Your team is facing rising data storage costs. How can you maintain service quality without overspending?
As data storage costs climb, maintaining quality without breaking the bank becomes crucial. Consider these strategies:
- Evaluate current data usage and purge unnecessary files to reduce costs.
- Explore alternative storage solutions like cloud services for scalability and potential savings.
- Negotiate with existing providers for better rates or look for more competitive pricing elsewhere.
How have you managed to cut down on data storage expenses while preserving service quality?
Your team is facing rising data storage costs. How can you maintain service quality without overspending?
As data storage costs climb, maintaining quality without breaking the bank becomes crucial. Consider these strategies:
- Evaluate current data usage and purge unnecessary files to reduce costs.
- Explore alternative storage solutions like cloud services for scalability and potential savings.
- Negotiate with existing providers for better rates or look for more competitive pricing elsewhere.
How have you managed to cut down on data storage expenses while preserving service quality?
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Managing rising data storage costs while maintaining quality requires careful strategies. Striking the right balance between efficiency and cost ensures stakeholder satisfaction and smooth business operations ... Storage tiers: Classify data based on its usage. Data that is accessed frequently remains in high-speed storage, while infrequently used data is moved to lower-cost options. Ignoring this will result in unnecessary costs. Data lifecycle policies: Automate the deletion or archiving of obsolete data. Otherwise, storage space requirements will grow, driving up costs. Compression techniques: Compress large data sets to reduce storage requirements while maintaining quality. Poor compression can affect data integrity or usability.
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Throwing more storage at the problem isn’t a solution—it’s a delay. Rising costs caught up with us once because we kept storing raw, transformed, and duplicate data “just in case.” Spoiler: “Just in case” is expensive. Start with tiered storage. Keep hot, frequently accessed data on high-performance systems and push cold, archival data to cheaper tiers like object storage. Next, clean up. Identify duplicates, obsolete data, and anything that’s outlived its purpose—cull ruthlessly. Finally, compression and columnar formats like Parquet should be considered to store more in less space without sacrificing performance. Try to move your most problematic use cases to Delta Lake. Optimizing storage now keeps quality intact and wallets happier.
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According to my experience, to maintain service quality without overspending on data storage, I would: - Eliminate unnecessary or outdated data to reduce storage needs. - Apply data compression and deduplication to minimize storage requirements. - Implement tiered storage by storing frequently accessed data on high-performance systems and moving archival data to lower-cost storage. - Leverage cloud storage for flexible, scalable pricing based on actual usage. - Monitor storage usage trends to proactively manage costs and avoid over-provisioning. - Optimize backup strategies by using incremental backups instead of full backups to save on storage.
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- Delete redundant/outdated data and use tiered storage - Apply compression and deduplication - Streamline data collection to capture only necessary information - Use reserved/spot instances and auto-scaling in cloud storage - Implement data partitioning and automatic purging policies - Consider in-house storage for specific data
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En un mundo donde el exceso de datos es la realidad, lo primero hay que tener claro qué pregunta se quiere responder y definir el rango de tiempo. Hay que limpiar la data, eliminando duplicados o guardando en otras plataformas los datos que no aportan valor o solo generan ruido. Es clave también automatizar procesos para que en futuras ocasiones sólo tengamos en los servidores la info útil, y, si es necesario, implementar modelos de ml o algo ya existente como los vectores de eliminación. Según las herramientas disponibles, se puede considerar un sistema de almacenamiento híbrido que combine servidores locales y la nube. En última instancia hacer una partición de la estructura por criterios puede optimizarla.
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Well, let´s tackle rising storage costs without compromising performance: Smart Data Tiering: Move cold or infrequently used data to cheaper storage tiers. Shrink It: Use compression and efficient formats. Why pay for space you don’t need? Leverage Cloud Smarts: Go with scalable cloud solutions like BigQuery. Pay for what you use, not what you don’t. Stay on Top of It: Regularly monitor usage, cut out redundant data, and automate data management to avoid surprises. Mix It Up: Hybrid setups (cloud + on-prem) can strike the right balance between speed and cost. Big players like Netflix use these moves to scale smartly without burning through budgets. We can do it too.
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I have some ideas to decrease data storage costs includes: - Delete Unnecessary Data: Remove outdated, irrelevant, or duplicate data. - Distribute Data: Move infrequent data to low-cost storage. - Build Alerts: Monitor and alert when usage nears critical levels. - Automate Lifecycle: Archive or delete data based on rules. - Compress Data: Reduce storage footprint with compression. - Optimize Queries: Refactor queries to minimize data retrieval costs. - Implement Tiering: Use high-performance storage for active data and low-cost tiers for archives. - Monitor Trends: Analyze growth and adjust storage dynamically.
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In a short time, My project data has grown by nearly 40%, causing longer execution times for pipelines, stored procedures, and Power BI dashboards. To address this: 1. Optimize Processes: Focus on improving the pipeline, refining the data model, and using compression techniques to handle rapid data growth effectively. 2. Engage Stakeholders: Collaborate with stakeholders to adjust the data scope based on priorities and timeline constraints. 3. Adopt Cloud Solutions: If still on-premises, consider migrating to the cloud for cost-effective, scalable, and efficient data storage. 4. Request Budget Increases: If necessary, negotiate for a higher budget to support additional resources or infrastructure.
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In my experience, optimizing database queries and storage structures to minimize storage needs can help reduce costs. For example, data can be stored in a data lake instead of in a comparatively expensive data warehouse. It can be achieved by implementing the Lakehouse concept where the data resides in the data lake but can queried using a serverless SQL pool on the database which in turn queries an abstraction layer. This abstraction layer holds all the metadata of the actual data in the data lake. So by doing this, it gives you the functionality of querying a traditional database while the data still resides in the data lake.
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~Implement automated data lifecycle policies to manage retention and deletion. ~Adopt hybrid storage systems. ~Migrate infrequently accessed data. ~Use version control to avoid storing unnecessary duplicates of files. What other strategies do you think can be used to reduce storage costs without compromising performance?