In the age of data-driven decision-making, companies grapple with the mammoth task of setting up a robust Modern Data Stack. The on premise legacy systems struggle to keep up, and standing up a Modern Data Stack (MDS) isn't just a tech upgrade; it's an essential pivot, ensuring businesses extract actionable insights from the raw data they encounter. However, the road to achieving this is complex and slower than the line at the DMV.
If the responsibility of establishing a Modern Data Stack falls on your shoulders and you're feeling the weight of its time/resource/knowledge-intensive nature, this post offers insights and solutions.We explore the hurdles businesses encounter while shaping their data infrastructure and how you can streamline and expedite the process.
What is a Modern Data Stack?
A Modern Data Stack refers to a suite of tools and digital technologies specifically designed for data management. Within this stack, some tools specialize in collecting data, while others focus on storing or processing it. As data moves through this system, it's transformed from raw input into actionable insights.
Many of these tools come from various providers and must be seamlessly integrated to ensure optimal performance. Leveraging the latest technologies, the modern data stack efficiently manages the entire data lifecycle, from collection to analysis. This stack is both scalable and flexible, ensuring it can adapt and grow with the ever-evolving demands of a business, and provide consistent performance regardless of data volume or complexity.
Below you can see an example Modern Data Architecture Diagram.
Standing up a Modern Data Stack Takes Time
The path to a comprehensive end-to-end enterprise data platform is not without challenges. Embarking on such a journey requires diligent research, because the process of migrating to a Modern Data Stack or establishing it from the ground up is intricate and piecemeal. Since there are many individual tools in the Modern Data Stack, you may have to tackle each tool individually so you can focus on setting it up correctly. Given the complexity of the endeavor, even with a skilled team on board, it can take between 6 to 9 months to build a complete end-to-end data solution. This may be frustrating, but understanding the pain points in setting up a Modern Data Stack can help to make educated decisions that accelerate the process.
High Level Pain Points:
Hundreds of tools to choose from - While having many options can be beneficial, it can also be overwhelming. The vast selection can lead to what's known as "tool overload," making it hard to pick the best fit.
Difficult to Integrate various tools - Once you've picked your tools, the next step is integration. But with so many different systems and platforms available, getting them to work together can be like solving a complex puzzle.
Architecting a secure platform - Everyone agrees that data security is critical. Creating a platform that's not just secure but also easy to replicate and audit is challenging and requires careful planning.
Implementing best practices - The lack of standard processes in the data world can lead to inconsistencies. Finding and applying the best practices isn't just about knowledge; it's also about experience.
Hidden Costs - Even though a lot of modern data stack tools are freely available, the hidden cost emerges in the form of time - spent on learning, configuring, testing, and refining. It’s like getting a "free puppy"; while there might be zero upfront expenses, the continuous care, attention, and commitment required are far from zero.
Modern Data Stack: Guiding Principles for Success
A strong data platform is the backbone of good decision-making. It helps us see clear insights fast and strengthens our data teams. When creating or choosing such a system, keep these principles in mind:
Trustworthiness - There needs to be trust that the data is always right and true.
Usability - The system should be easy to use and understand.
Collaboration - Teams should be able to work together in a secure way.
Reusability - If one part of the system is good, we should be able to use it in other places too.
Maintainability - There should be automated process and DataOps in place.
Reliability - It should always work well, detect errors, not break down often, and keep data safe.
Following these rules can help us get the most from our data and make the best decisions
Simplify the Modern Data Stack
Understanding the challenges and intricacies of setting up a Modern Data Stack makes it clear why we need efficient solutions. In the data world things move fast and speed is imperative. While there are numerous tools available that cater to specific components, Datacoves offers a more comprehensive approach, addressing the end-to-end data stack. Datacoves could reduce the setup of your Enterprise Data Platform from the usual 6-9 months to just 2-3 weeks. But how does it achieve this feat?
A Turnkey Solution - Datacoves doesn't just offer a solution; it provides an all-encompassing package designed meticulously to streamline the entire data-to-insight trajectory. This isn't about starting from scratch; it's about leveraging a fully-equipped platform to jumpstart your journey.
Guidelines and Expertise - No more searching in the dark. Datacoves ensures its users have a clear path ahead. The challenge of standardizing processes, which once seemed like climbing Everest, is simplified, thanks to the expert guidance provided.
Scalability At Its Finest - Whether you’re a budding team of 3 just starting out or a robust squad of 300, Datacoves has been engineered to scale with your needs, ensuring consistency and efficiency at every stage.
State-of-the-Art Tools - With tools like a finely-tuned VS Code in the browser with dbt Core, Datacoves ensures users aren't just walking but sprinting from the get-go. It's about giving you the best gear to make your climb smoother.
Best Practices at Your Fingertips - Datacoves realizes that in the fast-paced world of data, time is of the essence. That's why, through integrated accelerators, it ensures that adhering to industry best practices isn’t a drawn-out quest but just a matter of configuring to your needs.
Highlighting Datacoves' Features
Datacoves is not just another platform; it's a game-changer. Its project-based structure integrates seamlessly with any git repository, and it can be swiftly deployed in a private cloud to connect with existing tools. Each project provides multiple environments, facilitating role-based access and ensuring user-specific needs are met.
Here are just a few ways that Datacoves empowers the Data and Analytics Engineer to deliver quickly:
Everything in one place - The objective is to streamline the Data/Analytics Engineer's workflow. By consolidating essential tools and functionalities into a single interface, users can load data, review entries in their data warehouse, configure DAGs, write code in VSCode, and more, all without switching tabs.
Airflow YML Configuration - With this feature, users can bypass the complexities of Python when working with Airflow. Instead, the YML configuration allows for a more direct way to set up your DAGs.
dbt-coves Extension - Within your vscode workspace, the dbt-coves extension is integrated, making tasks more efficient. Specifically, the "dbt-coves generate sources" command examines your database, updates files, and integrates them into your yml with ease.
ChatGPT Integration - Embedded directly in your vscode workspace, ChatGPT offers a hassle-free way to seek answers without changing tabs. This feature is especially handy for tasks like creating model descriptions—simply generate, adjust as needed, and move forward.
Datacoves aims to simplify, reduce friction, enhance collaboration, and inject software engineering practices into data operations. It seeks to empower teams, enabling swift productivity and ensuring teams function cohesively.
Intrigued by Datacoves? Dive deeper by watching the full video below or book a demo to experience its magic first-hand.
Looking for an enterprise data platform?
Datacoves offers managed dbt core and Airflow and can be deployed in your private cloud.