Ajilius 2.4.7

This week’s release 2.4.7 brings the following new features:

  • Salesforce Adapter. Connect to Salesforce, browse and profile your data, then quickly load it to your choice of data warehouse for transformation to facts and dimensions.
  • Double-Bar delimiter. Delimited files now accept a double bar (||) as a column delimiter.

The following errors have been corrected:

  • Historic Persistent Staging. CTAS versions were not correctly updating the current row flag for this variant of persistent staging.
  • Blank lines in log. Occasional blank lines were being appended to the console, and have now been suppressed.

Registered users will receive an email over the next 1-2 days with download instructions.

Go build some data warehouses šŸ™‚

 

Cloud Evaluations

We’re pleased to announce that the Ajilius evaluation version now includes all cloud data warehouse platforms.

Previously, we restricted evaluations to on-premise databases. This was to simplify the evaluation environment.

Now, we’re finding that more and more customers are moving their entire workload to the cloud, and there is no on-premise database. And as those workloads move, the knowledge sets of evaluators move with them.

In recognition of this shift, we now includeĀ all databases – both cloud and on-premise – in our evaluation version. You can now trial Snowflake, Redshift and Azure SQL Data Warehouse alongside favourites like SQL Server, PostgreSQL, MariaDB and Exasol.

Ajilius. Tomorrow’s data warehouse, today.

 

#CloudCred

I saw a great tweet from Snowflake today:

I laughed when I read it, it exactly mirrors the way I feel about Oracle, IBM and Teradata (among others) with their cloudwashing of legacy DW platforms.

We’re cloud-first. Cross-cloud. Multi-cloud. Even hybrid-cloud. We’ve been working with cloud data warehouses since they were born.

Ajilius. CLOUD data warehousing.

Release 2.4.2

This week’s release makes the following changes:

  • New data type “char” for single character columns. Used internally for SCD_FROM column in type-2 slowly changing dimensions.
  • New TRUNCATE option for the command line scheduler, cleans up transient data after loads to minimise storage costs on cloud platforms.
  • PostgreSQL interface on Windows was not always being fed UTF-8 encoded data. Added encoding parameter to byte conversion.
  • Deleting the active data warehouse repository on Windows would sometimes fail. Now triggering call to garbage collector to ensure closed connections are purged from memory.

Registered users will receive an email in the next 1-2 days with download instructions.

Interestingly, the last two items only showed up when we shifted our development platforms from OSX to Windows. Really liking the change – like moving back home after living on the road for a while – but it does show up the quirks of the Windows internals compared to OSX and Linux.

The list is lighter than usual because we’re working hard on new features for Version 3 … stay tuned for updates!

 

Why Data Warehouse Automation kicks the crap out of SSIS

Here’s a great explanation of how to do incremental loads in SSIS. It uses third-party components from Pragmatic Works, and a combination of hand coding and SSIS tasks.

http://blog.pragmaticworks.com/incrementally-loading-data-from-salesforce.com

See how complex it is? How long it takes?

Here’s how to do the same thing in Ajilius:

Simply check the box on the column/s that govern incremental change.

Under the covers we do almost exactly the same tasks as described in the blog post, but the difference is thatĀ we do them, not you. You make a decision in the UI, and we instantly generate all the code and logic that implements that decision.

That’s it … a perfect example of why Data Warehouse Automation kicks the crap out ofĀ  great ETL tools like SSIS, and why we deliver data warehouses in a fraction of the time spent in those products.

Ajilius. Data warehouses. Faster.

Release 2.4.1

We’re back in the normal release cycle, with our first update to 2.4.

Here’s what you’ll find in this release:

  • New batch scheduler command line parameter “-x” to extract one or more tables to CSV.
  • Printing of scriptĀ messages in batch output.
  • Licensing revisions making it easier and faster for us to create evaluation license keys.
  • Support for delimited files with spaces in file name.
  • Error corrections.

Internally, we added six new automated test cases this week, around delimited file names, and SCD2 dimension processing. Our automated testing process is really starting to pay off, none of the errors we fixed this week were reported by users, all were found through automated testing.

Registered users will receive an email in the next 24 hours with the download details.

 

Ajilius 2.4.0

We’re a month late, and we’re sorry. But Ajilius 2.4.0 has finally been delivered.

We’ll blog more about specific features, but here’s what you have to look forward to in this release:

  • BI Accelerators for Yellowfin, Tableau and Qlik. Full business-friendly metadata generation at the click (Qlik?) of a mouse. We’re theĀ only data warehouse automation platform to support all three platforms.
  • BI Views for a unified query experience across any BI tool, including PowerBI and Excel. Now your PowerPivot users can share the same business-friendly table and column names as their Yellowfin and Tableau colleagues.
  • A revised, high-performance CTAS engine for MPP platforms, including Microsoft APS/PDW, Azure SQL Data Warehouse, Redshift and Snowflake. And we’re still the only data warehouse automation platform to support three click migration between supported platforms.
  • Full support for Microsoft APS/PDW. That makes us theĀ only data warehouse automation platform that supports EVERY Microsoft RDBMS, SMP and MPP, on-premise and cloud.
  • A new Inference engine for managing early arriving facts. You have the one-click choice of automatically inferring dimension rows when new values are found in fact processing, or simply assigning those rows to the “Unknown” value.
  • Integrated Authentication for all Microsoft sources and targets.

Now the work begins on the Version 3.0 series of releases!

 

Why SQL Server on Linux Rules (again)

I just upgraded three servers. It took less than 5 minutes.

On each server:

sudo apt-get update
sudo apt-get install mssql-server

No more gigabyte downloads (it needed 145Mb), no bewildering and befuddling multi-page installer screens, just two simple commands and the job was done.

On Linux, at least, SQL Server has gone from one of the most complicated installers in the industry to one of the most elegant.

I love it šŸ™‚

 

Yellowfin Integration

One of the challenges in self-service BI is that you can quickly end up in your BI tool’s version of Excel Hell. That is, every user has their own definition of data, sources and aggregations, leading to the pre-DW situation where no two reports ever agreed.

Ajilius now brings a new level of governance to self-service, by directly integrating the data warehouse metadata with the BI tool. Over the 2.4 development cycle we’re bringing integration to a number of popular BI platforms, beginning today with Yellowfin.

yellowfin1

Select a fact table, select a destination, and a full Yellowfin model for your star schema will be written to that destination.

Use the Import function in Yellowfin, and your model is imported as a Yellowfin connection and view.

Not only do we generate the metadata, but Yellowfin makes round-trip integration a breeze. In our demonstrations we show how models can be enriched in line with changes to the data warehouse, preserving your existing reports and dashboards.

Now your data warehouse and your BI solution can be closer than ever before.

Ajilius. Self-serviceĀ and governance.