Building the modern data stack: Keys for success
TL;DR: Building a modern data stack involves integrating efficient data pipelines, scalable storage, precise transformation tools, and insightful BI solutions to maximize data utility. With the right setup, businesses can harness data to fuel innovation and drive strategic growth.
One thing is certain – data drives decisions. It fuels innovation and acts as the bedrock for understanding trends, driving growth, and optimizing performance. As a data-oriented company, we have the unique privilege of engaging with data leaders and experts across diverse industries. These conversations provide us with an unmatched perspective on the most efficient and scalable data stack setups that power modern businesses.
When it comes to building a robust data stack, there are four essential components that you need to consider: data pipelines (ELT/ETL solutions), data storage (including both databases and data warehouses), transformation managers, and business intelligence (BI) solutions.
Data Pipelines: Data is the lifeblood of any business, and it needs to be moved efficiently from its sources to the places where it can be analyzed and utilized. For this, we have data pipeline solutions like Fivetran and Airflow leading the pack. They ensure seamless data integration and automate the extraction, loading, and transformation processes, allowing businesses to focus more on analysis and less on engineering.
Data Storage: Once your data is ready for processing, it needs a reliable and scalable place to be stored. PostgreSQL, BigQuery, and Snowflake are the key players in this segment. Each offers unique strengths: PostgreSQL for its robustness and flexibility, BigQuery for its seamless integration with Google Cloud, and Snowflake for its scalable, cloud-built data warehouse solution. Typically we see a combination of solutions used, where lower performant but cheaper storage is used for normalized data while low latency, but more expensive solutions are used for wide tables powering the analytics layer.
Data Transformation: The quality of your data analysis is only as good as the data you feed into it. The data transformation tool dbt plays a crucial role here, helping to transform raw data into a more digestible and analysis-ready format. dbt does this while maintaining accuracy, consistency, and ensuring your data models are as transparent as possible.
Business Intelligence (BI): To unlock the full potential of your data, you need a BI tool that makes the data understandable and actionable. Looker stands out for its modern data platform that offers powerful exploration and visualization capabilities, enabling data-driven decision-making across organizations.
It's important to note that these are not the only options available. The best choice of solutions will depend on your specific needs, data complexity, and scale. But these are the most commonly adopted solutions we encounter, due to their proven capabilities and industry-wide acceptance. If you would like insights into any of these or other solutions, we would be happy to offer our perspective and guide you in your decision.
With the evolving data landscape, companies are also looking to supplement their BI solutions with ad hoc request handlers, like Fabi.ai. These tools provide more flexible, interactive data analysis capabilities and allow businesses to respond quickly to emerging trends and opportunities.
Building a modern data stack is an investment in your company's future. With the right tools and strategies, you can leverage data to drive meaningful business outcomes. Remember, your goal is not just to manage data but to make it work for your business.
