More and more businesses today demand real-time analytics about their business. In response, some vendors are adding embedded analytics to current SaaS applications as a way to increase their value proposition. Others recognize the opportunity to create an additional revenue stream by using their vast stores of data to serve up standalone analytics. Naturally, SQL development tools and capabilities play a big role in data application development. However, developers should choose the tools in their toolbox wisely to ensure they can create and deploy the most effective data apps possible for their internal and external clients.
New data apps tend to be built almost entirely on public cloud infrastructure and use APIs to bring together core features. However, not all cloud-based solutions are created equally. Many software developers start their app journey by adopting generic low-cost tools that allow for quick development without upfront investment. For example, open-source tools such as PostgreSQL, Elasticsearch, and NoSQL databases are tempting because they are an easy starting point to get up and running.This shortcut allows a new analytics app to be sent to market without the upfront cost of procuring a database, which alleviates a potential friction point between development and finance teams. Sounds too good to be true? It is. Four common development challenges arise when you don’t consider thoroughly what’s needed from a data stack to deliver powerful data analytics apps:
1. Increasing data storage and compute strains on the system: this usually comes from more and larger customers. Fully open source solutions require manual and disruptive scaling that impacts the customer experience and requires intensive time and effort from engineering.
2. Lack of native support for semi-structured data: Using data types such as JSON, XML, and Avro is a huge challenge if the open-source solution doesn’t natively support semi-structured data. This forces data engineering teams to build and maintain complex data pipelines.
3. Maintenance: While development teams should spend their time writing and developing analytics applications, open-source solutions require overhead in the form of frequent upgrades and maintenance. As a result, developers end up dealing with system maintenance instead of coding.
4. Expertise: The use of open-source tools requires specific skills that may not exist within an organization. As a result, companies need to hire more resources, which can be challenging to find and expensive to acquire.
SQL Development with the data cloud
Given the inherent risks of "free" software and tools mentioned above, data engineers and application developers need to leverage the Data Cloud, which provides what is needed to build and run modern analytics applications and deliver value to end customers. With the dev stack inside the Data Cloud, all of the features needed to develop and scale modern data analytics apps are built into the architecture from the ground up, including unlimited scalability, concurrency, and instant elasticity.
Snowflake for SQL Development
Snowflake's platform is designed to power applications with no limitations on performance, concurrency, or scale, Leveraging the ease and simplicity of PaaS-style application development, Snowflake handles all the infrastructure complexity, so developers can focus on innovating.
Snowflake provides the following native SQL development and data querying interfaces:
Snowflake Worksheets:
Browser-based SQL editor integrated directly into the Snowflake web interface.
No installation or configuration required.
Supports multiple, independent working environments that can be opened/closed, named, and reused across multiple sessions (all work is automatically saved).
SnowSQL:
Python-based client for performing all tasks in Snowflake, including querying, executing DDL/DML commands, and bulk loading/unloading of data.
Download from the Snowflake web interface and install it using the provided installer.
In addition, Snowflake works with a variety of 3rd-party SQL tools for managing the modeling, development, and deployment of SQL code in your Snowflake applications, including, but not limited to:
Aginity
DataOps
DBeaver
Agile Data EngineSqlDBM
SQLWorkbench/J
Learn more here.