Apr 26, 2024 9:36:24 AM
Have you ever watched a triathlon? There are three segments – swim, bike, run. Take a guess — which part of the race tends to cause athletes to lose a lot of time? It’s actually not any particular leg of the race... rather, it’s the transitions between these segments. Miniscule stuff like putting on shoes frequently ruins whatever gains triathletes make during their grueling training. Therefore, serious athletes focus on this area as well.
In data management just like in sports, the quest for efficiency, agility, and innovation is relentless. Though most people are focused on agility during an implementation project or when it comes to operations, we want to set the focus on the often overlooked, yet critical, upstart and package selection phase. Experience tells us you can lose a lot of time even before the actual implementation starts. It’s the proverbial transition phase.
Make sure you choose the right database warehouse
Before moving a data warehouse to the cloud comes the critical first step: selecting the right database technology. There is a plethora of vendors out there today: Snowflake, Databricks, Big Query, Synapse, Redshift... the list goes on and on. While all of them are very robust - and each has its own distinct advantages and features - it is not always easy to pick the vendor that’s the right fit for your requirements.
Package selection projects don't have to be a time sink
Sound business practices lead organizations to embark on a database selection process. This usually entails whipping up different trial versions, implementing sample use cases in each database, and testing specific performance criteria. Automation tools need to be installed, integrated and configured for each database. Depending on the setup, this can take up to a few months. And it's in this crucial transition where you’ll end up losing a lot of valuable time without any added benefit.
Data transition phase: Design multiple times
Testing use cases on different platforms is challenging. Each database comes with its unique nuances and SQL dialect, requiring developers to effectively implement the same use cases multiple times. This leads to redundant work and a lot of lost time without much benefit. The higher the complexity of the use cases, the higher the effort. And let’s be honest – your team also needs to spend time on learning how to work with each technology and the automation tools.
Ask yourself whether this is time well spent. Duplicating work effort is hardly ever a great idea. What if you could dramatically minimize the duplication work? What if you could design your use cases once and deploy them on multiple database platforms in parallel?
Enabling true data warehouse agility
Agile Date Engine (ADE), built on low-code principles, covers the entire DataOps lifecycle. From intuitive data modeling to seamless workflow management, robust transformations to Continuous Integration/Continuous Deployment (CI/CD) pipelines. Instead of having to purchase, learn & integrate multiple tools, we've created a single data warehouse automation platform that empowers data engineers to unleash their full potential. Best of all, Agile Data Engine works with all typical cloud databases and was built as a SaaS product to support a quick setup.
Develop Once, Deploy Multiple Times
ADE provides a hidden but extremely powerful feature. We built our platform with the inherent ability to focus on the logical design of your cloud data warehouse without having to put too much focus on the individual database platform. With our design once, deploy anywhere capability, you can craft your data solution (e.g. entities, Loads, Transformations & Workflows) once and effortlessly deploy it to various databases in parallel. ADE will automatically create the relevant SQL & Python code for each database in the background, so you won’t have to worry about it. Deployments can be delivered to multiple environments in parallel. And ADE will keep everything in sync for you.
Efficient Evaluation, Maximum Impact
Imagine the possibilities: Instead of investing time in redeveloping use cases for different databases multiple times, you can focus on testing and inspecting the unique database-specific features. Performance comparisons become meaningful as you’re guaranteed to compare the proverbial apples to apples. This not only expedites the evaluation process, but also lowers cost & effort – typically the most important goal. This becomes very powerful when you iterate through design changes. Moving swiftly through the database selection phase of your cloud data warehouse journey will allow you to create momentum and build trust with the business. Nothing hurts a relationship more than being left sitting and waiting.
How Agile Data Engine works
- Setup
- Setup the ADE service once and connect your desired databases to the platform
- Design
- Craft your data solution (e.g. model design, loads, transformations, workflows) using our intuitive low-code environment, ensuring flexibility and ease of development.
- Deploy
- Effortlessly deploy your solution to Snowflake, BigQuery, Databricks, and more, with just a few clicks.
- Test
- Test and evaluate the different database platforms based on your performance criteria. Collect user feedback and leverage the inherent agility.
Don’t delay your DataOps deployment
At Agile Data Engine, we believe in allowing data engineers to focus on the important things by reducing the tedious overhead such as manual coding, code reviews, and other repetitive, time-consuming tasks. Our design once, deploy anywhere capability is a testament to that commitment, offering you efficiency, agility, and a streamlined approach right from the very start of your cloud data warehouse journey. Why waste valuable time and resources to evaluate database platforms when you could be investing that time into future solution design instead? Becoming the proverbial triathlete who aces the transition periods frees up the resources to future-proof and prepare for any unforeseen crises.
Future-proof your Data Warehouse
The design once, deploy multiple times approach also future-proofs your data warehouse. What if you were to switch your database platform at some point in time? Better pricing or an IT strategy shift typically triggers moves like this, and when it happens you won’t want to redevelop your entire data warehouse. What if you didn’t have to? With Agile Data Engine, you can just lift and shift the majority of your design in an efficient manner. Leveraging the same ‘develop once, deploy multiple time’ approach, you can efficiently move your IP into a new technology with ease, and avoid vendor lock-in while you’re at it. That’s how a champions would come out on top.
Intrigued? Feel free to reach out! Our team of experts is ready to help you!
