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Top Five Google Cloud Database Services — Part 1 (SQL)

Google Cloud Platform offers various cloud database tools and services based on what a company needs to accomplish with them. Thus, the different tools have different purposes.

In this article, we'll look at the top five Google Cloud database services/tools that support SQL in one form or another. I'll take you through each of their features and benefits and provide some code snippets. Above all, I'll briefly explain when you can use them. Here is what we will cover:

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10 AWS Services That Use SQL

Amazon Web Services (AWS) is the biggest cloud platform in the world, with over 200 features. In this article, we break down 10 AWS services that support at least some SQL syntax, talk about their use cases, and give examples of how to write queries.

Service Description SQL Support Use Case
RDS Postgres, MySQL, etc. Full Small-medium web apps
Aurora Serverless databases Full Serverless apps
Redshift Data warehouse Full OLAP, Petabytes of data, analytics
DynamoDB NoSQL database Some - PartiSQL Ecommerce, building fast
Keyspaces Managed Cassandra (key value) Some - CQL Messaging
Neptune Graph database Some - openCypher Social networks
Timestream Time series database Partial IOT, Logging
Quantum Ledger Cryptographically verified transactions Some - PartiSQL Finance
Athena Ad-hoc queries on S3 Some - CTAS Historical data
Babelfish MSFT SQL Server on Aurora Full .NET

The table above shows how SQL support varies between the services. A graph database cannot be queried in the same way as a classic relational database, and various subsets of SQL, like PartiQL, have emerged to fit these models. In fact, even within standard SQL, there are many SQL dialects for different companies like Oracle and Microsoft.

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Postgres Connection Pooling and Proxies

One essential concept that every backend engineer should know is connection pooling. This technique can improve the performance of an application by reducing the number of open connections to a database. Another related term is "proxies," which help us implement connection pools.

In this article, we'll discuss connection pooling, implementing it in Postgres, and how proxies fit in. We'll do this while also examining some platform-specific considerations.

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Why You Need To Backup Your Postgres Database and How To Do It

Do you remember the last time you worked hard on an essay or paper, only to lose it all when Word or your computer suddenly crashed? Hours of work are gone because you didn't hit the save button often enough. That sort of frustration could drive even the bravest of souls to tears.

You might be able to afford this kind of data loss once in a while, but now imagine it's an entire database with Terrabytes of information—not just one document. Especially if that information is in a production environment, the damage could be catastrophic, costing millions of dollars. Not even mature organizations can afford these mistakes, and database backups prevent precisely this kind of situation.

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Should You Invent a New Query Language? (Probably Not)

Should You Invent a New Query Language? Cover Image

"What's worse than data silos? Data silos that invent their own query language." - Erik Bernhardsson

In his infamous and widely discussed blog post named ' I don't want to learn your garbage query language,' Erik Bernhardsson expressed what so many other Data Engineers and Analysts related with so strongly. Namely, that he "really [doesn't] like software that invents its own query language" and that he "just [wants his] SQL back."

The fairly short-yet-passionate rant summarized the almost universal experience that technologies that require their own language often produce a whole new different set of complexities.

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No-Code: ”It’s a Trap!”

Gartner predicts that by 2023, over 50% of medium to large enterprises will have adopted a Low-code/No-code application as part of their platform development.

The proliferation of Low-code/No-code tooling can be partially attributed to the COVID-19 pandemic, which has put pressure on businesses around the world to rapidly implement digital solutions. However, adoption of these tools — while indeed accelerated by the pandemic — would have occurred either way.

Even before the pandemic, the largest, richest companies had already formed an oligopsony around the best tech talent and most advanced development tools. Low-Code/No-code, therefore, is an attractive solution for small and mid-sized organizations to level the playing field, and it does so by giving these smaller players the power to do more with their existing resources.

While these benefits are often realized in the short term, the long-term effect of these tools is often shockingly different. The promise of faster and cheaper delivery is the catch — or lure — inside this organizational mousetrap, whereas backlogs, vendor contracts, technical debts, and constant updates are the hammer.

So, what exactly is the No-Code trap, and how can we avoid it?

What is a No-Code Tool?

First, let's make sure we clear up any confusion regarding naming. So far I have referred Low-Code and No-Code as if they were one term. It’s certainly easy to confuse them — even large analyst firms seem to have a hard time differentiating between the two — and in the broader context of this article, both can lead to the same set of development pitfalls.

Under the magnifying glass, however, there are lots of small details and capabilities that differentiate Low-code and No-code solutions. Most of them aren’t apparent at the UI level, leading to much of the confusion between where the two come from.

In this section, I will spend a little bit of time exploring the important differences between those two, but only to show that when it comes to the central premise of this article they are virtually equivalent.

Low-Code vs. No-Code Tools

The goal behind Low-Code is to minimize the amount of coding necessary for complex tasks through a visual interface (such as Drag 'N' Drop) that integrates existing blocks of code into a workflow.

