyone based on my most recent exposure and I’d definitely not recommend it to any Python developer.

IBM Bluemix

During the past week we have been testing the new data source adapters for DB2 and Informix. This time we’re using IBM’s Bluemix cloud to host our test databases.

The initial experience with Bluemix is awful. A bizarre labyrinth of errors about missing spaces and empty containers, all solved when you finally realise that the service you want to provision is only available in some regions, and your default is not one of them.

Depending on the region you have chosen, there are many supported databases including variants of Informix, DB2 and Netezza, as well as a variety of open source, big data and NoSQL products.

Once you’re up and running, the actual database experience is quite good. I like the data load feature, which quickly helps you to move test data into the database. The help around connectivity – CLI, ODBC and JDBC – is also good, with all the connection information clearly presented for each of the options.

The free database allowance for SQL DB (the old DB2 LUW) is quite generous, enough for us to complete all our testing. If you want more than the free tier supports, though, the next step jumps from zero to $500 per month. That is expensive compared to Azure SQL and AWS RDS databases.

dashDb (Netezza) and Time Series (Informix) start at around $55 per month, which is reasonable value compared to other vendors.

Our testing is focussed on connectivity and extracts, not in-database performance, so we can’t comment on how well the platform scales.

Well, time to get the testing finished, as this is the last task between us and Version 1.4.

MySQL, MariaDB and Aurora

This afternoon I signed off the enhanced data source adapters for MySQL, MariaDB and Amazon’s new Aurora database.

These adapters are compatible with both on-premise and cloud-hosted databases, with full Unicode support.

We’re fully tested against the Employee, Sakila and Classic Models sample databases, so now it is time to see some customer databases being loaded.

That’s another big step on the path to Version 1.4, only the enhanced DB2 adapter remains in the queue.

Adwords puzzle

For 10 months we’ve been testing combinations of Google Adwords keywords to drive traffic to Ajilius. Over that time we’ve got a pretty good idea of which keywords work.

As we planned a new campaign for the New Year, we decided to turn off Google advertising for a couple of weeks, to reset our no-advertising baseline.

The expected result was that traffic would go down, and it has, but not quite as much as we expected. That suggests search results rather than advertising may be driving a higher volume of traffic than we thought.

What was unexpected was the change to Google search results.

When running an Adwords campaign, if we searched for one of our terms such as “data warehouse automation” we were usually on result page 2 or 3, with many competitor ads above us.

Now that we’re not running a campaign we’re in roughly the same search position, but there are far fewer competitor advertisements showing above us.

Could it be that Google artificially inserts advertisements above yours, pressuring you to increase your budget to move you up the page?

That theory smells of tin-foil hats, but it is something for us to watch over the next few weeks.

Dependency diagrams

With Version 1.4 due by the end of December, we’ve just made our final implementation decision regarding dependency diagrams.

Over the past month, the team has produced literally hundreds of diagrams, testing fourteen different approaches to diagram generation.

The short list of implementation technologies came down to three Javascript libraries – Mermaid,VisJS and Cytoscape.

Mermaid’s Dagre layout algorithm produced the best technical appearance in diagrams, but its layout engine experienced severe difficulties when diagrams became more complex. Development is lagging, and is dependent on an unsupported core library.

VisJS was good, but it suffered from hierarchy layout problems, as well as labelling conflicts in diagrams. I think VisJS is more generally powerful, but fell back in our use case.

The best compromise solution was Cytoscape. It is in active (and enthusiastic) development, has a growing selection of layout algorithms, does the best job of laying out our diagrams, and supports features we need such as custom colours and tooltips.

Here’s a sample of what you can expect with the coming release of V1.4.


Release 1.3.11

This release fixes a couple of minor errors, and makes some productivity improvements to existing functions.

Stage Column Documentation
If a column was manually added to a stage table after initial creation, the documentation entered at this point was not updated. This has been fixed.

First Column Calculation / Literal
If a calculated column, or literal, was moved to the first column position in a table, Ajilius would generate invalid transform SQL. This has been fixed.

We’ve made the following changes to existing functions.

Enable Transformations on New Columns
We made an early design decision that transformations were applied by changing a column. When adding a column, we did not know if it was capable of being transformed until after the addition. This means that if you wanted to re-add a previously deleted column, and upper-case it at the same time, that you had to go through two steps – add the column, then change it to add the transformation.
We have now modified the Add Column function to enable you to enter your choice of source-column, role, transform and calculation. Roles and transforms can only be applied to selected source-columns, not to calculations. If both a source-column and a calculation have been entered, the source-column selection will take precedence.

Enable Column Selection on Join Tables
You might recall a post, many months ago, titled Our Join Editor Sucks. Well, Join Editor V2 was a big improvement, but still had some workflow issues. When you joined a table, all columns from that table were copied into the join set. You could delete columns from the column management functions, but users (self included) did not like this interruption to the thought process that typically happens when joining tables.
The new Join Editor enables column selection at the point of join definition. A toggle supports select-all functionality on the list, or you may select individual columns. This is a small improvement when you want all columns from a joined table, but a huge leap forward in productivity when you only want one or two columns from a larger table.
You can still used the column management functions to add, change and delete individual columns from the join set, as well as sort the columns into your preferred sequence in the table.

Registered users will be notified by email of the download location. If you’re reading this and have not received your email, please contact support@ajilius.com.

PostgreSQL message encoding

We ran into an interesting error this week, where a job was ending with an encoding error on the completion message.

Our first thought was that the database encoding did not match the value in the metadata, but no, both were set to UTF-8.

This is an Italian customer, and after much head-scratching, we discovered that the configuration value for lc_messages was set to ‘Italian_Italy.1252’. Ajilius expects messages returned by the server to be in the same encoding as the data warehouse.

In this case, the DBMS had been installed with settings from the Italian locale on the server, but the data warehouse database had been created with UTF-8 encoding.

We will now recommend that servers are initialised with initdb using the preferred encoding.

In this case, the customer changed the value of lc_messages in postgresql.conf, and the problem was resolved.



Release 1.3.10

This release is a quick maintenance update, with a minor enhancement to built-in transforms.

WHERE Test Error
The Test button on the WHERE clause editor may have triggered a fatal error screen. The clause was correctly written, but could not be tested. ELT jobs worked correctly. This error has been corrected.

We’ve made the following changes to existing functions.

New Transforms
We’ve added the following transforms to the Change Column screen:
– Cast to Integer
– Cast to Numeric

Registered users will be notified by email of the download location. If you’re reading this and have not received your email, please contact support@ajilius.com.