The tech industry is chock-full of events this week. In the data space, there are no fewer than four events happening concurrently: SAP is having its Sapphire Now event in Orlando from today through November 7th; Databricks' Spark + AI Summit started yesterday in San Francisco and continues through tomorrow; Postgres Vision is in Boston, running today through the 7th; and Alteryx Inspire started in Anaheim yesterday and also finishes up on the 7th.
Don't be too surprised; this week provides a last blast before the lazy, hazy days of summer waft in. And once that happens, we'll need to wait until Strata Data in New York in September for another major news cycle in the data world.
Anyway, as you might expect, SAP and Databricks have some announcements of their own. SAP's is around its HANA Data Management Suite; Databricks' announcements concern what the company calls the "AI dilemma" -- actually getting a successful workflow around doing AI work and getting it into production. Big on Data compadre Tony Baer has both companies' news nicely packaged up for us already, separate posts:
Also read: SAP Launches HANA Data Management Suite
Also read: Spark Summit 2018 Preview: Putting AI up front, and giving R and Python programmers more respect
But with the Postgres and Alteryx events also going on, there's still more to cover. I also want to take a little time to analyze how the confluence of these events and announcements should be interpreted.
Quest's Toad leapfrogs to full-on open source RDBMS support
At the Postgres show, Quest is announcing new support for that open source database in Toad Edge, the open source database version of its juggernaut relational database management system (RDBMS) tool. Toad was originally created for Oracle's database, and a Microsoft SQL Server version has been with us for years now. Quest introduced Toad Edge, with support for both MySQL and its offshoot MariaDB, earlier this year. And now Quest is adding support for Postgres to the product.
With Toad versions also available for IBM's DB2 as well as for SAP solutions (including SAP Adaptive Server Enterprise, HANA, IQ, and SQL Anywhere), Quest now has its operational RDBMS territory covered, both in the commercial and open source software realms.
Alteryx pivots to new databases, users
Alteryx Platform 2018.2 was announced at Inspire today and it too is adding support for new databases, MySQL among them. The new metadata loader for that database is joined by others for Microsoft Azure (ostensibly that means Azure SQL Database and perhaps SQL Data Warehouse); Snowflake's cloud data warehouse (and for Snowflake Bulk); and Qlik (both Qlik Sense and QlikView). Alteryx also added in-database (pushdown) support for its generic ODBC connector and for MySQL.
Beyond the database support, Alteryx is adding "guided sessions" and access to its community knowledge base via the community search bar, which will now be visible in Alteryx Promote, Alteryx Connect and Alteryx Designer. It's also added built-in cues for less technical users to help them through dragging, dropping, connecting tools, configuring tools, running workflows, and how to get help and training. Together, these moves are designed to provide more of a self-service experience in Alteryx's tools, which have always been geared to more of a technical user demographic.
Finally, Alteryx is adding SAML support for Alteryx Server and Alteryx Connect to integrate better with Enterprise single sign-on configurations. It's also adding Japanese language support to what it already has for English, French and German.
Where are we now?
What we see from the Quest and Alteryx news, as well as the SAP and Databricks news to some extent, are various attempts to tame the massive fragmentation in the data world. Quest is bringing open source RDBMSes to the same tooling platform as the major commercial ones; Alteryx is adding support for both open source and cloud databases to its tooling.
SAP's aim is, in Tony Baer's words, "governing and integrating data across highly federated environments." Databricks, meanwhile, is trying to bring sanity to AI workflows, support for both of data science's main languages (Python and R) and for numerous popular machine learning and deep learning frameworks.
This is all positive, especially as a set of deliverables bestowed upon us before the summer months. The data world has experienced a ton of innovation over the last eight or nine years. Now it's time to clean up the mess, rationalize similar but heretofore siloed technologies and, as a general matter, make the modern data analytics and AI stack more accessible and, especially, more productive.
There will be lots more work in this area when things pick up again in the Fall.