Large datasets come with big responsibilities. Learn how to store, clean, and analyze massive data efficiently and reliably with expert practices.
Whether you're working with millions of rows of user data or streaming events from IoT devices, large datasets require deliberate strategies to manage effectively.
In this blog, we cover best practices that help ensure your data workflows remain fast, cost-efficient, and scalable.
Columnar formats like Parquet or ORC are ideal for analytics—they compress well and support fast filtering.
Cloud storage is preferred for scalability, redundancy, and integrated compute options—but costs must be managed.
Use aggregated views, sampling, and BI tools that support live queries against warehouses or cubes.
Archive data when it’s infrequently accessed but still needs retention for compliance or historical analysis.
Managing large datasets doesn’t have to be overwhelming. With smart storage strategies, modern tools, and scalable pipelines, your organization can maintain high performance—even as your data grows.
Use these best practices as your blueprint for building data systems that scale with your needs and deliver consistent value.
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