Skilled professionals have the potential to work smarter and faster with Low-Code tools because repetitive coding or duplicating work is streamlined. Through this, they can spend less time on the 80% of work that builds the foundation and focuses more on optimizing the 20% that makes it different. It, therefore, takes on the role of an entry-level employee doing the grunt work for more senior developers/engineers.

No-Code has a very similar look and feel to Low-Code, but is different in one very important dimension. Where Low-Code is meant to optimize the productivity of developers or engineers that already know how to code (even if just a little), No-Code is built for business and product managers that may not know any actual programming languages. It is meant to equip non-technical workers with the tools they need to create applications without formal development training.

No-Code applications need to be self-contained and everything the No-Code vendor thinks the user may need is already built into the tool.

As a result, No-Code applications create a lot of restrictions for the long-term in exchange for quick results in the short-term. This is a great example of a 'deliberate-prudent' scenario in the context of the Technical Debt Quadrant, but more on this later.

Advantages of No-Code Solutions

The appeal of both Low-Code and No-Code is pretty obvious. By removing code organizations can remove those that write it — developers — because they are expensive, in short supply, and fundamentally don’t produce things quickly.

The benefits of these two forms of applications in their best forms can be pretty substantial:
  • Resources: Human Capital is becoming increasingly scarce — and therefore expensive. This can stop a lot of ambitious projects dead in their tracks. Low-Code and No-Code tools minimize the amount of specialized technical skills needed to get an application of the ground, which means things can get done more quickly and at a lower cost.
  • Low Risk/High ROI: Security processes, data integrations, and cross-platform support are all built into Low-Code and No-Code tools, meaning less risk and more time to focus on your business goals.
  • Moving to Production: Similarly, for both types of tools a single click is all it takes to send or deploy a model or application you built to production.
Looking at these advantages, it is no wonder that both Low-Code and No-Code have been taking industries by storm recently. While being distinctly different in terms of users, they serve the same goal — that is to say, faster, safer and cheaper deployment. Given these similarities, both terms will be grouped together under the 'No-Code' term for the rest of this article unless otherwise specified.

List of No-Code Data Tools

So far, we have covered the applications of No-Code in a very general way, but for the rest of this article, I would like to focus on data modeling. No-Code tools are prevalent in software development, but have also, in particular, started to take hold in this space, and some applications even claim to be an alternative to SQL and other querying languages (crazy, right?!). My reasons for focusing on this are two-fold: 
Firstly, there is a lot of existing analysis around this problem for software development and very little for data modeling. Secondly, this is also the area in which I have the most expertise.
Now let's take a look at some of the vendors that provide No-Code solutions in this space. These in no way constitute a complete list and are, for the most part, not exclusively built for data modeling. 

1. No-Code Data Modeling in Power BI

Power BI was created by Microsoft and aims to provide interactive visualizations and business intelligence capabilities to all types of business users. Their simple interface is meant to allow end-users to create their own reports and dashboards through a number of features, including data mapping, transformation, and visualization through dashboards. Power BI does support some R coding capabilities for visualization, but when it comes to data modeling, it is a true No-Code tool.

2. Alteryx as a Low-Code Alternative

Alteryx is meant to make advanced analytics accessible to any data worker. To achieve this, it offers several data analytics solutions. Alteryx specializes in self-service analytics with an intuitive UI. Their offerings can be used as Extract, Transform, Load (ETL) Tools within their own framework. Alteryx allows data workers to organize their data pipelines through their custom features and SQL code blocks. As such, they are easily identified as a Low-Code solution.

3. Is Tableau a No-Code Data Modeling Solution?

Tableau is a visual analytics platform and a direct competitor to Power BI. They were recently acquired by Salesforce which is now hoping to 'transform the way we use data to solve problems—empowering people and organizations to make the most of their data.' It is also a pretty obvious No-Code platform that is supposed to appeal to all types of end-users. As of now, it offers fewer tools for data modeling than Power BI, but that is likely to change in the future.

4. Looker is a No-Code Alternative to SQL

Looker is a business intelligence software and big data analytics platform that promises to help you explore, analyze, and share real-time business analytics easily. Very much in line with Tableau and Power BI, it aims to make non-technical end-users proficient in a variety of data tasks such as transformation, modeling, and visualization.

You might be wondering why I am including so many BI/Visualization platforms when talking about potential alternatives to SQL. After all, these tools are only set up to address an organization's reporting needs, which constitute only one of the use cases for data queries and SQL. This is certainly a valid point, so allow me to clarify my reasoning a bit more.

While it is true that reporting is only one of many potential uses for SQL, it is nevertheless an extremely important one. There is a good reason why there are so many No-Code BI tools in the market—to address heightening demand from enterprises around the world — and therefore, it is worth taking a closer look at their almost inevitable shortcomings.

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Reimagining the SQL Curriculum: Change How SQL is Taught

After looking at SQL's impressive 50-year reign and what we could learn from it, let us now focus on how we can learn it.

Not only is SQL everywhere, but it is also in high demand. A quick search of job boards easily reveals not only that virtually all of the S&P 500 companies are hiring for SQL skills, but that the demand for them is even higher than more 'high-tech' languages like R and Python.