Our latest SQL Code Reuse survey was sent to tens of thousands of data professionals to get a current pulse on the methods, frequency, and effectiveness of SQL code sharing and reuse. We’ll be publishing deeper analysis and commentary in an upcoming white paper, but want to share some initial insights below, such as the fact that nearly half of respondents share SQL with colleagues on a daily or weekly basis.

As strong advocates for analytic code sharing and reuse, we’re often asked why we don’t integrate with GitHub or other Git repositories. The simple answer is that Git isn’t often a great fit for sharing SQL code. This post explains why, and suggests a better alternative.

Centralized analytics moves too slowly to respond to an evolving set of business needs. There’s simply no way a centralized data engineering team can understand and build all the analytic capabilities every team in the business needs…nor can it be fast enough.

Data is essential for organizations but always remember, if you’re not staying up to date to make sure data (and analytic output) is still relevant, your analytics will become a liability vs. an asset. Both unrevised analysis and analysis done from scratch are prone to the risks of orphan analytics and analytics drift.

Aginity Pro update release 35 is now available for download on MacOS and Windows. In addition to extending our Data Insert Wizard capabilities to another database platform, we’ve added support for a legacy version of Microsoft SQL Server, three new authentication types for Amazon Redshift, and some helpful UI improvements to the connections screen. View demo video.

A great team is more than the sum of its parts: some team members will have special aptitudes or skills; their levels of experience will differ. That’s why collaboration among data analysts is so powerful.

Our latest release of Aginity Pro, v34, is now available for both MacOS and Windows. This update includes deeper integration with Amazon Redshift, Cross-Database Autocomplete, and some just-right UI improvements.

We hear it all the time: data engineers and analysts complaining about how hard it is to locate existing SQL assets, whether generated by themselves or by a teammate, for reuse in a new project.

As organizations ingest and process more data, managing this crucial business asset can create insights into just about every aspect of the business: customers, employees, information systems, partners’ performance, marketing efficiency — the list is effectively endless.

Good analytics is equal parts business acumen and technology expertise. Even so, it’s easy to become imbalanced in pursuit of ever-deepening data engineering skills.

Today we’re excited and proud to announce Aginity support for the newly released Amazon Redshift Data Sharing.

With all the interest over the Snowflake IPO, I wanted to share a few thoughts. Now I am not the right person to tell you if these valuations will pay off and for whom. You’ll have to look elsewhere for investment advice.

At Aginity, we are lucky to interact with Data Analysts, Data Engineers and Data Scientists from across a wide range of industries and company sizes. It gives us a birds-eye view of some trends in data processing and the thousands of users of our SQL query applications…

It’s been an unprecedented year of growth for us at Aginity. With the recent launch of our two next-generation solutions Pro and Premium, we’ve got tens of thousands of individuals and enterprises depending on our products in their everyday workflow.

If the retirement of Netezza has you floundering, we’ve got at least one point of consolation: this doesn’t mean you have to part ways with Aginity! In addition to supporting IBM’s next generation database offerings with support coming out this year, Aginity already enables your SQL query and development workloads on 10 different databases (and counting), including cloud specialists like Azure Data Warehouse and Amazon Redshift.

It feels like every conference talk, blog post, podcast or marketing message that I get these days is touting the amazing benefits of Data Science, Machine Learning (ML) or Artificial Intelligence (AI) (or if you are old school, you probably recall the marketing and promotion of many similar techniques as “Statistical Modeling”).

Anyone who works with code knows that learning doesn’t stop when school’s out—it continues every single day. (Even on luxurious summer days!)

There is a movement that’s happening. An analytic movement. Analytics is one of the most important and valuable assets to an organization—next to you, of course. Yet we don’t seem to give analytics the love it deserves. This motivated us to take a deeper look at how we treat analytics today and how we can uncover its potential and showcase its value.

It finally happened. Your star analyst, beloved by the business for producing those top-notch analytics, just flew the nest.

In the past week, two leading tech platforms spent $18.3B acquiring business intelligence and data visualization companies.  Google acquired Looker for $2.6B, and Salesforce acquired Tableau for $15.7B. 

We all have our own ways of organizing stuff at home and at work. My friend swears by bullet journaling, my boss juggles multiple Google calendars and productivity apps, and my daughter keeps a simple to-do list. There’s no right or wrong answer here. We all have different ways of keeping a grip on our life.

It’s unavoidable—at every conference, all I seem to hear about is Artificial Intelligence (AI) and Machine Learning (ML). I can’t help but think about when I was kid and AI meant robots like C3PO from Star Wars, Chip from Not Quite Human, or Rosie the Maid from The Jetsons.